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 Uncertainty |
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 variables |
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 |