**Reading Group**

20 January 2006

A. Corduneanu and C.M. Bishop, Variational Bayesian Model Selection for Mixture Distributions

Presenter: Shihao Ji, Presentation

27 January 2006

M. Kuss and C. Rassmussen, Assessing Approximations for Gaussian Process Classification

Presenter: David Williams, Presentation

3 February 2006

`A. Niculescu-Mizil and R. Caruana, Learning the Structure of Related Tasks`

`Presenter: Lihan He, Presentation`

17 February 2006

Y.W. The, M.I. Jordan, M.J. Beal and D.M. Blei, Sharing Clusters Among Related Groups: Hierarchical Dirichlet Process

Presenter: Kai Ni, Presentation

24 February 2006

E. Bart and S. Ullman, Cross-Generalization: Learning Novel Classes From A Single Example By Feature Replacement

Presenter: Nilanjan Dasgupta, Presentation

3 March 2006

H. Ishwaran and L.F. James, Gibbs Sampling Methods for Stick-Breaking Priors

Presenter: Yuting Qi, Presentation

10 March 2006

T.L. Griffiths and Z. Ghahramani, Infinite Latent Feature Models and the Indian Buffet Process

Presenter: Ya Xue, Presentation

17 March 2006

E. Meeds and S. Osindero, Bayes Sets

Presenter: Qi An, Presentation

23 March 2006

Y. Wang and Q. Ji, A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences

Presenter: Qiuhua Liu, Presentation

31 March 2006

N. Mehta, S. Natarajan, P. Tadepalli and A. Fern, Transfer in Variable-reward Hierarchical Reinforcement Learning

Presenter: Hui Li, Presentation

7 April 2006

J. Zhang, Z. Ghahramani, Y. Yang, Learning Multiple
Related Tasks using Latent Independent Component Analysis

Presenter: Iulian Pruteanu, Presentation

14 April 2006

O.L. Mangasarian and E.W. Wild, Multisurface
Proximal SVM Classification via Generalized Eigenvalues

Presenter: Jun Fang, Presentation

21 April 2006

A. Eliazar and R. Parr, DP-SLAM: Fast, Robust Simultaneous Localization and Mapping without Predetermined Landmarks

A. Eliazar and R. Parr, DP-SLAM 2.0

A. Eliazar and R. Parr, Constant/Linear Time Simultaneous Localization and Mapping for Dense Maps

Presenter: Lihan He, Presentation

28 April 2006

D.B. Dunson and N. Pillai, Bayesian Density Regression

Presenter: Ya Xue, Presentation

5 May 2006

R.R. Coifman et al., Geometric Diffusions as a Tool for Harmonic Analysis and Structure Definition of Data: Diffusion Maps

Presenter: Xuejun Liao, Presentation

12 May 2006

R.R. Coifman et al., Geometric Diffusions as a Tool for Harmonic Analysis and Structure Definition of Data: Multiscale Methods

Presenter: Shihao Ji, Presentation

19 May 2006

E. Sudderth, A. Torralba, W. Freeman and A. Willsky, Describing Visual Scenes Using Transformed Dirichlet Processes

Presenter: Kai Ni, Presentation

26 May 2006

S. Mahadevan and M. Maggioni, Value Function Approximation with Diffusion Wavelets and Laplacian Eigenfunctions

Presenter: Qi An, Presentation

2 June 2006

B. Nadler, S. Lafon, R. Coifman,
I. Kevrekidis, *Diffusion Maps, Spectral Clustering and Eigenfunctions of Fokker-Planck Operators*

*Presenter: *Nilanjan Dasgupta, Presentation

19 June 2006

M. Szummer and T. Jaakkola, Partially Labeled Classification with Markov Random Walks

Presenter: Xuejun Liao, Presentation

7 July 2006

B. Wolfe, M.R. James and S. Singh, Learning Predictive State Representations

Presenter: Hui Li, Presentation

14 July 2006

D. Justice and A. Hero, A Binary Linear Programming Formulation of the Graph Edit Distance

Presenter: Shihao Ji, Presentation

21 July 2006

X.L. Nguyen, M.J. Wainwright, M.I. Jordan, On Optimal
Quantization Rules for Sequential Decision Problems

Presenter: Qi An, Presentation

28 July 2006

L. Fei-Fei and P. Perona, A Bayesian Hierarchical Model for Learning Natural Scene Categories

D. Blei and J.D. Lafferty, Dynamic Topic Models

Presenter: Iulian Pruteanu, Presentation

4 August 2006

D. Blei, J. Lafferty, Correlated Topic Models

Presenter: Chunping Wang, Presentation

25 August 2006

E.P. Xing, K.-A. Sohn, M.I. Jordan and Y.-W. Teh, Bayesian
Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process
Mixture

Presenter: Kai Ni, Presentation

4 September 2006

D. Hähnel, D. Fox, W. Burgard, and S. Thrun, A highly efficient
FastSLAM algorithm for generating cyclic maps of large-scale environments from
raw laser range measurements

G. Grisetti, C. Stachniss, and W. Burgard, Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling

Presenter: Lihan He, Presentation

11 September 2006

J. Langford and B. Zadrozny, Reducing T-step Reinforcement Learning to Classification

D. Blatt and A. O. Hero, From weighted classification to policy search

Presenter: Hui Li, Presentation

22 September 2006

A. Hyvarinen and P. Hoyer, Emergence of Phase- and Shift-Invariant Features by decomposition of Natural Images into Independent feature Subspaces

A. Hyvarinen, P.O. Hoyer and M. Inki, Topographic Independent Component Analysis

Presenter: Iulian Pruteanu, Presentation

29 September 2006

S. Lafton and A.B. Lee, Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameterization

Presenter: Shihao Ji, Presentation

6 October 2006

O. Papaspiliopoulos and G.O. Roberts, Retrospective MCMC Methods for DP Hierarchical Models

Presenter: Yuting Qi, Presentation

13 October 2006

O. Papaspiliopoulos and G.O. Roberts, Retrospective MCMC Methods for DP Hierarchical Models and comparison of MCMC samplers for Dirichlet processors

Presenter: Yuting Qi, Presentation

20 October 2006

Graphical techniques for semi-supervised learning

Presenter: Xuejun Liao, Presentation

27 October 2006

F. Wood, T.L. Griffiths and Z. Ghahramani, A Non-Parametric Bayesian Method of Inferring Hidden Causes

Presenter: Qi An, Presentation

3 November 2006

S. Lafton, Y. Keller and R.R. Coiffman, Data Fusion and Multi-Cue Data Matching by Diffusion Maps

Presenter: Chunping Wang, Presentation

10 November 2006

A. Rodriguez, D.B. Dunson and A.E. Gelfand, The Nested Dirichlet Process

Presenter: Kai Ni, Presentation

17 November 2006

A.E. Raftery and S. Lewis, How Many Iterations in the Gibbs Sampler

Presenter: Iulian Pruteanu, Presentation

5 December 2006

M.K. Cowles and B.P. Carlin, MCMC Convergence Diagnostics: A Comparative Review

Presenter: Yuting Qi, Presentation

12 January 2007

T. Smith and R. Simmons, Heuristic Search Value Iteration for POMDPs

Presenter: Hui Li, Presentation

19 January 2007

D.B. Dunson and J.-H. Park, Kernel Stick-Breaking Processes

Presenter: Qi An, Presentation

26 January 2007

D.B. Dunson, Bayesian Dynamic Modeling of Latent Trait Distributions

Presenter: Kai Ni, Presentation

2 February 2007

N. Tatti, Distances Between Data Sets Based on Summary Statistics

Presenter: Yuting Qi, Presentation

9 February 2007

E.J. Candes, J. Romberg, T. Tao, Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information

E. Candes and J. Romberg, Practical Signal Recovery from Random Projections

Presenter: Dehong Liu, Presentation

16 February 2007

S. Kim and P. Smyth, Segmental Hidden Markov Models with Random Effects for Waveform Modeling

Presenter: Lu Ren, Presentation

23 February 2007

E.P. Xing and K.-A. Sohn, A New Nonparametric Bayesian Model for Genetic Recombination in Open Ancestral Space

Presenter: Chunping Wang, Presentation

9 March 2007

K. Watanabe and Sumio Watanabe, Stochastic Complexities of Gaussian Mixtures in Variational Bayesian Approximation

Presenter: Iulian Pruteanu, Presentation

16 March 2007

T. De Bie and N. Cristianini, Fast SDP Relaxations of Graph Cut Clustering, Transduction, and Other Combinatorial Problems

Presenter: Lihan He, Presentation

23 March 2007

D. Zhou, O. Bousquet, T. Navin Lal, J. Weston and B. Scholkopf, Learning with Local and Global Consistency

Presenter: Qiuhua Liu, Presentation

30 March 2007

P. K. Shivaswamy, C. Bhattacharyya and A.J. Smola, Second Order Cone Programming Approaches for Handling Missing and Uncertain Data

Presenter: Qi An, Presentation

6 April 2007

H. Raghavan, O. Madani and R. Jones, Active Learning with Feedback on Both Features and Instances

Presenter: John Paisley, Presentation

27 April 2007

F. Dominici, G. Parmigiani, K.H. Reckhow and R.L. Wolpert, Combining Information from Related Regressions

Presenter: Kai Ni, Presentation

4 May 2007

F.R. Bach and M.I. Jordan, Learning Spectral Clustering, With Application to Speech Processing

Presenter: Yuting Qi, Presentation

11 May 2007

E. Evan-Dar, S. Mannor and Y. Mansour, Action Elimination and Stopping Conditions for the Multi-Armed Bandit and Reinforcement Learning Problems

Presenter: Shihao Ji, Presentation

18 May 2007

F.R. Bach, D. Heckerman, E. Horvitz, Considering Cost Asymmetry in Learning Classifiers

Presenter: Chunping Wang, Presentation

30 May 2007

R. Munos, Policy Gradient in Continuous Time

Presenter: Hui Li, Presentation

8 June 2007

D. Tao1, X. Li, X. Wu and S.J. Maybank, General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

Presenter: Iulian Pruteanu, Presentation

15 June 2007

R. Thibaux and M.I. Jordan, Hierarchical Beta
Processes and the Indian Buffet Process

Presenter: Qi An, Presentation

9 July 2007

T. Liu, A.W. Moore and A. Gray, New Algorithms for Efficient High-Dimensional Nonparametric Classification

Presenter: Yuting Qi, Presentation

18 July 2007

S. Whiteson and P. Stone, Evolutionary Function
Approximation for Reinforcement Learning

Presenter: Xuejun Liao, Presentation

27 July 2007

Y.W. Teh, D. Gorur, Z. Ghahramani, Stick-breaking construction for the Indian buffet process

Presenter: Kai Ni, Presentation

6 August 2007

A.D. Szlam, M. Maggioni and R.R. Coifman, Regularization on Graphs with Function-Adapted Diffusion Process

Presenter: Eric Wang, Presentation

15 August 2007

H. Ishwaran and M. Zarepour, Dirichlet Prior Sieves in Finite Normal Mixtures

Presenter: John Paisley, Presentation

31 August 2007

K. Kurihara, M. Welling and Y.W. Teh, Collapsed Variational Dirichlet Process Mixture Models

Presenter: Qi An, Presentation

7 September 2007

J. Hoey and J.J. Little, A Collapsed Variational
Bayesian Inference Algorithm for Latent Dirichlet Allocation

Presenter: Iulian Pruteanu, Presentation

14 September 2007

S. Yu, V. Tresp and K. Yu, Robust Multi-Task Learning with t-Processes

Presenter: Ivo Shterev, Presentation

21 September 2007

P. Quelhas, F. Monay, J.M. Odobez, D. Gatica-Perez and T. Tuytelaars, A Thousand Words in a Scene

Presenter: Yuting Qi, Presentation

5 October 2007

P. Domingos and M. Pazzani, On the Optimality of the Simple Bayesian Classifier under Zero-One Loss

Presenter: Lu Ren, Presentation

18 October 2007

Q. Wu, J. Guinney, M. Maggioni and S. Mukherjee, Learning Gradients: Predictive Models that Infer Geometry and Dependence

Presenter: Xuejun Liao, Presentation

26 October 2007

R.M. Neal, Density Modeling and Clustering Using Dirichlet Diffusion Trees

Presenter: Ivo Shterev, Presentation

2 November 2007

J. Guinney, Q. Wu and S. Mukherjee, Estimating Variable Structure and Dependence in Multi-Task Learning via Gradients

Presenter: John Paisley, Presentation

16 November 2007

M. Belkin and P. Niyogi, Using Manifold Structure for Partially Labelled Classification

Presenter: Chunping Wang, Presentation

30 November 2007

R. Castelo and A. Roverato, A Robust Procedure For
Gaussian Graphical Model Search From Microarray DataWith *p *Larger Than *n*

Presenter: Kuan-Ming Lin, Presentation

7 December 2007

Y.W. The, K. Kurihara and M. Welling, Collapsed Variational
Inference for HDP

Presenter: Iulian Pruteanu, Presentation

18 January 2007

H. Ishwaran and M. Zarepour, Exact and Approximate Sum Representations for the Dirichlet Process

Presenter: John Paisley, Presentation

25 January 2007

A.B. Owen, Infinitely
Imbalanced Logistic Regression

Presenter: Ivo Shterev, Presentation

15 February 2008

J.-H. Park and D. Dunson, Bayesian Generalized
Product Partition Model

Presenter: Eric Wang, Presentation

22 February 2008

R.F. MacLehose and D.B. Dunson, Bayesian semi-parametric
multiple shrinkage

Presenter: Lu Ren, Presentation

3 March 2008

W. Li, D. Blei and A. McCallum, Nonparametric Bayes
Pachinko Allocation

Presenter: Lihan He, Presentation

12 March 2008

N. Bouguila and D. Ziou, High-Dimensional Unsupervised
Selection & Estimation of a Finite Generalized Dirichlet Mixture Model
Based on MML

