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

P. Hoff, Simulation of the matrix Bingham-von Mises-Fisher distribution, with applications to multivariate and relational data

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

Classification

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

Embeddings

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

 

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

Presenter: Liqun Chen

 

 

15 December 2017

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

Presenter: Chenyang Tao

 

 

22 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 Zheng

 

 

29 December 2017

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

Presenter: Dinghan Shen

 

 

5 January 2018

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

Presenter: Kevin Liang

 

12 January 2018

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

Presenter: Dinghan Shen

 

 

19 January 2018

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

Presenter: Hongteng Xu

 

 

26 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

 

 

2 February 2018

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 Zheng

 

 

9 February 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