Monday, April 29
Time Event Title Authors
7:30am Breakfast
8:15am Organizers Welcome
8:30am Invited Talk The Jeopardy! Challenge and Beyond Erik Brown (IBM)
9:30am Session I: Learning Theory Session Chair: Karthik Sridharan (Univ. of Pennsylvania)
Notable Paper Permutation estimation and minimax rates of identifiability Olivier Collier (IMAGINE-ENPC / CREST-ENSAE); Arnak Dalalyan (IMAGINE-ENPC / CREST-ENSAE)
Oral Further Optimal Regret Bounds for Thompson Sampling  Shipra Agrawal, MSR India; Navin Goyal, MSR India
10:30am Coffee Break
11:00am Session II: Bayesian Inference I Session Chair: Richard Hahn (Univ. of Chicago)
Notable Paper Diagonal Orthant Multinomial Probit Models James Johndrow (Duke); Kristian Lum (Virgina Tech); David Dunson (Duke)
Oral ODE parameter inference using adaptive gradient matching with Gaussian processes  Frank Dondelinger, Biomathematics and Statistics Scotland; Dirk Husmeier, University of Glasgow; Simon Rogers, University of Glasgow; Maurizio Filippone, University of Glasgow
Oral Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian
processes 		 Andrej Aderhold, University of St Andrews; Dirk Husmeier, University of Glasgow; V. Anne Smith, University of St Andrews
12:30pm Afternoon Break On your own. Afternoon off.
5:00pm Session III: Graphical Models Session Chair: David Sontag (NYU)
Notable Paper Distributed Learning of Gaussian Graphical Models via Marginal Likelihoods Zhaoshi Meng (Michigan); Dennis Wei (Michicgan); Ami Wiesel (Hebrew); Alfred Hero III (Michigan)
Oral Computing the M Most Probable Modes of a Graphical Model  Chao Chen, Rutgers University; Vladimir Kolmogorov, IST Austria; Yan Zhu, Rutgers University; Dimitris Metaxas, Rutgers University; Christoph Lampert, IST Austria
Oral Estimating the Partition Function of Graphical Models Using Langevin Importance Sampling  Jianzhu Ma, TTIC; Jian Peng, TTIC; Sheng Wang, TTIC; Jinbo Xu, TTIC
6:30pm Dinner Break On your own.
8:00pm Poster Session I Hors d'oevers and cash bar
List of Posters
Tuesday, April 30
Time Event Title Authors
7:30am Breakfast
8:30am Invited Talk Geometric and Topological Inference Larry Wasserman (CMU)
9:30am Session IV: Probability Session Chair: Stephan Clemencon (Telecom ParisTech)
Notable Paper A unifying representation for a class of dependent random measures Nicholas Foti (Dartmouth); Sinead Williamson (CMU); Daniel Rockmore (Dartmouth); Joseph Futoma (Dartmouth)
Oral Distribution-Free Distribution Regression  Barnabas Poczos, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; Alessandro Rinaldo, Carnegie Mellon University; Larry Wasserman, Carnegie Mellon University
10:30am Coffee Break
11:00am Session V: Sparsity Session Chair: Daryl Pregibon (Google)
Notable Paper Sparse Principal Component Analysis for High Dimensional Multivariate Time Series Fang Han (JHU); Zhaoran Wang (Princeton); Han Liu (Princeton)
Oral Texture Modeling with Convolutional Spike-and-Slab RBMs and Deep Extensions  Heng Luo, Universitˇ de Montrˇal; Pierre Luc Carrier, Universitˇ de Montrˇal; Aaron Courville, Universitˇ de Montrˇal; Yoshua Bengio, Universitˇ de Montrˇal
Oral Detecting Activations over Graphs using Spanning Tree Wavelet Bases James Sharpnack, Carnegie Mellon University; Aarti Singh, Carnegie Mellon University; Akshay Krishnamurthy, CMU
12:30pm Afternoon Break On your own. Afternoon off.
4:00pm Invited Talk Approximative Bayesian Computation (ABC): Computational Advances versus
Inferential Uncertaintyertainties		 Christian Robert (Paris)
5:00pm Session VI: Bayesian Inference II Session Chair: Emily Fox (Univ. of Washington)
Notable Paper Bayesian learning of joint distributions of objects Anjishnu Banerjee (Duke); Jared Murray (Duke); David Dunson (Duke)
Oral Efficient Variational Inference for Gaussian Process Regression Networks  Trung Nguyen, ANU and NICTA; Edwin Bonilla, NICTA and ANU
Oral Structural Expectation Propagation (SEP): Bayesian structure learning for networks
with latent variableses 		 Nevena Lazic, Microsoft Research; Christopher Bishop, Microsoft Research; John Winn, Microsoft Research
6:30pm Dinner Break On your own.
8:00pm Poster Session II Hors d'oevers and cash bar
List of Posters
Wednesday, May 1
Time Event Title Authors
7:30am Breakfast
8:30am Session VII: Efficient Learning and
Inference		 Session Chair: Geoff Gordon (CMU)
Oral Faster Training of Structural SVMs with Diverse M-Best Cutting-Planes  Abner Guzman-Rivera, University of Illinois; Pushmeet Kohli, Microsoft Research Cambridge; Dhruv Batra, Virginia Tech
Oral Dual Decomposition for Joint Discrete-Continuous Optimization  Christopher Zach, Microsoft Research
Oral Nystrom Approximation for Large-Scale Determinantal Processes  Raja Hafiz Affandi, University of Pennsylvania; Emily Fox, University of Washington; Ben Taskar, University of Pennsylvania; Alex Kulesza, University of Michigan
Oral Supervised Sequential Classification Under Budget Constraints  Kirill Trapeznikov, Boston University; Venkatesh Saligrama, Boston University
10:30am Coffee Break
11:00am Session VIII: Learning, Networks and
Causality		 Session Chair: Guillaume Bouchard (Xerox)
Oral Statistical Tests for Contagion in Observational Social Network Studies  Greg Ver Steeg, Information Sciences Institute; Aram Galstyan, Information Sciences Institute, USC
Oral Meta-Transportability of Causal Effects: A Formal Approach  Elias Bareinboim, UCLA; Judea Pearl, UCLA
Oral Localization and Adaptation in Online Learning  Alexander (Sasha) Rakhlin, University of Pennsylvania; Ohad Shamir, Microsoft Research; Karthik Sridharan, University of Pennsylvania
Oral Uncover Topic-Sensitive Information Diffusion Networks  Nan Du, Georgia Inst. of Tech.; Le Song, Georgia Inst. of Tech.; Hyenkyun Woo, Georgia Inst. of Tech.; Hongyuan Zha, Georgia Inst. of Tech.
1:00pm AISTATS ENDS