Presenter: Qi An, Presentation

21 March 2008

S. Ray and B. Mallik, Functional
Clustering by Bayesian Wavelet Methods

Presenter: Iulian Pruteanu, Presentation

4 April 2008

M. West, Bayesian
Factor Regression Models in the “Large p, Small n” Paradigm

Presenter: John Paisley, Presentation

11 April 2008

M. Dundar, B. Krishnapuram, J. Bi and R. Rao, Learning Classifiers
When the Training Data Are Not IID

Presenter: Ivo Shterev, Presentation

18 April 2008

Y. Sun, M. Kamel and Y. Wang, Boosting
for Learning Multiple Classes with Imbalanced Class Distribution

Presenter: Minhua Chen, Presentation

25 April 2008

H.M. Wallach, Topic
Modeling: Beyond Bag of Words

Presenter: Eric Wang, Presentation

2 May 2008

J. Baxter, A
Model of Inductive Bias Learning

S. Ben David and R.S. Borbely, A Notion of Task
Relatedness Yielding Provable Multiple-task Learning Guarantees

Presenter: Xuejun Liao, Presentation

9 May 2008

J. Canny and T. Rattenbury, A Dynamic Topic Model
for Document Segmentation

Presenter: Lan Du, Presentation

16 May 2008

S.M. O’Brien and D. Dunson, Bayesian
Multivariate Logistic Regression

Presenter: Lihan He, Presentation

23 May 2008

M.Y. Park and T. Hastie, Penalized
Logistic Regression for Detecting Gene Interactions

Presenter: Minhua Chen, Presentation

30 May 2008

J. Amores, N. Sebe and P. Radeva, Context-Based
Object-Class Recognition and Retrieval by Generalized Correlograms

Presenter: Qi An, Presentation

6 June 2008

D.M Blei and J.D. McAuliffe, Supervised Topic
Models

Presenter: Iulian Pruteanu, Presentation

13 June 2008

S. Gould, J. Rodgers, D. Cohen, G. Elidan, D. Koller, Multi-Class Segmentation with Relative Location Prior

Presenter: Lan Du, Presentation

20 June 2008

C. Wang, D. Blei and D. Heckerman, Continuous Time Dynamic Topic Models

Presenter: Ivo Shterev, Presentation

27 June 2008

P. Liang and M.I. Jordan, An Asymptotic Analysis of Generative, Discriminative and Pseudolikelihood Estimators

Presenter: Lihan He, Presentation

3 July 2008

G. Chechik, G. Heitz, G. Elidan,
P. Abbeel, D. Koller, Max-margin
Classification of Data with Absent Features

Presenter: Chunping Wang, Presentation

11 July 2008

E.B. Fox, E.B. Sudderth, M.I. Jordan and A.S. Willsky, An HDP-HMM for Systems with State Persistence

Presenter: Lu Ren, Presentation

18 July 2008

M.B. Wakin and R.G. Baraniuk, Random projections of
signal manifolds

C. Hegde, M.B. Wakin and R.G. Baraniuk, Random projections for
manifold learning

R.G. Baraniuk and M.B. Wakin, Random projections of
smooth manifolds

Presenter: John Paisley, Presentation

25 July 2008

S.J.D. Prince and J.H. Elder, Tied factor
analysis for face recognition across large pose changes

Presenter: Lan Du, Presentation

1 August 2008

G. Elidan, B. Packer, G. Heitz and D. Koller, Convex Point Estimation using Undirected Bayesian Transfer Hierarchies

Presenter: Haojun Chen, Presentation

8 August 2008

P. Liang, D. Klein and M.I. Jordan, Agreement-Based Learning

Presenter: Xuejun Liao, Presentation

22 August 2008

M. Girolami and S. Rogers, Variational Bayesian multinomial probit regression with Gaussian priors

Presenter: Minhua Chen, Presentation

29 August 2008

X.Z. Fern and C.E. Brodley, Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach

Presenter: Dehong Liu, Presentation

5 September 2008

K.T. Miller, T.L. Griffiths and M.I. Jordan, The
Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for
Latent Features

Presenter: John Paisley, Presentation

12 September 2008

T.L. Griffiths, M. Steyvers, D.M.
Blei and J.B. Tenenbaum, Integrating Topics and
Syntax

Preseneter: Eric Wang, Presentation

19 September 2008

L. Meier, S. van de Geer and P. Buhlmann, The Group Lasso for Logistic Regression

Presenter: Lu Ren, Presentation

3 October 2008

P. Poupart and N. Vlassis, Model-based Bayesian Reinforcement Learning in Partially Observable Domains

Presenter: Lihan He, Presentation

17 October 2008

P. Indyk and R. Motwani, Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality

Presenter: Dehong Liu, Presentation

24 October 2008

J.A. Duan, M. Guindani and A.E. Gelfand, Generalized Spatial
Dirichlet Process Models

Presenter: Lu Ren, Presentation

31 October 2008

R. Tibshirani, Regression
Shrinkage and Selection via the Lasso

A.E. Hoerl and R.W. Kennard, Ridge Regression: Biased
Estimation for Nonorthogonal Problems

Presenter: John Paisley, Presentation

7 November 2008

H. Zou and T. Hastie, Regularization and
Variable Selection via the Elastic Net

Presenter: Minhua Chen, Presentation

14 November 2008

J. van Gael, Y. Saatci, Y.W. Teh and Z. Ghahramani, Beam Sampling for
the Infinite HMM

Presenter: Lihan He, Presentation

28 November 2009

Y. Gal and A. Pfeffer, Networks of Influence Diagrams: A Formalism for Representing Agents’ Beliefs and Decision Making

Presenter: Chenghui Cai, Presentation

17 December 2008

S. Shringarpure and E.P. Xing, mStruct: A New
Admixture Model for Inference of Population Structure in Light of Both Genetic
Admixing and Allele Mutations

Presenter: Haojun Chen, Presentation

5 January 2009

R. Gomes, M. Welling and P. Perona, Memory Bounded Inference in Topic Models

Presenter: Eric Wang, Presentation

14 January 2009

P. Orbanz and J.M. Buhmann, Smooth Image Segmentation
by Nonparametric Bayesian Inference

Presenter: Lan Du, Presentation

30 January 2008

D. Achlioptas, Database Friendly Random Projections

Presenter: Xuejun Liao, Presentation

6 February 2009

M.A.T. Figueiredo, D.S. Cheng and V. Murino, Clustering Under Prior Knowledge with Application to Image Segmentation

Presenter: Lu Ren, Presentation

23 February 2009

G. Sfikas, C. Nikou and N. Galatsanos, Edge Preserving Spatially Varying Mixtures for Image Segmentation

Presenter: Lihan He, Presentation

2 March 2009

A. Gruber, M. Rosen-Zvi and Y. Weiss, Hidden Topic
Markov Models

Presenter: Chunping Wang, Presentation

13 March 2009

Y. Qi and T.S. Jaakkola, Parameter
Expanded Variational Bayesian Methods

Presenter: John Paisley, Presentation

20 March 2009

H.D. Bondell and B.J. Reich, Simultaneous Regression
Shrinkage, Variable Selection, and Supervised Clustering of Predictors with
OSCAR

Presenter: Minhua Chen, Presentation

27 March 2009

P. Rai and H. Daume III, The Infinite
Hierarchical Factor Regression Model

Presenter: Bo Chen, Presentation

3 April 2009

J.B. Tenenbaum, V. de Silva and J.C. Langford, A Global Geometric Framework
for Nonlinear Dimensionality Reduction

Presenter: Mingyuan Zhou, Presentation

10 April 2009

A. Krause and C. Guestrin, Near-Optimal Observation
Selection Using Submodular Functions

Presenter: Haojun Chen, Presentation

17 April 2009

J. Haupt, R. Castro and R. Nowak, Distilled Sensing:
Selective Sampling for Sparse Signal Recovery

Presenter: Lihan He, Presentation

24 April 2009

X. Wang, X. Ma and E. Grimson, Unsupervised
Activity Perception by Hierarchical Bayesian Models

Presenter: Xuejun Liao, Presentation

8 May 2009

L.-J. Li, R. Socher and L. Fei-Fei, Towards
Total Scene Understanding: Automatic Classification, Annotation and
Segmentation

Presenter: Eric Wang, Presentation

22 May 2009

X. Nguyen and A.E. Gelfand, The Dirichlet
Labeling Process for Functional Data Analysis

Presenter: Lu Ren, Presentation

1 June 2009

T. Shi, M. Belkin, B. Yu, Data Spectroscopy:
Learning Mixture Models using Eigenspaces of Convolution Operators

Presenter: Jorge
Silva, Presentation

12 June 2009

A.M. Bruckstein, D.L. Donoho and M. Elad, From Sparse
Solutions of Systems of Equations to Sparse Modeling of Signals & Images

Presenter: Mingyuan Zhou , Presentation

19 June 2009

J.M. Duarte-Carvajalino and G. Sapiro, Learning to Sense Sparse
Signals

Presenter: Haojun Chen , Presentation

26 June 2009

J. Sivic and A. Zisserman, Efficient Visual
Search of Videos Case as Text Retrieval

Presenter: John Paisley, Presentation

3 July 2009

C. Wang, D. Blei and L. Fei-Fei, Simultaneous
Image Classification and Annotation

Presenter: Eric Wang, Presentation

10 July 2009

Presenter: Chunping Wang, Presentation

17 July 2009

D.B. Dunson, Multivariate Kernel Partition Processes

Presenter: Lan Du, Presentation

24 July 2009

M. Elad and I. Yavneh, A Weighted
Average of Sparse Representations is Better than the Sparsest One Alone

Presenter: Dehong Liu, Presentation

31 July 2009

R.P. Adams, I. Murray and D.J.C. MacKay, Tractable
Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process
Intensities

Presenter: Lihan He, Presentation

14 August 2009

T. Tieleman and G. Hinton, Using Fast Weights to
Improve Persistent Contrastive Divergence

Presenter: Jorge
Silva, Presentation

21 August 2009

R.P. Adams and Z. Ghahramani, Archipelago:
Nonparametric Bayesian Semi-Supervised Learning

Presenter: Lu Ren, Presentation

4 September 2009

F. Doshi-Velez, K.T. Miller, J. Van Gael and Y.W. Teh, Variational Inference for the
Indian Buffet Process

Presenter: John Paisley, Presentation

11 September 2009

N.D. Lawrence and R. Urtasun, Non-linear
Matrix Factorization with Gaussian Processes

Presenter: Eric Wang, Presentation

18 September 2009

H. Lee, R. Grosse, R. Ranganath, A.Y. Ng, Convolutional Deep Belief
Networks for Scalable Unsupervised Learning of Hierarchical Representations

Presenter: Mingyuan Zhou, Presentation

25 September 2009

A. Beygelzimer, S. Dasgupta and John Langford, Importance Weighted
Active Learning

Presenter: Lingbo Li, Presentation

2 October 2009

F. Doshi-Velez and Z. Ghahramani, Accelerated Sampling for
the Indian Buffet Process

Presenter: John Paisley, Presentation

16 October 2009

P. Liang, M.I. Jordan and D. Klein, Learning From
Measurements in Exponential Families

Presenter: Haojun Chen, Presentation

23 October 2009

L. Xu, M. White and D. Schuurmans, Optimal Reverse
Prediction: A Unified Perspective on Supervised, Unsupervised and
Semi-supervised Learning

Presenter: Chunping Wang, Presentation

30 October 2009

M.J. Choi, V. Chandrasekaran and A.S. Willsky, Exploiting Sparse
Markov *and *Covariance Structure in Multiresolution Models

Presenter: Minhua Chen, Presentation

13 November 2009

K. Yu, J. Lafferty, S. Zhu, Y. Gong, Large-Scale
Collaborative Prediction Using a Nonparametric Random Effects Model

Presenter: Xuejun Liao, Presentation

20 November 2009

K.A. Heller and Y.W. Teh and D. Gurur, Infinite Hierarchical Hidden
Markov Models

Presenter: Lu Ren, Presentation

18 December 2009

F. Wood and Y.W. Teh, A Hierarchical Nonparametric
Bayesian Approach to Statistical Language Model Domain Adaptation

Presenter: Mingyuan Zhou, Presentation

5 January 2010

Y.W. Teh, A
Bayesian Interpretation of Interpolated Kneser-Ney

R. Thibaux and M.I. Jordan, Hierarchical
Beta Processes and the Indian Buffet Process

Y.W. Teh and D. Gurur, Indian Buffet
Processes with Power-Law Behavior

Presenter: L. Carin, Presentation

15 January 2010

J. Ghosh and D.B. Dunson, Default Priors and
Efficient Posterior Computation in Bayesian Factor Analysis

Presenter: Eric Wang, Presentation

22 January 2010

D. Hsu, S.M. Kakade, J. Langford and T. Zhang, Multi-Label Prediction
via Compressed Sensing

Presenter: Lingbo Li, Presentation

29 January 2010

H.F. Lopes, E. Salazar and D. Gamerman, Spatial Dynamic Factor
Analysis

Presenter: Zhengming.Xing, Presentation

12 February 2010

P.J. Green and D.I. Hastie, Reversible Jump MCMC

Presenter: Miao Liu, Presentation

19 February 2010

K. Bush and J. Pineau, Manifold Embeddings
for Model-Based Reinforcement Learning Under Partial Observability

Presenter: Chenghui Cai, Presentation

26 February 2010

J. Luttinen and A. Ilin, Variational
Gaussian-Process Factor Analysis for Modeling Spatio-Temporal Data

Presenter: Bo Chen, Presentation

5 March 2010

H.M. Wallach, D. Minno and A. McCullum, Rethinking LDA: Why
Priors Matter

Presenter: Eric Wang, Presentation

12 March 2010

E.B. Fox, E.B. Sudderth, M.I. Jordan and A.S. Willsky, Sharing Features Among
Dynamical Systems With Beta Processes

