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 TasksPresenter: 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, Presentation
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, Presentation
19 March 2021
K. Choromanski, M. Rowland and A. Weller, The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings
Presenter: Pengyu Cheng, Presentation
26 March 2021
P. Goyal, M. Caron, B. Lefaudeux, M. Xu, P. Wang, V. Pai, M. Singh, V. Liptchinsky, I. Misra, A. Joulin and P. Bojanowski, Self-supervised Pretraining of Visual Features in the Wild
Presenter: Yuan Li, Presentation
2 April 2021
A. Radford, J. Wook Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger and I. Sutskever, CLIP: Learning Transferable Visual Models from Natural Language Supervision
Presenter: Jiachang Liu, Presentation
9 April 2021
J. Zheng, A. D’Amour and A, Franks, Copula-based Sensitivity
Analysis for Multi Treatment Causal Inference with Unobserved Confounding
Presenter: Serge Assaad, Presentation
16 April 2021
Z. Tan, S. Yeom, M. Fredrikson and A. Talwalkar, Learning Fair Representations for Kernel Models
Presenter: Chenyang Tao, Presentation
23 April 2021
B. Dhingra, M. Zaheer, V. Balachandran, G. Neubig, R. Salakhutdinov and W.W. Cohen, Differentiable reasoning over a virtual knowledge base
Presenter: Dhanasekar Sundararaman, Presentation
30 April 2021
S. Wang, W. Guo, H. Narasimhan, A. Cotter, M. Gupta, and M.I. Jordan, Robust Optimization for Fairness with Noisy Protected Groups
Presenter: Junya Chen, Presentation
7 May 2021
M. Wortsman, V. Ramanujan, R. Liu, A. Kembhavi, M. Rastegari, J. Yosinski and A. Farhadi, Supermasks in Superposition
Presenter: Vinay Verma, Presentation