Presenter: Haojun Chen, Presentation

19 March 2010

C. Wang and D.M. Blei, Decoupling Sparsity
and Smoothness in the Discrete Hierarchical Dirichlet Process

Presenter: Chunping Wang, Presentation

2 April 2010

T. Iwata, T. Yamada and N. Ueda, Modeling Social
Annotation Data With Content Relevance Using a Topic Model

Presenter: Jorge
Silva, Presentation

9 April 2010

J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman, Non-Local Sparse Models for
Image Restoration

Presenter: Mingyuan Zhou, Presentation

16 April 2010

O-A. Maillard and R. Munos, Compressed
Least-Squares Regression

Presenter: Minhua Chen, Presentation

23 April 2010

S. Gould, T. Gao and D. Koller, Region-Based
Segmentation and Object Detection

Presenter: Eric Wang, Presentation

30 April 2010

F. Doshi-Velez, The Infinite Partially
Observable Markov Decision Process

Presenter: Xuejun Liao, Presentation

7 May 2010

H. Lee, Y. Largman, P. Pham and A.Y. Ng, Unsupervised Feature
Learning for Audio Classification Using Convolutional Deep Belief Networks

Presenter: Bo Chen, Presentation

14 May 2010

D. Krishnan and R. Fergus, Fast Image
Deconvolution Using Hyper-Laplacian Priors

Presenter: Zhengming.Xing, Presentation

21 May 2010

A.C. Courville, D. Eck and Y. Bengio, An Infinite Factor
Modeling Hierarchy via a Noisy-Or Mechanism

Presenter: Lingbo Li, Presentation

28 May 2010

Y. Chen, M. Kapralpw, D. Pavlov and J.E. Canny, Factor Modeling for
Advertisement Targeting

Presenter: Miao Liu, Presentation

4 June 2010

K.T. Miller, T.L. Griffiths and M.I. Jordan, Nonparametric Latent
Feature Models for Link Prediction

Presenter: Minhua Chen, Presentation

11 June 2010

R. Jenatton, J. Mairal, G. Obozinski and F. Bach, Proximal Methods for
Sparse Hierarchical Dictionary Learning

Presenter: Bo Chen, Presentation

18 June 2010

P. Smaragdis, M. Shashanka and B. Raj, A Sparse
Non-Parametric Approach for Single Channel Separation of Known Sounds

Presenter: Priyadip Ray, Presentation

25 June 2010

V. Rao and Y.W. Teh, Spatial Normalized
Gamma Processes

Presenter: Eric Wang, Presentation

2 July 2010

M.N. Schmidt, Linearly
Constrained Bayesian Matrix Factorization for Blind Source Separation

Presenter: Jorge
Silva, Presentation

9 July 2010

R.P. Adams, H.M. Wallach and Z. Ghahramani, Learning the
Structure of Deep Sparse Graphical Models

Presenter: Zhengming Xing, Presentation

16 July 2010

P. Rai and H. Daume III, Model-Label Prediction
via Sparse Infinite CCA

Presenter: Lingbo Li, Presentation

23 July 2010

S. Bengio, F. Pereira, Y. Singer and D. Strelow, Group Sparse Coding

Presenter: Miao Liu, Presentation

30 July 2010

J. Vanhatalo, P. Jylanki and A. Vehtari, Gaussian Process
Regression With Student-t Likelihood

Presenter: Minhua Chen, Presentation

6 August 2010

S.-H. Yang, H. Zha and B.-G. Hu, Dirichlet-Bernoulli
Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance
Corpora

Presenter: Haojun Chen, Presentation

13 August 2010

L. Zhu, Y. Chen, W. Freeman and A. Torralba, Nonparametric Bayesian
Texture Learning and Synthesis

Presenter: Eric Wang, Presentation

20 August 2010

Y.L. Boureau, F. Bach, Y. LeCun and J. Ponce, Learning Mid-Level
Features for Recognition

Presenter: Bo Chen, Presentation

3 September 2010

J. Yang, K. Yu and T. Huang, Supervised
Translation-Invariant Sparse Coding

Presenter: Jorge Silva, Presentation

10 September 2010

R. Jenatton, G. Obozinski and F. Bach, Structured Sparse
Principal Component Analysis

Presenter: Xuejun Liao, Presentation

17 September 2010

K. Yu, T. Zhang and Y. Gong, Nonlinear Learning Using
Local Coordinate Coding

K. Yu and T. Zhang, Improved
Local Coordinate Coding Using Local Tangents

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang and Y. Gong, Locality-Constrained
Linear Coding for Image Classification

Presenter: Mingyuan Zhou, Presentation

24 September 2010

M.D. Hoffman, D.M. Blei and P.R. Cook, Bayesian
Nonparametric Matrix Factorization for Recorded Music

Presenter: Lu Ren, Presentation

1 October 2010

A. Rodriguez, D.B. Dunson and A.E. Gelfand, Latent Stick-Breaking Processes

Presenter: Esther Salazar, Presentation

8 October 2010

R. Yoshida and M.West, Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing

Presenter: Miao Liu, Presentation

15 October 2010

C.M.
Carvalho, N.G. Polson and J.G. James, The
horseshoe estimator for sparse signals

Presenter: Eric Wang, Presentation

22 October 2010

J. Chang and D.M. Blei, Hierarchical
Relational Models for Document Networks

Presenter: Haojun Chen, Presentation

5 November 2010

J. Friedman, T. Hastie and R. Tibshirani, Sparse Inverse
Covariance Estimation with the Graphical Lasso

N. Stadler and P. Buhlmann, Missing Values: Sparse
Inverse Covariance Estimation and an Extension to Sparse Regression

O. Banerjee, L. El Ghaoui and A. d’Aspremont, Model Selection Through
Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data

N. Meinshausen and P. Buhlmann, High-Dimensional
Graphs and Variable Selection with the Lasso

Presenter: Minhua Chen, Presentation

17 November 2010

N. Bartlett, D. Pfau, F. Wood, Forgetting
Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor
Process

Presenter: Yingjian Wang, Presentation

24 November 2010

R.P. Adams, Z. Ghahramani and M.I. Jordan, Tree-Structured Stick
Breaking Processes for Hierarchical Data

Presenter: XianXing Zhang, Presentation

3 December 2010

Y.L. Boureau, J. Ponce and Y. LeCun, A Theoretical Analysis of
Feature Pooling in Visual Recognition

Presenter: Bo Chen, Presentation

13 December 2010

K. Jarrett, K. Kavukcuoglu, M.A. Ranzato and Y. LeCun, What is the Best
Multi-Stage Architecture for Object Recognition?

Presenter: Lingbo Li, Presentation

20 December 2010

N. Joshi and M. Brady, Non-Parametric
Mixture Modeling Based Evolution of Level Sets and Application to Medical
Images

Presenter: Lu Ren, Presentation

7 January 2011

J. Zhu, A. Ahmed and E.P. Xing, MedLDA: Maximum Margin
Supervised Topic Models for Regression and Classification

Presenter: Haojun Chen, Presentation

14 January 2011

J. Zhu and E.P. Xing, Conditional
Topic Random Fields

Presenter: Eric Wang, Presentation

21 January 2011

J. Gasthaus, F. Wood, Yee Whye Teh, Lossless Compression
Based on the Sequence Memoizer

Presenter: Yingjian Wang, Presentation

18 February 2011

N. Srebro and T. Jaakkola, Weighted Low-Rank
Approximations

Presenter: Mingyuan Zhou , Presentation

25 February 2011

A. Torralba, K.P. Murphy and W.T. Freeman, Sharing Visual Features
for Multiclass and Multiview Object Detection

Presenter: Jorge Silva, Presentation

4 March 2011

A. McCallum, X. Wang and A. Corrada-Emmanuel, Topic and Role
Discovery in Social Networks with Experiments on Enron and Academic Email

Presenter: Miao Liu, Presentation

11 March 2011

F. Lin and W.W. Cohen, Power Iteration
Clustering

Presenter: Minhua Chen, Presentation

25 March 2011

Y. Zhang and J. Schneider, Learning Multiple
Tasks with a Sparse Matrix-Normal Prior

Presenter: Esther Salazar, Presentation

1 April 2011

K. Kavukcuoglu, P. Sermanet, Y.L. Boureau, K. Gregor, M. Mathieu, Y.
LeCun, Learming Convolutional Feature
Hierarchies for Visual Recognition

Presenter: Bo Chen, Presentation

8 April 2011

J. Mairal, F. Bach, J. Ponce, G. Sapiro, Online Learning for Matrix Factorization and Sparse Coding

Presenter:
Haojun Chen, Presentation

15 April 2011

M.D. Hoffman, D.B. Blei and F. Bach, Online Learning
for Latent Dirichlet Allocation

Presenter: Lingbo Li, Presentation

22 April 2011

J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos, Z. Ghahramani, Kronecker
Graphs: An Approach to Modeling Networks

Presenter: Eric Wang, Presentation

29 April 2011

A. Canale and D.B. Dunson, Bayesian Kernel
Mixtures for Counts

Presenter: Yingjian Wang, Presentation

6 May 2011

H. Ishwaaran and J.S. Rao, Spike and Slab Variable
Selection: Frequentist and Bayesian Strategies

Presenter: Minhua Chen, Presentation

13 May 2011

K. Weinberger and O. Chapelle, Large Margin Taxonomy
Embedding with an Application to Document Categorization

Presenter: Jorge Silva, Presentation

20 May 2011

S. Bengio, J. Weston and D. Grangier, Label Embedding
Trees for Large Multi-Class Tasks

Presenter: Zhengming Xing, Presentation

3 June 2011

D. Lin, E. Grimson and J. Fisher, Construction of
Dependent Dirichlet Processes Based on Poisson Processes

Presenter: Yingjian Wang, Presentation

10 June 2011

R. Salakhutdinov, J. Tenenbaum and A. Torralba, One-Shot Learning with a Hierarchical
Nonparametric Bayesian Model

Presenter: Esther Salazar, Presentation

17 June 2011

A. Coates, H. Lee and A.Y. Ng, An Analysis of Single-Layer Networks in Unsupervised Feature Learning

A. Coates and A.Y. Ng, The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization

Presenter: Mingyuan Zhou, Presentation

24 June 2011

J. Kallsen and P. Tankovy, Characterization
of Dependence of Multidimensional Lévy Processes Using Lévy Copulas

Presenter: Yingjian Wang, Presentation

8 July 2011

A.L. Chambers, P. Smyth and M. Steyvers, Learning Concept Graphs
from Text with Stick-Breaking Priors

Presenter: Lingbo Li, Presentation

15 July 2011

J. Paisley, C. Wang and D. Blei, The Discrete Infinite
Logistic Normal Distribution for Mixed-Membership Modeling

Presenter: Minhua Chen, Presentation

25 July 2011

B.P. Carlin and A. E. Gelfand, An Iterative Monte Carlo
Method for Nonconjugate Bayesian Analysis

P. Muller, A
Generic Approach to Posterior Integration and Gibbs Sampling

J.S. Liu, Metropolized
Independent Sampling with Comparisons to Rejection Sampling and Importance
Sampling

Presenter: Mingyuan Zhou, Presentation

5 August 2011

A. Ahmed and E.P.
Xing, Timeline: A Dynamic
Hierarchical Dirichlet Process Model for Recovering Birth/Death and Evolution
of Topics in Text Stream

Presenter: Bo Chen, Presentation

12 August 2011

R. M. Neal, Slice
Sampling

Presenter: Priyadip Ray, Presentation

19 August 2011

C.M. Carvalho, H.F. Lopes, N.G. Polson and M.A. Taddy, Particle Learning for
General Mixtures

N. Chopin, A. Iacobucci, J.-M. Marin, K. L. Mengersen, C. P. Robert, R. Ryder, and C. Schafer, On Particle Learning

Presenter: Miao Liu, Presentation

26 August 2011

I. Sutskever, R. Salakhutdinov, J.B. Tenenbaum, Modeling Relational Data
Using Bayesian Clustered Tensor Factorization

Presenter: Esther Salazar, Presentation

2 September 2011

A.P. Singh and G.J. Gordon, A Bayesian
Matrix Factorization Model for Relational Data

A.P. Singh and G.J. Gordan, Relational Learning via Collective Matrix Factorization

Presenter: XianXing Zhang, Presentation

16 September 2011

S. Williamson, C. Wang, K.A. Heller and D.M. Blei, The IBP
Compound Dirichlet Process and its Application to Focused Topic Modeling

Presenter: Eric Wang, Presentation

23 September 2011

L.-J. Li, C. Wang, Y. Lim, D.M. Blei, L. Fei-Fei, Building and Using a Semantivisual Image Hierarchy

Presenter: Lingbo Li, Presentation

3 October 2011

A. Geramifard, F. Doshi, J. Redding, N. Roy, and J.P. How, Online Discovery of
Feature Dependencies

Presenter: Xuejun Liao, Presentation

10 October 2011

M. Dud´ık, J. Langford and L.
Li, Doubly Robust
Policy Evaluation and Learning

Presenter: Miao Liu, Presentation

17 October 2011

M. Welling and Y.W. Teh, Bayesian Learning via
Stochastic Gradient Langevin Dynamics

Presenter: David Carlson, Presentation

26 October 2011

F. Doshi-Velez, D. Wingate, J. Tenenbaum and N. Roy, Infinite Dynamic
Bayesian Networks

Presenter: Mingyuan Zhou, Presentation

4 November 2011

D.L. Donoho, A. Maleki and A. Montanari, Message Passing Algorithms for Compressed
Sensing: I. Motivation and Construction

D.L. Donoho, A. Maleki and A. Montanari, Message Passing Algorithms for
Compressed Sensing: II. Analysis and Validation

Presenter: Nate Strawn, Presentation

11 November 2011

A. Banerjee, D. Dunson and S. Tokdar, Efficient Gaussian Process
Regression for Large Data Sets

Presenter: Priyadip Ray, Presentation

18 November 2011

L. Wang and D.B.
Dunson, Fast
Bayesian Inference in Dirichlet Process Mixture Models

Presenter: Esther
Salazar, Presentation

2 December 2011

A. Agovic, A. Banarjee and S. Chatterjee, Probabilistic Matrix
Addition

Presenter: Lingbo Li, Presentation

9 December 2011

J. Zhu and T.
Hastie, Kernel Logistic
Regression and the Import Vector Machine

Presenter: Ding
Mingtao, Presentation

16 December 2011

F. Liang, K. Miao,
M. Liao, S. Mukherjee and M. West, Nonparametric
Bayesian Kernel Models

Presenter:
Yingjian Wang, Presentation

13 January 2012

Z. Zhang, G. Dai and M.I. Jordan*, *Bayesian
Generalized Kernel Mixed Models

Presenter:
Priyadip Ray, Presentation

20 January 2012

N.S. Pillai, Q.
Wu, F. Liang, S. Mukherjee and* *R.L. Wolpert,* *Characterizing
the Function Space for Bayesian Kernel Models

Presenter:
Mingyuan Zhou, Presentation

27 January 2012

L. Zhang, C.
Chen, J. Bu, D. Cai, X. He, T.S. Huang, Active Learning Based on
Locally Linear Reconstruction

Presenter: Minhua
Chen, Presentation

3 February 2012

N. Friedman and D. Koller, Being
Bayesian About Network Structure: A Bayesian Approach to Structure Discovery in
Bayesian Networks

Presenter: XianXing Zhang, Presentation

10 February 2012

N.G. Polson and
J.G. Scott, Local
Shrinkage Rules, Levy Processes, and Regularized Regression

Presenter: David Carlson, Presentation

17 February 2012

M. Kolar, J. Lafferty and L. Wasserman, Union Support Recovery in
Multi-task Learning

Presenter: Shaobo Han, Presentation

2 March 2012

Y. Hitomi, J. Gu, M. Gupta, T. Mitsunaga and S. K. Nayar, Video from a Single
Coded Exposure Photograph using a Learned Over-Complete Dictionary

Presenter: Ajit Rajwade, Presentation

9 March 2012

M. Harel and S. Mannor, Learning from Multiple
Outlooks

B. Kulis, K. Saenko and T. Darrel, What You Saw is Not What
You Get: Domain Adaptation Using Asymmetric Kernel Transforms

Presenter: Minhua Chen, Presentation

16 March 2012

A. Armagan, D.B. Dunson and J. Lee, Generalized double
Pareto shrinkage

Presenter: Esther Salazar, Presentation

30 March 2012

A. Bhattacharya and D.B. Dunson, Sparse Bayesian
infinite factor models

Presenter:
XianXing Zhang, Presentation

6 April 2012

E. Grave, G. Obozinski and F. Bach, Trace Lasso: a trace norm
regularization for correlated designs

Presenter: Zhengming Xing, Presentation

13 April 2012

H. Rue and S. Martino, Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Presenter: Esther Salazar, Presentation

20 April 2012

O. Dikmen and C. Fevotte, Nonnegative
Dictionary Learning in the Exponential Noise Model for Adaptive Music Signal
Representation

Presenter: Ding Mingtao, Presentation

7 May 2012

F. Niu, B. Recht, C. Re and S.J. Wright, A Lock-Free Approach to
Parallelizing Stochastic Gradient Descent

Presenter: David Carlson, Presentation

14 May 2012

Z. James, X. Hao Xu and P.J. Ramadge, Learning Sparse
Representations of High Dimensional Data on Large Scale Dictionaries

Presenter: David Lue, Presentation

21 May 2012

A.C. Sankaranarayanan, P.K. Turaga, R. Chellappa, and R.G. Baraniuk, Compressive Acquisition of Dynamical Scenes

Presenter: Ajit
Rajwade, Presentation

4 June 2012

T. Zhou, H. Shan, A. Banerjee, G. Sapiro, Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information

Presenter: Jorge Silva, Presentation

11 June 2012

A. Rodriguez and K. Ghosh, Nested
Partition Models

Presenter: Esther Salazar, Presentation

18 June 2012

D. Zoran and Y, Weiss, From Learning
Models of Natural Image Patches to Whole Image Restoration

Presenter: Eric Wang, Presentation

6 July 2012

Z. Xu, F. Yan and Y. Qi, Infinite Tucker Decomposition:
Nonparametric Bayesian Models for Multiway Data Analysis

Presenter: Joe Ryu, Presentation

13 July 2012

Z. Yang, C. Zhang and L. Xie, Robustly Stable
Signal Recovery in Compressed Sensing with Structured Matrix Perturbation

Presenter: Minhua Chen, Presentation

20 July 2012

P. Bachman and D. Precup, Improved Estimation in Time Varying Models

Presenter: Zhengming Xing, Presentation

27 July 2012

G. Brown, A. Pocock, M.-J. Zhao and M. Lujan, Conditional Likelihood
Maximisation: A Unifying Framework for Information Theoretic Feature Selection

Presenter: Wenzhao Lian, Presentation

3 August 2012

J. Bruna and S. Mallat, Invariant Scattering
Convolution Networks

Presenter: Bo Chen, Presentation

15 August 2012

Y. Bachrach, T. Minka, J. Guiver and T. Graepel, How To Grade a Test
Without Knowing the Answers: A Bayesian Graphical Model for Adaptive
Crowdsourcing and Aptitude Testing

Presenter: David Carlson, Presentation

24 August 2012

J. Zhu, A. Ahmed, E.P. Xing*, *MedLDA: Maximum Margin
Supervised Topic Models

Presenter: Zhengming Xing, Presentation

31 August 2012

A. Jalali and Sujay Sanghavi, Learning the Dependence Graph of
Time Series with Latent Factors

Presenter: Xin Yuan, Presentation

7 September 2012

Y. Tang, R. Salakhutdinov and G. Hinton, Deep Mixtures of Factor
Analysers

Presenter: Jianbo Yang, Presentation

14 September 2012

B. Cseke and T. Heskes, Improving
Posterior Marginal Approximations in Latent Gaussian Models

B. Cseke and T. Heskes, Approximate Marginals in
Latent Gaussian Models

Presenter: Shaobo Han, Presentation

21 September 2012

P. Sprechmann, A. Bronstein and G. Sapiro, Learning Efficient Structured
Sparse Models

Presenter: Yingjian Wang, Presentation

28 September 2012

J. Zhu, Max-Margin
Nonparametric Latent Feature Models for Link Prediction

Presenter: XianXing Zhang, Presentation

5 October 2012

C. Farabet, C. Couprie, L. Najman and Y. LeCun, Scene Parsing with Multiscale
Feature Learning, Purity Trees, and Optimal Covers

Presenter: Bo Chen, Presentation

15 October 2012

T.P. Minka, R. Xiang and Y. Qi, Virtual Vector Machine for
Bayesian Online Classification

Presenter: Lingbo Li, Presentation

24 October 2012

C. Chen, N. Ding and W. Buntine, Dependent Hierarchical
Normalized Random Measures for Dynamic Topic Modeling

Presenter: Mingyuan Zhou, Presentation

6 November 2012

R. Peharz and F. Pernkopf, Exact Maximum Margin Structure
Learning of Bayesian Networks

Presenter: Miao Liu, Presentation

16 November 2012

Y. Shi and Fei Sha, Information-Theoretical
Learning of Discriminative Clusters for Unsupervised Domain Adaptation

Presenter: Liming Wang, Presentation

26 November 2012

N. Le Roux, M. Schmidt and F. Bach, A Stochastic Gradient Method
with an Exponential Convergence Rate for Strongly-Convex Optimization with
Finite Training Sets

Presenter: Miao Liu, Presentation

3 December 2012

C. Bracegirdle and D. Barber, Bayesian Conditional
Cointegration

Presenter: Jianbo Yang, Presentation

12 December 2012

J. Paisley, C. Wang, D.M. Blei and M.I. Jordan, Nested
Hierarchical Dirichlet Processes

Presenter: David Carlson, Presentation

11 January 2013

C. Wang and D. Blei, Truncation-free
Stochastic Variational Inference for Bayesian Nonparametric Models

Presenter: XianXing Zhang, Presentation

17 January 2013

I.J. Goodfellow, A. Courville and Y. Bengio, Large-Scale Feature Learning
With Spike-and-Slab Sparse Coding

Presenter: Xin
Yuan, Presentation

25 January 2013

A. Ahmed, S. Ravi, S.M. Narayanamurthy, A.J. Smola, FastEx: Hash
Clustering with Exponential Families

Presenter: Jianbo Yang, Presentation

1 February 2013

D.I. Kim, M.C. Hughes and E.B. Sudderth, The Nonparametric Metadata
Dependent Relational Model

Presenter: Zhengming Xing, Presentation

22 February 2013

Y.J. Ko and M. Seeger, Large
Scale Variational Bayesian Inference for Structured Scale Mixture Models

Presenter: Mingyuan Zhou, Presentation

1 March 2013

S. Ahn, A. Korattikara and M. Welling, Bayesian Posterior Sampling via
Stochastic Gradient Fisher Scoring

Presenter:
David Carlson, Presentation

8 March 2013

D. Mimno, M.D. Hoffman and D.M. Blei, Sparse stochastic inference for
latent Dirichlet allocation

Presenter: Miao Liu, Presentation

15 March 2013

A. Joulin and F. Bach, A
convex relaxation for weakly supervised classifiers

Presenter: Joe Ryu, Presentation

29 March 2013

J. Paisley, D.M. Blei, M.I.
Jordan, Variational Bayesian
Inference with Stochastic Search

Presenter:
Mingyuan Zhou, Presentation

5 April 2013

M.J. Paul and M. Dredze, Factorial LDA: Sparse
Multi-Dimensional Text Models

Presenter: Lingbo Li, Presentation

12 April 2013

A. Banerjee, S. Merugi, I.S. Dhillon and J. Ghosh, Clustering with Bregman Divergences

Presenter: Liming Wang, Presentation

19 April 2013

H. Nickisch and C.E. Rasmussen, Approximations for
Binary Gaussian Process Classification

Presenter: Shaobo Han, Presentation

26 April 2013

P. Pletscher and S. Wulff, LPQP for MAP: Putting LP Solvers
to Better Use

Presenter: Zhengming Xing, Presentation

3 May 2013

S. Balakrishnan, K. Puniyani and J. Lafferty, Sparse Additive Functional and
Kernel CCA

Presenter: Miao Liu, Presentation

14 May 2013

R. Fujimaki and S. Morinaga, Factorized
Asymptotic Bayesian Inference for Mixture Modeling

R. Fujimaki and K. Hayashi, Factorized
Asymptotic Bayesian Hidden Markov Models

Presenter: Hui Li, Presentation

20 May 2013

A. Kumar and H. Daume III, Learning Task Grouping and
Overlap in Multi-Task Learning

Presenter: Kyle Ulrich, Presentation

7 June 2013

H. Liu, F. Han, M. Yuan, J. Lafferty and L. Wasserman, The Nonparanormal SKEPTIC

H. Liu, F. Han, M. Yuan, J. Lafferty and L. Wasserman, High-Dimensional Semiparametric
Gaussian Copula Graphical Models** **

Presenter: Esther Salazar, Presentation

14 June 2013

D.P. Wipf, B.D. Rao*, *and
S. Nagarajan, Latent
Variable Bayesian Models for Promoting Sparsity

Presenter: Yan Kaganovsky, Presentation

21 June 2013

P. Sarkar, D. Chakrabartiy and M.I. Jordan, Nonparametric Link Prediction in Dynamic Networks

Presenter: Changwei Hu, Presentation

1 July 2013

A. Spiliopoulou and A. Storkey, A Topic Model for Melodic
Sequences

Presenter: Xin Yuan, Presentation

8 July 2013

M. Girolami and B. Calderhead, Riemann Manifold
Langevin and Hamiltonian Monte Carlo Methods

Presenter: Hui Li, Presentation

15 July 2013

R. Garnett, Y. Krishnamurthy, X. Xiong, J. Schneider and R. Mann, Bayesian Optimal Active Search
and Surveying

Presenter: Jianbo Yang, Presentation

22 July 2013

A. Armagan, D.B. Dunson and M. Clyde, Generalized Beta
Mixtures of Gaussians

Presenter: Shaobo
Han, Presentation

2 August 2013

N.J. Foti, J.D. Futoma, D.N. Rockmore and S. Williamson, A unifying representation
for a class of dependent random measures

Presenter: L. Carin, Presentation

9 August 2013

V. Bittorf, B. Recht, C. Re, and J.A. Tropp, Factoring nonnegative matrices
with linear programs

Presenter: David Carlson, Presentation

19 August 2013

M. Xu and J. Lafferty, Conditional
Sparse Coding and Grouped Multivariate Regression

Presenter: Kyle Ulrich, Presentation

26 August 2013

A.C. Damianou and N.D. Lawrence, Deep Gaussian
Processes

Presenter: Esther Salazar, Presentation

30 August 2013

P. Wang and P. Blunsom, Collapsed
Variational Bayesian Inference for Hidden Markov Models

Presenter: Yan Kaganovsky, Presentation

6 September 2013

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J.
Eckstein, Distributed
Optimization and Statistical Learning via the Alternating Direction Method of
Multipliers

Presenter: XianXing Zhang, Presentation

13 September 2013

T. Broderick, N. Boyd, A. Wibisono, A.C. Wilson, M.I. Jordan, Streaming Variational
Bayes

Presenter: Miao Liu, Presentation

11 October 2013

M. Park, O. Koyejo, J. Ghosh, R.A. Poldrack and J.W. Pillow, Bayesian
Structure Learning for Functional Neuroimaging

Presenter: Zhengming Xing, Presentation

18 October 2013

A. Aderhold D. Husmeier V.A. Smith, Reconstructing
ecological networks with hierarchical Bayesian regression and Mondrian
processes

Presenter: Yingjian Wang, Presentation

25 October 2013

F.L. Wauthier and M.I. Jordan, Heavy-Tailed
Process Priors for Selective Shrinkage

Presenter: Kyle Ulrich, Presentation

8 November 2013

J.D. Lee and T.J. Hastie, Structure Learning
of Mixed Graphical Models

Presenter: Shaobo Han, Presentation

15 November 2013

Y. Karklin and E.P. Simoncelli, Efficient
coding of natural images with a population of noisy Linear-Nonlinear neurons

Presenter: David Carlson, Presentation

22 November 2013

T. Jaakkola, M. Meila and T. Jebara, Maximum Entropy Discrimination

Presenter: XianXing Zhang, Presentation

7 December 2013

T. Herlau, M. Mørup and M. Schmidt, Modeling
Temporal Evolution and Multiscale Structure in Networks

Presenter: Changwei Hu, Presentation

13 December 2013

S. Arora, R. Ge, Y. Halpern, D. Mimno, A. Moitra, D. Sontag,
Y. Wu and M. Zhu, A Practical
Algorithm for Topic Modeling with Provable Guarantees

Presenter: Zhao Song, Presentation

20 December 2013

P.R. Hahn, C.M. Carvalho and S. Mukherjee, Partial Factor
Modeling: Predictor-Dependent Shrinkage for Linear Regression

Presenter: Esther Salazar, Presentation

10 January 2013

S. Williamson. A. Dubey and E. Xing, Parallel
Markov Chain Monte Carlo for Nonparametric Mixture Models

Presenter: Piyush Rai, Presentation

17 January 2013

A. Kleiner, A.
Talwalkar, P. Sarkar and M.I. Jordan, A Scalable
Bootstrap for Massive Data

Presenter: XianXing
Zhang, Presentation

7 February 2014

A. Wilson and R. Adams, Gaussian Process
Kernels for Pattern Discovery and Extrapolation

Presenter: Kyle Ulrich, Presentation

21 February 2014

Y. Wu, J. M. Hernandez-Lobato and Z. Ghahraman, Dynamic Covariance
Models for Multivariate Financial Time Series

Presenter: Zhengming Xing, Presentation

28 February 2014

B. Poczos, A. Rinaldo, A. Singh and L. Wasserman, Distribution-Free Distribution
Regression

Presenter: Esther Salazar, Presentation

7 March 2014

D.I. Inouye, P. Ravikumar and I.S. Dhillon, Admixture of Poisson MRFs:
A Topic Model with Word Dependencies

Presenter: Shaobo Han, Presentation

14 March 2014

B. Lakshminarayanan, D. Roy and Y.W. Teh, Top-down particle filtering for Bayesian decision trees

Presenter: David Carlson, Presentation

21 March 2014

C. Heaukulani and Z. Ghahramani, __Dynamic
Probabilistic Models for Latent Feature Propagation in Social Networks__

Presenter: Zhao Song, Presentation

28 March 2014

T. Broderick, B. Kulis and M.I. Jordan, MAD-Bayes:
MAP-based Asymptotic Derivations from Bayes

Presenter: Piyush Rai, Presentation

4 April 2014

J. Fan, Y. Liao and
M. Mincheva, Large
Covariance Estimation by Thresholding Principal Orthogonal Complements

Presenter: Esther
Salazar, Presentation

11 April 2014

Z. Wang, S. Mohamed, D.F. Nando, Adaptive
Hamiltonian and Riemann Manifold Monte Carlo

Presenter: Qi Mao, Presentation

18 April 2014

R. Gribonval, R. Jenatton, F. Bach, M. Kleinsteuber, M. Seibert, Sample Complexity of Dictionary
Learning and other Matrix Factorizations

M. Seibert, M. Kleinsteuber, R. Gribonval, R. Jenatton, F. Bach, On the Sample Complexity of Sparse Dictionary Learning

Presenter: Xuejun Liao, Presentation

25 April 2014

P. Hoff, Equivariant
and scale-free Tucker decomposition models

Presenter: Yingjian Wang, Presentation

2 May 2014

A. Banerjee, J. Murray and D.B. Dunson, Bayesian learning of
joint distributions of objects

Presenter: Kyle Ulrich, Presentation

9 May 2014

P. Hoff, B. Fosdick, A. Volfovsky, and K. Stovel, Likelihoods for fixed rank nomination networks

Presenter: Zhengming Xing, Presentation

16 May 2014

F. Caron and E.B. Fox, Bayesian nonparametric models of sparse and exchangeable random graphs

Presenter: Esther Salazar, Presentation

23 May 2014

B. Gong, K. Grauman and F. Sha, Reshaping Visual Datasets for Domain Adaptation

Presenter: David Carlson, Presentation

13 June 2014

P. Ruvolo and E. Eaton, Online Multi-Task Learning Based on K-SVD

Presenter: Zhengming Xing, Presentation

20 June 2014

A. Shah, A.G. Wilson and Z. Ghahramani, Student-t Processes as Alternatives to Gaussian Processes

Presenter: Ricardo Henao, Presentation

27 June 2014

M.C. Hughes and E.B. Sudderth, Memoized Online Variational Inference for Dirichlet Process Mixture Models

Presenter: Kyle Ulrich, Presentation

3 July 2014

B. Gong, K. Grauman and F. Sha, Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation

Presenter: Piyush Rai, Presentation

25 July 2014

Y. Bengio, Learning Deep Architectures for AI

Presenter: Piyush Rai, Presentation

1 August 2014

Y. Bengio, A. Courville and P. Vincent, Representation Learning: A Review and New Perspectives

Presenter: Piyush Rai, Presentation

8 August 2014

S. Nakajima and M. Sugiyama, Analysis of Empirical MAP and Empirical Partially Bayes: Can They be Alternatives to Variational Bayes?

Presenter: Zhe Gan, Presentation

15 August 2014

W. Neiswanger, F. Wood and E.
Xing, The
Dependent Dirichlet Process Mixture of Objects for Dection-free Tracking and
Object Modeling

Presenter: Yunchen Pu, Presentation

22 August 2014

M.K. Titsias and M. Lazaro-Gredilla, Doubly Stochastic Variational Bayes for non-Conjugate Inference

Presenter: Yan Kaganovsky, Presentation

12 September 2014

N. Srivastava and R. Salakhutdinov, Multimodal Learning with Deep Boltzmann Machines

Presenter: Ricardo Henao, Presentation

19 September 2014

M. Unser, P.D. Tafti, and Q. Sun, A Unified Formulation of Gaussian Versus Sparse Stochastic Processes—Part I: Continuous-Domain Theory

M. Unser, P.D. Tafti, and Q. Sun, A Unified Formulation of Gaussian Versus Sparse Stochastic Processes—Part II: Discrete-Domain Theory

Presenter: Liming Wang, Presentation

26 September 2014

D.J. Rezende, S. Mohamed and D. Wierstra, Stochastic Backpropagation and Approximate Inference in Deep Generative Models

D.P. Kingma and M. Welling, Auto-Encoding Variational Bayes

A. Mnih and K. Gregor, Neural Variational Inference and Learning in Belief Networks

D.P. Kingma, D.J. Rezende, S. Mohamed, M. Welling, Semi-supervised Learning with Deep Generative Models

Presenter: David Carlson, Presentation

3 October 2014

N. Ding, Y. Fang, R. Babbush, C. Chen, R.D. Skeel, H. Neven, Bayesian Sampling Using Stochastic Gradient Thermostats

Presenter: Changyou Chen, Presentation

10 October 2014

Y. Park, C.M. Carvalho, J. Ghosh, LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series

Presenter: Esther Salazar, Presentation

17 October 2014

J. Hensman M. Zwießele N.D. Lawrence, Tilted Variational Bayes

Presenter: Kyle Ulrich, Presentation

31 October 2014

P. Gopalan, F.J.R. Ruiz, R. Ranganath and D.M. Blei, Bayesian Nonparametric Poisson Factorization for Recommendation Systems

Presenter: Yunchen Pu, Presentation

7 November 2014

G. Andrew, R. Arora, J. Bilmes
and K. Livescu, Deep
Canonical Correlation Analysis

Presenter: Shaobo Han, Presentation

14 November 2014

N. Srivastava, R. Salakhutdinov and G. Hinton, Modeling Documents with a Deep Boltzmann Machine

Presenter: Zhe Gan, Presentation

21 November 2014

C. Chen, J. Zhu and X. Zhang, Robust Bayesian Max-Margin Clustering

Presenter: Ricardo Henao, Presentation

5 December 2014

S. Patterson and Y.W. Teh, Stochastic
Gradient Riemannian Langevin Dynamics__ on
the Probability Simplex__

Presenter: Changyou Chen, Presentation

12 December 2014

A. Solin and S. Sarkk, Explicit Link Between Periodic Covariance Functions and State Space Models

Presenter: Piyush Rai, Presentation

19 December 2014

D. Lin, Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation

Presenter: Changwei Hu, Presentation

9 January 2015

C.-J. Hsieh, M.A. Sustik, I.S. Dhillon, P. Ravikumar, R.A. Poldrack, BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables

Presenter: Yan Kaganovsky, Presentation

16 January 2015

M. Zhou, Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling

Presenter: Esther Salazar, Presentation

23 January 2015

L.F. James, Poisson Latent Feature Calculus for Generalized Indian Buffet Processes

Presenter: Piyush Rai, Presentation

30 January 2015

Y.W. Teh, A.H. Thiery, S.J. Vollmer, Consistency and Fluctuations for Stochastic Gradient Langevin Dynamics

Presenter: Changyou Chen, Presentation

13 February 2015

T. Chen, E.B. Fox, C. Guestrin, Stochastic Gradient Hamiltonian Monte Carlo

Presenter: Wenzhao Lian, Presentation

27 February 2015

T.D. Nguyen, T. Tran, D. Phung, S. Venkatesh, Tensor-variate Restricted Boltzmann Machines

Presenter: Xin Yuan, Presentation

6 March 2015

D. Newman, A. Asuncion, P Smyth, M. Welling, Distributed Algorithms for Topic Models

S. Ahn, B. Shahbaba, M. Welling, Distributed Stochastic Gradient MCMC

Presenter: Changyou Chen, Presentation

20 March 2015

R. Guhaniyogi, S. Qamar and D.B. Dunson, Bayesian Conditional Density Filtering

Presenter: Ricardo Henao, Presentation

27 March 2015

N. Houlsby, J.M. Hernandez-Lobato and Z. Ghahramani, Cold-start Active Learning with Robust Ordinal Matrix Factorization

Presenter: Piyush Rai, Presentation

3 April 2015

J.G. Scott and L. Sun, Expectation-maximization for logistic regression

Presenter: Changwei Hu, Presentation

10 April 2015

V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra M. Riedmiller, Playing Atari with Deep Reinforcement Learning

X. Guo, S. Singh, H. Lee, R. Lewis, and X. Wang, Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning

V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg and D. Hassabis, Human-Level Control Through Deep Reinforcement Learning

Presenter: Zhao Song, Presentation

17 April 2015

Q. Le and T. Mikolov, Distributed Representations of Sentences and Documents

Presenter: Chunyuan Li, Presentation

24 April 2015

R. Kiros, R. Salakhutdinov and R. Zemel, Multimodal Neural Language Models

Presenter: David Carlson, Presentation

1 May 2015

S. Lan, B. Zhou, and B. Shahbaba, Spherical Hamiltonian Monte Carlo for Constrained Target Distributions

Presenter: Changyou Chen, Presentation

8 May 2015

S. Reed, Kihyuk Sohn, Y. Zhang and H. Lee, Learning to Disentangle Factors of Variation with Manifold Interaction

Presenter: Kyle Ulrich, Presentation

15 May 2015

A. Graves, Generating Sequences With Recurrent Neural Networks

Presenter: Zhe Gan, Presentation

12 June 2015

M. Nickel, K. Murphy, V. Tresp and E. Gabrilovich, A Review of Relational Machine Learning for Knowledge Graphs: From Multi-Relational Link Prediction to Automated Knowledge Graph Construction

Presenter: Piyush Rai, Presentation

26 June 2015

A. Korattikara, Y. Chen and M. Welling, Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget

Presenter: Chunyuan Li, Presentation

3 July 2015

R. Bardenet, A. Doucet and C. Holmes, Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach

Presenter: Ricardo Henao, Presentation

17 July 2015

O. Rippel, J. Snoek and R.P. Adams, Spectral Representations for Convolutional Neural Networks

Presenter: David Carlson, Presentation

24 July 2015

R.C. Grande, T.J. Walsh and J.P. How, Sample Efficient Reinforcement Learning with Gaussian Process

Presenter: Zhao Song, Presentation

31 July 2015

J. Tang, Z. Meng, X.L. Nguyen, Q. Mei and M. Zhang, Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis

Presenter: Chen Changyou, Presentation

7 August 2015

G. Ver Steeg, A. Galstyan, F. Sha and S. DeDeo, Demystifying Information-Theoretic Clustering

Presenter: Chunyuan Li, Presentation

14 August 2015

P.O. Pinheiro and R. Collobert, Recurrent Convolutional Neural Networks for Scene Labeling

Presenter: Yizhe Zhang, Presentation

21 August 2015

T. Shi and J. Zhu, Online Bayesian Passive-Aggressive Learning

Presenter: Kyle Ulrich, Presentation

4 September 2015

D. Tran, D.M. Blei, and E.M. Airoldi, Variational inference with copula augmentation

Presenter: Shaobo Han, Presentation

11 September 2015

Y.-A. Ma, T. Chen and E.B. Fox, A Complete Recipe for Stochastic Gradient MCMC

Presenter: Changyou Chen, Presentation

18 September 2015

F.-X. Briol, C.J. Oates, M. Girolami, M.A. Osborne, Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

Presenter: Ricardo Giraldo, Presentation

25 September 2015

K. Xu, J. L. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R.S. Zemel, Y. Bengio, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Presenter: Yunchen Pu, Presentation

2 October 2015

K. Gregor, I. Danihelka, A. Graves, D. Rezende, D. Wierstra, DRAW: A Recurrent Neural Network For Image Generation

Presenter: Zhe Gan, Presentation

23 October 2015

M. Betancourt, The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling

Presenter: Chunyuan Li, Presentation

30 October 2015

M. Long, Y. Cao, J. Wang, M.I. Jordan, Learning Transferable Features with Deep Adaptation Networks

Presenter: Changyou Chen, Presentation

6 November 2015

H. Zhao, M. Melibari, P. Poupart, On the Relationship between Sum-Product Networks and Bayesian Networks

Presenter: Kyle Ulrich, Presentation

20 November 2015

N. Srivastava, E. Mansimov and R. Salakhudinov, Unsupervised Learning of Video Representations using LSTMs

Presenter: Zhe Gan, Presentation

11 December 2015

R. Nishihara, L. Lessard, B. Recht, A. Packard, M. Jordan, A General Analysis of the Convergence of ADMM

Presenter: Xuejun Liao, Presentation

17 December 2015

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Presenter: Zhao Song, Presentation

15 January 2016

Y. Gal and Z. Ghahramani, Dropout as a Bayesian Approximation: Insights and Applications

Presenter: Chunyuan Li, Presentation

29 January 2016

D.P. Kingma, T. Salimans and M. Welling, Variational Dropout and the Local Reparameterization Trick

Presenter: Changwei Hu, Presentation

12 February 2016

X. Shang, Z. Zhu, B. Leimkuhler, and A.J. Storkey, Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling

Presenter: Changyou Chen, Presentation

19 February 2016

T. Herlau, M. Mørup and M.N. Schmidt, Bayesian Dropout

Presenter: Yizhe Zhang, Presentation

26 February 2016

D. Liang and J. Paisley, Landmarking Manifolds with Gaussian Processes

Presenter: Ricardo Henao, Presentation

4 March 2016

T. Salimans, D. Kingma, M. Welling, Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

Presenter: Changyou Chen, Presentation

11 March 2016

S. Flaxman, A. Wilson, D. Neill, H. Nickisch, A. Smola, Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods

Presenter: Yizhe Zhang, Presentation

18 March 2016

Y. Gal, Y. Chen and Z. Ghahramani, Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data

Presenter: Kyle Ulrich, Presentation

25 March 2016

W. Tansey, O.H.M. Padilla, A.S. Suggala and P. Ravikumar, Vector-Space Markov Random Fields via Exponential Families

Presenter: Chunyuan Li, Presentation

1 April 2016

M. Kandemir, Asymmetric Transfer Learning with Deep Gaussian Processes

Presenter: Shaobo Han, Presentation

8 April 2016

R. Yu, D. Cheng, Y. Liu, Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams

Presenter: Qinlian Su, Presentation

15 April 2016

W. Wang, R. Arora, K. Livescu, J. Bilmes, On Deep Multi-View Representation Learning

Presenter: Yizhe Zhang, Presentation

22 April 2016

H. Kim, J. Xu, B. Vemuri, V. Singh, Manifold-valued Dirichlet Processes

Presenter: Zhao Zhang, Presentation

29 April 2016

M. Das, T. Bansal and C. Bhattacharyya, Ordered Stick-Breaking Prior for Sequential MCMC Inference of Bayesian Nonparametric Models

Presenter: Changwei Hu, Presentation

13 May 2016

A. Shah, D. Knowles, Z. Ghahramani, An Empirical Study of Stochastic Variational Inference Algorithms for the Beta Bernoulli Process

Presenter: Kevin Liang, Presentation

27 May 2016

L.-C. Chen, A. Schwing, A. Yuille, R. Urtasun, Learning Deep Structured Models

Presenter: Ricardo Henao, Presentation

17 June 2016

J. Sun, Q. Qu, J. Wright, Complete Dictionary Recovery Using Nonconvex Optimization

Presenter: Changyou Chen, Presentation

24 June 2016

J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. Patwary, M. Prabhat, R. Adams, Scalable Bayesian Optimization Using Deep Neural Networks

Presenter: Ikenna Odinaka, Presentation

1 July 2016

M. Zhou, Y. Cong, and B. Chen, The Poisson Gamma Belief Network

Presenter: Shaobo Han, Presentation

8 July 2016

M.E. Khan, R. Babanezhad, W. Lin, M. Schmidt, and M.Sugiyama, Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence

Presenter: Yan Kaganovsky, Presentation

15 July 2016

D. Rezende and S. Mohamed, Variational Inference with Normalizing Flows

Presenter: Kevin Liang, Presentation

22 July 2016

U. Simsekli, R. Badeau, A.T. Cemgil and G. Richard, Stochastic Quasi-Newton Langevin Monte Carlo

Presenter: Gregory Spelling, Presentation

17 August 2016

C. Naesseth, F. Lindsten, T. Schon, Nested Sequential Monte Carlo Methods

Presenter: Qinliang Su, Presentation

26 August 2016

P. Toulis, D. Tran, E.M. Airoldi, Towards Stability and Optimality in Stochastic Gradient Descent

Presenter: Ikenna Odinaka, Presentation

2 September 2016

D. Duvenaud, D. Maclaurin and R.P. Adams, Early Stopping as Nonparametric Variational Inference

Presenter: Zhe Gan, Presentation

9 September 2016

Y. Wang, M. Brubaker, B. Chaib-draa and R. Urtasun, Sequential Inference for Deep Gaussian Process

Presenter: Lei Yu, Presentation

16 September 2016

A.D. Saul, J. Hensman, A. Vehtari and N.D. Lawrence, Chained Gaussian Processes

Presenter: Yitong Li, Presentation

23 September 2016

A.G. Wilson, Z. Hu R. Salakhutdinov and E.P. Xing, Deep Kernel Learning

Presenter: Qinlian Su, Presentation

30 September 2016

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D.
Warde-Farley, S. Ozair, A. Courville and Y. Bengio, Generative Adversarial Nets

X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever and P. Abbeel, InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow and B. Frey, Adversarial Autoencoders

S. Reed, Z. Akata, X. Yan, L. Logeswaran B. Schiele and H. Lee, Generative Adversarial Text to Image Synthesis

A. Radford, L. Metz and S. Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

M. Mirza and S. Osindero, Conditional Generative Adversarial Nets

Presenter: Yunchen Pu, Presentation

7 October 2016

S.R. Bowman, L. Vilnis, O. Vinyals,
A.M. Dai, R. Jozefowicz and S. Bengio, Generating Sentences from a
Continuous Space

J. Chung, K. Kastner, L. Dinh, K. Goel, A. Courville and Y. Bengio A Recurrent Latent Variable Model for Sequential Data

S. Gu, Z. Ghahramani and R.E. Turner, Neural Adaptive Sequential Monte Carlo

M. Fraccaro, S.K. Sønderby, U. Paquet and O. Winther, Sequential Neural Models with Stochastic Layers

Y. Miao, L. Yu and Phil Blunsom, Neural Variational Inference for Text Processing

M.J. Johnson, D. Duvenaud, A.B. Wiltschko, S.R. Datta, R.P. Adams, Structured VAEs: Composing Probabilistic Graphical Models and Variational Autoencoders

Presenter: Zhe Gan, Presentation

14 October 2016

M. Yasuda, Relationship Between Pretraining and Maximum Likelihood Estimation in Deep Boltzmann Machines

Presenter: Wenlin Wang, Presentation

21 October 2016

I. Murray and M.M. Graham, Pseudo-Marginal Slice Sampling

Presenter: Yizhe Zhang, Presentation

28 October 2016

R. Bardenet, A. Doucet and C. Holmes, On Markov chain Monte Carlo methods for tall data

Presenter: David Carlson, Presentation

11 November 2016

M.U. Gutmann and A. Hyvarinen, Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics

Presenter: Yitong Li, Presentation

18 November 2016

S. Reed, Z. Akata, H. Lee, and B. Schiele, Learning Deep Representations of Fine-Grained Visual Descriptions

X. Zhang, J. Zhao and Y. LeCun, Character-level Convolutional
Networks for Text

Presenter: Zhe Gan, Presentation

16 December 2016

A.B.L. Larsen1, S.K. Sønderby, H. Larochelle and O. Winther, Autoencoding beyond pixels using a learned similarity metric

Presenter: Kevin Liang, Presentation

6 January 2017

J. Ho and S. Ermon, Generative Adversarial
Imitation Learning

Presenter: Xinyuan Zhang, Presentation

13 January 2017

R. Houthooft, X. Chen, Yan Duan, J. Schulman, F.D. Turck,
and Pieter Abbeel, Variational
Information Maximizing Exploration

Presenter: Zhao Zhang, Presentation

20 January 2017

A. Bouchard-Côté , S.J. Vollmer and A. Doucet, The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method

A. Pakman, D. Gilboa, D. Carlson and L. Paninski, Stochastic Bouncy Particle Sampler

Presenter: Changyou Chen, Presentation

27 January 2017

Z. Wang, T. Schaul, M. Hessel, H. van Hasselt, M. Lanctot
and N. de Freitas, Dueling Network
Architectures for Deep Reinforcement Learning

Presenter: Zhao Song, Presentation

3 February 2017

Y. Burda, R. Grosse and R. Salakhutdinov, Importance Weighted Autoencoders

Presenter: Zhe Gan, Presentation

17 February 2017

M. Arjovsky, S. Chintala, and L. Bottou, Wasserstein GAN

M. Arjovsky and L. Bottou, Towards
Principled Methods for Training Generative Adversarial Networks

Presenter: Chunyuan Li, Presentation

3 March 2017

K. Chwialkowski, H. Strathmann and A. Gretton, A Kernel Test of Goodness of Fit

Q. Liu, J.D. Lee and M. Jordan, A Kernelized Stein Discrepancy
for Goodness-of-fit Tests

Presenter: David Carlson, Presentation

17 March 2017

Q. Liu and D. Wang, Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm

Presenter: Yunchen Pu, Presentation

24 March 2017

J. Zhao, M. Mathieu and Y. LeCun, Energy-Based Generative Adversarial Network

D. Wang and Q. Liu, Learning to Draw Samples: With Application to Amoritized MLE for Generalized Adversarial Learning

Presenter: Chunyuan Li, Presentation

31 March 2017

L. Yu, W. Zhang, J. Wang, and Y. Yu, SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

Presenter: Zhao Zhang, Presentation

7 April 2017

D.P. Kingma, T. Salimans, R. Jozefowicz, Xi Chen, Ilya Sutskever and M. Welling, Improved Variational Inference with Inverse Autoregressive Flow

Presenter: Dan Salo, Presentation

14 April 2017

D. Bahdanau, K.H. Cho and Y. Bengio, Neural Machine Translation by
Jointly Learning to Align and Translate

Presenter: Xinyuan Zhang, Presentation

21 April 2017

J. Oh, X. Guo, H. Lee, R. L. Lewis, and S. Singh, Action-Conditional Video Prediction Using Deep Networks

Presenter: Kevin Liang, Presentation

28 April 2017

C. Louizos and M. Welling, Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors

Presenter: Zhe Gan, Presentation

12 May 2017

Y. Mroueh, T. Sercu and V. Goel, McGan: Mean and Covariance Feature Matching GAN

Presenter: Dan Salo, Presentation

9 June 2017

V. Dumoulin, I. Belghazi, B. Poole, O. Mastropietro, A. Lamb, M. Arjovsky and A. Courville, Adversarially Learned Inference

J. Donahue, P. Krahenbuhl and T. Darrell, Adversarial Feature Learning

Presenter: Hao Liu, Presentation

23 June 2017

L. Theis and M. Bethge, Generative Image Modeling Using Spatial LSTMs

A. van den Oord, N. Kalchbrenner and K. Kavukcuoglu, Pixel Recurrent Neural
Networks

A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves and K. Kavukcuoglu, Conditional Image Generation with PixelCNN Decoders

Presenter: Qi Wei, Presentation

30 June 2017

M. Jaderberg, K. Simonyan, A. Zisserman and K. Kavukcuoglu, Spatial Transformer Networks

V. Mnih, N. Heess. A. Graves and K. Kavukcuoglu, Recurrent Models of Visual Attention

Presenter: Liqun Chen, Presentation

7 July 2017

L. Mescheder, S. Nowozin and A. Geiger, Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks

Presenter: Paidamoyo Chapfuwa, Presentation

14 July 2017

D. Ulyanov, A. Vedaldi and V. Lempitsky, Adversarial Generator-Encoder Networks

D. Berthelot, T. Schumm and L. Metz, BEGAN: Boundary Equilibrium
Generative Adversarial Networks

Presenter: Ikenna Odinaka, Presentation

21 July 2017

A. Rasmus, H. Valpola, M. Honkala, M. Berglund and T. Raiko,
Semi-Supervised
Learning with Ladder Networks

M. Pezeshk, L. Fan, P. Brakel, A. Courville and Y. Bengio, Deconstructing the Ladder
Network Architecture

C.K. Sønderby T. Raiko, L. Maaløe, S.K. Sønderby and O.
Winther, Ladder
Variational Autoencoders

Presenter: Chenyang Tao, Presentation

11 August 2017

Y. Liu, P. Ramachandran, Q. Liu and J. Peng, Stein Variational Policy Gradient

T. Haarnoja, H. Tang, P. Abbeel and S. Levine, Reinforcement Learning with Deep Energy-Based Policies

Presenter: Rohith Kuditipudi, Presentation

18 August 2017

A. Makhzani and B. Frey, PixelGAN Autoencoders

Presenter: Qi Wei, Presentation

25 August 2017

Z. Hu, Z. Yang, R. Salakhutdinov and E.P. Xing, On Unifying Deep Generative Models

Presenter: Liqun Chen, Presentation

1 September 2017

M. Rosca, B. Lakshminarayanan, D. Warde-Farley and S. Mohamed, Variational Approaches for Auto-Encoding Generative Adversarial Networks

Presenter: Paidamoyo Chapfuwa, Presentation

8 September 2017

B. Dai1, R. Guo, S. Kumar, N. He and L. Song, Stochastic Generative Hashing

Presenter: Ikenna Odinaka, Presentation

15 September 2017

Y. Xia, T. Qin, W. Chen, J. Bian, N. Yu and T.-Y. Liu, Dual Supervised Learning

Presenter: Chunyuan
Li, Presentation

22 September 2017

S. Zhao, J. Song and S. Ermon, Learning Hierarchical Features from Generative Models

Presenter: Zhe Gan, Presentation

29 September
2017

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu
and A.C. Berg, SSD: Single
Shot MultiBox Detector

S. Ren, K. He, R. Girshick and J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks

J. Dai, Y. Li, K. He and J. Sun, R-FCN: Object Detection via Region-based Fully Convolutional Networks

J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama and K. Murphy, Speed/accuracy trade-offs for modern convolutional object detectors

Presenter: Kevin Liang, Presentation

6 October
2017

C.J. Maddison, A. Mnih and Y.W. Teh, The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

E. Jang, S. Gu and B. Poole, Categorical Reparameterization with Gumbel-Softmax

M. Balog, N. Tripuraneni, Z. Ghahramani and A. Weller, Lost Relatives of the Gumbel Trick

Presenter: Yunchen Pu, Presentation

13 October 2017

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomezy, Ł. Kaiser and I. Polosukhin, Attention Is All You Need

B. McCann, J. Bradbury, C. Xiong and R. Socher, Learned in Translation: Contextualized Word Vectors

Presenter: Dinghan Shen, Presentation

20 October 2017

S. Arora, Y. Liang and T. Ma, A Simple But Tough-To-Beat Baseline
For Sentence

J. Wieting, M. Bansal, K. Gimpel and K. Livescu, Towards Universal Paraphrastic
Sentence Embeddings

Presenter: Chunyuan Li, Presentation

27 October 2017

A.P. Parikh, O. Tackstrom, D. Das and J. Uszkoreit, A Decomposable Attention Model
for Natural Language Inference

Presenter: Xinyuan Zhang, Presentation

3 November 2017

R. Arora, T.V. Marinov and P. Mianjy, Stochastic Approximation for Canonical Correlation Analysis

Presenter: Liqun Chen, Presentation

10 November 2017

J. Lu2, A. Kannan, J. Yang, D. Parikh and D. Batra, Best of Both Worlds:
Transferring Knowledge from Discriminative Learning to a Generative Visual
Dialog Model

Presenter: Kevin Liang, Presentation

1 December 2017

Y. Mroueh and T. Sercu, Fisher GAN

C.-L. Li, W.-C. Chang, Y. Cheng, Y. Yang and B. Póczos, MMD GAN: Towards Deeper Understanding of Moment Matching Network

Presenter: Rui-Yi Zhang, Presentation

15 December 2017

R.J. Tibshirani, Dykstra’s Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions

Presenter: Chenyang Tao, Presentation

5 January 2018

V. Nagarajan and J.Z. Kolter, Gradient Descent GAN Optimization is Locally Stable

Presenter: Kevin Liang, Presentation

12 January 2018

V. Badrinarayanan, A. Kendall and R. Cipolla, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

O. Ronneberger, P. Fischer and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation

H. Noh. S. Hong and B. Han, Learning Deconvolution Network for Semantic Segmentation

Presenter: Greg Spell, Presentation

26 January 2018

J. Altosaar, Rajesh Ranganath and D.M. Blei, Proximity Variational Inference

Presenter: Hongteng Xu, Presentation

2 February 2018

L. Mescheder, S. Nowozin and A. Geiger, The Numerics of GANs

Presenter: Dinghan Shen, Presentation

A. Das, S. Kottur, J.M.F. Moura, S. Lee, D. Batra, Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

H. de Vries, F. Strub, S. Chandar, O. Pietquin, H. Larochelle and A. Courville, GuessWhat?! Visual Object Discovery through Multi-Modal Dialogue

Presenter: Rui-Yi Zhang, Presentation

2 March 2018

J. Wang, L. Yu, W. Zhang Y. Gong, Y. Xu, B. Wang, P. Zhang,
D. Zhang, IRGAN: A Minimax Game
for Unifying Generative and Discriminative Information Retrieval Models

K. Lin, D. Li, X. He, Z. Zhang, and M.-T. Sun, Adversarial Ranking for Language Generation

Presenter: Xinyuan Zhang, Presentation

9 March 2018

S. Mohamed and B. Lakshminarayanan, Learning in Implicit Generative Models

Presenter: Dinghan Shen, Presentation

16 March 2018

S. Nowozin, B. Cseke and R. Tomioka, f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization

R. Nock, Z. Cranko, A. K. Menon, L. Qu and R.C. Williamson, f-GANs in an Information
Geometric Nutshell

Presenter: Liqun Chen, Presentation

23 March 2018

R. Kiryo, G. Niu, M.C.D. Plessis, and M. Sugiyama, Positive-Unlabeled Learning with Non-Negative Risk Estimator

Presenter: Kevin Liang, Presentation

30 March 2018

Q. Liu, Stein Variational Gradient Descent as Gradient Flow

Presenter: Chenyang Tao, Presentation

6 April 2018

N.S. Gorbach, S. Bauer and J.M. Buhmann, Scalable
Variational Inference for Dynamical Systems

Presenter: Rui-Yi Zhang, Presentation

13 April 2018

J.-Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A.A. Efros, O. Wang and E. Shechtman, Toward Multimodal Image-to-Image Translation

Presenter: Greg Spell, Presentation

27 April 2018

F. Dutil, C. Gulcehre, A. Trischler and Y. Bengio, Plan, Attend, Generate: Planning for Sequence-to-Sequence Models

Presenter: Xinyuan Zhang, Presentation

30 May 2018

S. Xiao, M. Farajtabar, X. Ye, J. Yan, L. Song and H. Zha, Wasserstein Learning of Deep Generative Point Process Models

Presenter: Johnny Sigman, Presentation

8 June 2018

K. Roth, A. Lucchi, S. Nowozin and T. Hofmann, Stabilizing Training of Generative Adversarial Networks through Regularization

Presenter: Dixin Luo, Presentation

J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A.A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinsk, D. Hassabis, C. Clopat, D. Kumaran, and R. Hadsell, Overcoming catastrophic forgetting in neural networks

F. Zenke, B. Poole and S. Gangul, Continual Learning Through Synaptic Intelligence

H. Ritter, A. Botev and D. Barber, Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

Presenter: Kevin Liang, Presentation

22 June 2018

J. Zhao, L. Xiong, K. Jayashree, J. Li, F. Zhao, Z. Wang, S. Pranata, S. Shen, S. Yan and J. Feng, Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis

Presenter: Greg Spell, Presentation

6 July 2018

K. Konyushkova, S. Raphael and P. Fua, Learning Active Learning from Data

Presenter: Dong Wang, Presentation

20 July 2018

A. Kar, C. Häne and J. Malik, Learning a Multi-View Stereo Machine

Presenter: Tim Dunn, Presentation

27 July 2018

A.M. Alaa and M. van der Schaar, Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes

Presenter: Matthew Engelhard, Presentation

3 August 2018

L.-P. Liu, F.J.R. Ruiz, S. Athey and D.M. Blei, Context Selection for Embedding Models

M.E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee and L. Zettlemoyer, Deep contextualized word representations

Presenter: Kevin Liang, Presentation

22 August 2018

R. Fathony, M. Bashiri and B.D. Ziebart, Adversarial Surrogate Losses for Ordinal Regression

Presenter: Greg Spell, Presentation

31 August 2018

Y. Li, C. Fang, J. Yang, Z. Wang, X. Lu and M.-H. Yang, Universal Style Transfer via Feature Transforms

Presenter: Dong Wang, Presentation

7 September 2018

N. Courty, R. Flamary, A. Habrard and A. Rakotomamonjy, Joint Distribution Optimal Transportation for Domain Adaptation

Presenter: Yulai Cong, Presentation

21 September 2018

A.Volokitin, G. Roig and T. Poggio, Do Deep Neural Networks Suffer from Crowding?

Presenter: Johnny Sigman, Presentation

28 September 2018

K. Greff, S. van Steenkiste and J. Schmidhuber, Neural Expectation Maximization

Presenter: Dixin Luo, Presentation

5 October 2018

H. Mei and J. Eisner, The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process

Presenter: Hongteng Xu, Presentation

Y. Duan, M. Andrychowicz, B. Stadie, J. Ho, J.Schneider, I. Sutskever, P. Abbeel, and W. Zaremba, One-Shot Imitation Learning

Presenter: Wenlin Wang, Presentation

26 October 2018

M. Volkovs, G. Yu and T. Poutanen, DropoutNet: Addressing Cold Start in Recommender Systems

Presenter: Paidamoyo Chapfuwa, Presentation

2 November 2018

E.A. Platanios, H. Poon, T.M. Mitchell and E. Horvitz, Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach

Presenter: Shyyang Dai, Presentation

R. Singh, J. Lanchantin, A. Sekhon and Y. Qi, Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin

Presenter: Yan Zhao, Presentation

16 November 2018

M. Hauser and A. Ray, Principles of Riemannian Geometry in Neural Networks

Presenter: Chenyang Tao, Presentation

30 November 2018

M. Nickel and D. Kiela, Poincaré Embeddings for Learning Hierarchical Representations

Presenter: Ke Bai, Presentation

7 December 2019

S. Bai, J.Z. Kolter and V. Koltun, An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Presenter: Rachel Draelos, Presentation

21 December 2018

K. Greenewald, S. Park, S. Zhou and A. Giessing, Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

Presenter: Yitong Li, Presentation

4 January 2019

C.A. Metzler, A. Mousavi and R.G. Baraniuk, Learned D-AMP: Principled Neural Network Based Compressive Image Recovery

Presenter: Guoyin Wang, Presentation

A. Bietti and J. Mairal, Invariance and Stability of Deep Convolutional Representations

Presenter: Liqun Chen, Presentation

M. Yin and M. Zhou, Semi-Implicit Variational Inference

Presenter: Xinyuan Zhang, Presentation

1 February 2019

M.I. Belghazi, A. Baratin, S. Rajeswar, S. Ozair, Y. Bengio, A. Courville and R.D. Hjelm, Mutual Information Neural Estimation

Presenter: Kevin Liang, Presentation

15 February 2019

K. Guu, T.B. Hashimoto, Y. Oren and P. Liang, Generating Sentences by Editing Prototypes

J. Li, R. Jia, H. He and P. Liang, Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer

J. Weston, E. Dinan and A.H. Miller, Retrieve and Refine: Improved Sequence Generation Models For Dialogue

Presenter: Shuyang Dai, Presentation

22 February 2019

A. Radford, K. Narasimhan, T. Salimans and I. Sutskever, Improving Language Understanding by Generative Pre-Training

J. Devlin. M.-W. Chang, K. Lee and K. Toutanova, BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Presenter: Dixin Luo, Presentation

1 March 2019

M.A. Erdogdu, L. Mackey and O. Shamir, Global Non-convex Optimization with Discretized Diffusions

Presenter: Chenyang Tao, Presentation

8 March 2019

J. Adler and S. Lunz, Banach Wasserstein GAN

Presenter: Guoyin Wang, Presentation

15 March 2019

C. Tallec and Y. Ollivier, Can Recurrent Neural Networks Warp Time?

Presenter: Shuyang Dai, Presentation

29 March 2019

C. Louizos, K. Ullrich and M. Welling, Bayesian Compression for Deep Learning

C. Louizos, M. Welling and D.P. Kingma, Learning Sparse Neural Networks Through L0 Regularization

W. Wen, C. Wu, Y. Wang, Y. Chen and H. Li, Learning Structured Sparsity in Deep Neural Networks

Presenter: Liqun Chen, Presentation

5 April 2019

D. Liang, R.G. Krishnan, M.D. Hoffman and T. Jebara, Variational Autoencoders for Collaborative Filtering

Presenter: Wenlin Wang, Presentation

12 April 2019

Z. Dai, Z. Yang, Y. Yang, W.W. Cohen, J. Carbonell, Q.V. Le, R. Salakhutdinov,

Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context

Presenter: Qian Yang, Presentation

19 April 2019

R. Kiros, Y. Zhu, R. Salakhutdinov, R.S. Zemel, A. Torralba, R. Urtasun and S. Fidler, Skip-Thought Vectors

D. Cera, Y. Yanga, S.-y. Konga, N. Huaa, N. Limtiacob, R. St. Johna, N. Constanta, M. Guajardo-Cespedesa, S. Yuanc, C. Tara, Y.-H. Sunga, B. Stropea, and R. Kurzweila, Universal Sentence Encoder

Presenter: Serge Assaad, Presentation

26 April 2019

H. Shah and D. Barber, Generative Neural Machine Translation

Presenter: Xinyuan Zhang, Presentation

3 May 2019

H. Chen, S. Huang, D. Chiang, J. Chen, Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder

Presenter: Kevin Liang, Presentation

10 May 2019

T. Zhao, R. Zhao and M. Eskenazi, Learning Discourse-level
Diversity for Neural Dialog Models using Conditional Variational Autoencoders

Presenter: Yitong Li, Presentation

17 May 2019

D. Sussillo, R. Jozefowicz, L.F. Abbott, and C. Pandarinath, LFADS - Latent Factor Analysis via Dynamical Systems

Presenter: Tim Dunn, Presentation

24 May 2019

I. Golan and R. El-Yaniv, Deep Anomaly Detection Using Geometric Transformations

L. Ruff, R.A. Vandermeulen, N. Gornitz, L. Deecke, S.A. Siddiqui, A. Binder, E. Muller and M. Kloft, Deep One-Class Classification

Presenter: David Dov, Presentation

31 May 2019

E. Insafutdinov and A. Dosovitskiy, Unsupervised Learning of Shape and Pose with Differentiable Point Clouds

Presenter: Dong Wang, Presentation

7 June 2019

D. Bau, J.-Y. Zhu, H. Strobelt, B. Zhou, J.B. Tenenbaum, W.T. Freeman and A. Torralba, GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Presenter: Shounak Datta, Presentation

19 July 2019

T. Karras, S. Laine and T. Aila, A Style-Based Generator Architecture for Generative Adversarial Networks (StyleGAN)

Presenter: Qian Yang, Presentation

26 July 2019

J. Wu, C. Zhang, T. Xue, W.T. Freeman and J.B. Tenenbaum, Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling

Presenter: Shijing Si, Presentation

2 August 2019

K. Saito, K. Watanabe, Y. Ushiku, and T. Harada, Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

Presenter: Nikhil Mehta, Presentation

9 August 2019

H.H. Bui, H. Narui, and S. Ermon, A DIRT-T Approach to Unsupervisde Domain Adaptation

Presenter: JiaChang Liu, Presentation

16 August 2019

M. Dehghani, S. Gouws, O. Vinyals, J. Uszkoreit and Ł. Kaiser, Universal Transformers

Presenter: Rachel Draelos, Presentation

23 August 2019

Y.N. Dauphin, A. Fan, M. Auli and D. Grangier, Language Modeling with Gated Convolutional Networks

A. van den Oord, N. Kalchbrenner, O. Vinyals, L. Espeholt, A. Graves and K. Kavukcuoglu, Conditional Image Generation with PixelCNN Decoders

Presenter: Shounak Datta, Presentation

30 August 2019

E. Grave, A. Joulin, M. Cisse, D. Grangier and H. Jegou, Efficient Softmax Approximation for GPUs

A. Baevski and M. Auli, Adaptive Input Representations for Neural Language Modeling

Presenter: Dong Wang, Presentation

6 September 2019

Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov and Q.V. Le, XLNET: Generalized Autoregressive Pretraining for Language Understanding

Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, RoBERTa: A Robustly Optimized BERT Pretraining Approach

Presenter: Guoyin Wang, Presentation

13 September 2019

M.X. Chen, O. Firat, A. Bapna, M. Johnson, W. Macherey, G. Foster, L. Jones, N. Parmar, M. Schuster and Z. Chen, The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation

Presenter: Dhanasekar Sundararaman, Presentation

27 September 2019

F.D. Johansson D. Sontag and R. Ranganath, Support and Invertibility in Domain-Invariant Representations

Presenter: Hao Fu, Presentation

11 October 2019

D. Berthelot, N. Carlini, I. Goodfellow, A. Oliver, N. and Colin Raffel, MixMatch: A Holistic Approach to Semi-Supervised Learning

Presenter: Kevin Liang, Presentation

18 October 2019

S. Suwajanakorn, N. Snavely, J. Tompson and M. Norouz, Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning

Presenter: Liqun Chen, Presentation

25 October 2019

M. Defferrard, X. Bresson and P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

T.N. Kipf and M. Welling, Semi-Supervised Classification with Graph Convolutional Networks

Presenter: Jiachang Liu, Presentation

1 November 2019

R. Liao, Z. Zhao, R. Urtasun and R.S. Zemel, LanczosNet: Multi-Scale Deep Graph Convolutional Networks

Presenter: Ruiyi Zhang, Presentation

15 November 2019

Z. Zhang, M. Wang, Y. Xiang, Y. Huang and A. Nehorai, RetGK: Graph Kernels based on Return Probabilities of Random Walks

Z. Zhang, Y. Xiang, L. Wu, B. Xue and A. Nehorai, KerGM: Kernelized Graph Matching

Presenter: Shounak Datta, Presentation

22 November 2019

F. Johansson, U. Shalit, and D. Sontag, Learning representations for counterfactual inference

C. Louizos, U. Shalit, J. M Mooij, D. Sontag, R. Zemel, and M. Welling, Causal effect inference with deep latent-variable models

U. Shalit, F.D. Johansson and D. Sontag, Estimating individual treatment effect: generalization bounds and algorithms

Presenter: Serge Assaad, Presentation

J. Frankle and M. Carbin, The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Presenter: Kevin Liang, Presentation

20 December 2019

J. Yoon, J. Jordon and M. van der Schaar, GANITE: Estimation of Individualized Treatment Effects Using Generative Adversarial Nets

A.M. Alaa, M. Weisz, M. van der Schaar, Deep Counterfactual Networks with Propensity-Dropout

Presenter: Shounak Dutta, Presentation

10 January 2020

R.T.Q. Chen, Y. Rubanova, J. Bettencourt and D. Duvenaud, Neural Ordinary Differential Equations

Presenter: Guoyin Wang, Presentation

17 January 2020

Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner, Variational Continual Learning

Presenter: Nikhil Mehta, Presentation

31 January 2020

T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen and T. Aila, Analyzing and Improving the Image Quality of StyleGAN

Presenter: Miaoyun Zhao, Presentation

J. Behrmann, W. Grathwohl, R.T.Q. Chen, D. Duvenaud and J.-H. Jacobsen, Invertible Residual Networks

Presenter: Jiachang Liu, Presentation

28 February 2020

G. Zeng, Y. Chen, B. Cui and S. Yu, Continuous Learning of Context-Dependent Processing in Neural Networks

Presenter: Yulai Cong, Presentation

6 March 2020

L. Mescheder, A. Geiger and S. Nowozin, Which Training Methods for GANs do actually Converge?

Presenter: Pengyu Cheng, Presentation

13 March 2020

E. Mathieu, C.L. Lan, C.J. Maddison, R. Tomioka, Y.W. Teh, Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders

Presenter: Shuyang Dai, Presentation

20 March 2020

R.D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler and Y. Bengio, Learning Deep Representations by Mutual Information Estimation and Maximization

Presenter: Jiachang Liu, Presentation

27 March 2020

Presenter: Serge Assaad, Presentation

3 April 2020

K. Song, X. Tan, T. Qin, J. Lu and T.-Y. Liu, MASS: Masked Sequence to Sequence Pre-training for Language Generation

L. Dong, N. Yang, W. Wang, F. Wei, X. Liu, Y. Wang, J. Gao, M. Zhou and H.-W. Hon, Unified Language Model Pre-training for Natural Language Understanding and Generation

Presenter: Liqun Chen, Presentation

10 April 2020

W. Chan, N. Kitaev, K. Guu, M. Stern and J. Uszkoreit, KERMIT: Generative Insertion-Based Modeling for Sequences

Presenter: Qian Yang, Presentation

17 April 2020

Q. Xie, Z. Dai, E. Hovy, M.-T. Luong and Q.V. Le, Unsupervised Data Augmentation for Consistency Training

X. Wu, S. Lv, L. Zang, J. Han and S. Hu, Conditional BERT Contextual Augmentation

Presenter: Pengyu Cheng, Presentation

24 April 2020

R. Tang, Y. Lu, L. Liu, L. Mou, O. Vechtomova and J. Lin, Distilling Task-Specific Knowledge from BERT into Simple Neural Networks

S. Sun, Y. Cheng, Z. Gan and J. Liu, Patient Knowledge Distillation for BERT Model Compression

Presenter: Hao Zhang, Presentation

1 May 2020

T. Niven and H.-Y. Kao, Probing Neural Network Comprehension of Natural Language Arguments

K. Clark, U. Khandelwal, O. Levy, C.D. Manning, What Does BERT Look At? An Analysis of BERT's Attention

P. Michel, O. Levy and G. Neubig, Are Sixteen Heads Really Better than One?

O. Kovaleva, A. Romanov, A. Rogers and A. Rumshisky, Revealing the Dark Secrets of BERT

Presenter: Ruiyi Zhang, Presentation

8 May 2020

H. Gao and S. Ji, Graph U-Nets

Presenter: Wei Tuo Hao, Presentation

15 May 2020

K. He, H. Fan, Y. Wu, S. Xie and R. Girshick, Momentum Contrast for Unsupervised Visual Representation Learning

Presenter: Jiachang Liu, Presentation

29 May 2020

T. Chen, S. Kornblith, M. Norouzi and G. Hinton, A Simple Framework for Contrastive Learning of Visual Representations

Presenter: David Dov, Presentation

12 June 2020

D. Hendrycks, M. Mazeika, S. Kadavath and D. Song, Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

Presenter: Miaoyun Zhao, Presentation

19 June 2020

X. Nie and S. Wager, Quasi-Oracle Estimation of Heterogeneous Treatment Effects

Presenter: Junya Chen, Presentation

26 June 2020

S. Jetley, N.A. Lord, N. Lee and P.H.S. Torr, Learn to Pay Attention

Presenter: Rachel Draelos, Presentation

3 July 2020

R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh and D. Batra, Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

Presenter: Dong Wang, Presentation

10 July 2020

M. Makar, F.D. Johansson, J.V. Guttag and D. Sontag Estimation of Bounds on Potential Outcomes For Decision Making

Presenter: Chengyang Tao, Presentation

17 July 2020

W. Zhang, T. K. Panum, S. Jha, P. Chalasani and D. Page, CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

Presenter: David Page, Presentation

24 July 2020

J. Miller, S. Milli and M. Hardt, Strategic Classification is Causal Modeling in Disguise

Presenter: Christy Yuan Li, Presentation

31 July 2020

A. Zhang, C. Lyle, S. Sodhani, A. Filos, M. Kwiatkowska, J. Pineau, Y. Gal and D. Precup, Invariant Causal Prediction for Block MDPs

M. Arjovsky, L. Bottou, I. Gulrajani, D. Lopez-Paz, Invariant Risk Minimization

Presenter: Junya Chen, Presentation

7 August 2020

E. Creager, D. Madras, T. Pitassi, and R. Zemel, Causal Modeling for Fairness In Dynamical Systems

Presenter: Yulai Cong, Presentation

14 August 2020

T. Teshima, I. Sato and M. Sugiyama, Few-Shot Domain Adaptation by Causal Mechanism Transfer

Presenter: Chenyang Tao, Presentation

21 August 2020

B. Saeed, S. Panigrahi and C. Uhler, Causal Structure Discovery from Distributions Arising from Mixtures of DAGs

Presenter: Vinay Kumar Verma, Presentation

4 September 2020

S. Tople, A. Sharma and A. Nori, Alleviating Privacy Attacks via Causal Learning

Presenter: Bai Li, Presentation

11 September 2020

Y. Saito and S. Yasui 2, Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models

Presenter: Serge Assaad, Presentation

18 September 2020

N. Kallus, X. Mao, A. Zhou, Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding

Presenter: Junya Chen, Presentation

9 October 2020

N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, End-to-End Object Detection with Transformers

Presenter: Christy Yuan Li, Presentation

16 October 2020

Overview of NeurIPS 2020 Causal Inference Papers

Presenter: Chenyang Tao, Presentation

23 October 2020

F.D. Johansson, N. Kallus, U. Shalit and D. Sontag, Learning Weighted Representations for Generalization Across Designs

Presenter: Serge Assaad, Presentation

30 October 2020

J.-B. Grill, F. Strub, F. Altché, C. Tallec, P.H. Richemond, E. Buchatskaya, C. Doersch, B.A. Pires, Z.D. Guo, M.G. Azar, B, Piot, K. Kavukcuoglu, R, Munos, M. Valko, Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning

Presenter: Yuewei Yang, Presentation

13 November 2020

T. Wang and P. Isola, Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere

Presenter: Shijing Si, Presentation

20 November 2020

A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit and N. Houlsby, An is worth 16x16 words: Transformers for image recognition at scale

Presenter: Christy Li, Presentation

4 December 2020

Y. Bar-Sinaia, S. Hoyer, J. Hickey and M.P. Brenner, Learning data-driven discretizations for partial differential equations

Presenter: Jianyi Zhang, Presentation

11 December 2020

B. Stevens and T. Colonius, FiniteNet: A fully
convolutional LSTM network architecture for time-dependent
partial differential equations

Presenter: Ke Bai, Presentation

18 December 2020

Z. Long, Y. Lu and B. Dong, PDE-NET 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network

Presenter: Jiachang Liu, Presentation

8 January 2021

D. Randle, P. Protopapas and D. Sondak, Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks

Presenter: Jianyi Zhang, Presentation

15 January 2021

I. Schlag, P. Smolensky, R. Fernandez, N. Jojic, J. Schmidhuber, J. Gao, Enhancing the Transformer With Explicit Relational Encoding for Math Problem Solving

Y. Wang X. Liu and S. Shi, Deep Neural Solver for Math Word Problems

D. Saxton, E. Grefenstette, F. Hill and P. Kohli, Analyzing Mathematical Reasoning Abilities of Neural Networks

Presenter: Dhanasekar Sundararaman, Presentation

22 January 2021

M. Raissi, P. Perdikaris and G.E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Presenter: David Dov, Presentation

5 February 2021

A. Radford, J. Wu, R. Child, D. Luan, D. Amodei and I. Sutskever, Language Models are Unsupervised Multitask Learners

T.B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D.M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever and D. Amodei, Language Models are Few-Shot Learners

Presenter: Jiachang Liu, Presentation

12 February 2021

A. Gu, F. Sala, B. Gunel and C. Re, Learning mixed-curvature representation in products of model spaces

Presenter: Ke Bai, Presentation

19 February 2021

J.Z. Liu, Z. Lin, S. Padhy, D. Tran, T. Bedrax-Weiss and B. Lakshminarayanan, Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness

Presenter: Nikhil Mehta, Presentation

26 February 2021

C. Zhang, K. Zhang and Y. Li, A Causal View on Robustness of Neural Networks

Presenter: Serge Assaad, Presentation

5 March 2021

J. Winkens, R. Bunel, A.G. Roy, R. Stanforth, V. Natarajan, J.R. Ledsam, P. MacWilliams, P. Kohli, A. Karthikesalingam, S. Kohl, T. Cemgil, S.M. Ali Eslami and O. Ronneberger, Contrastive Training for Improved Out-of-Distribution Detection

Presenter: Jiachang Liu

12 March 2021

K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Davis, A. Mohiuddin, L. Kaiser, D. Belanger, L. Colwell and A. Weller, Rethinking Attention With Performers

Presenter: Nikhil Mehta

19 March 2021

K. Choromanski, M. Rowland and A. Weller, The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings

Presenter: Pengyu Cheng