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AISTATS 2016 Schedule

Accepted papers

All accepted papers are available here.

Information for presenters

Each presenter has 20 minutes: 16-minute presentation and 4-minute Q&A session.

Registration desk hours

• Sunday May 8: 17:00 to 20:00
• Monday May 9: 7:30 to 13:30
• Tuesday May 10: 7:30 to 13:30
• Wednesday May 11: 7:30 to 10:30

May 9 (Monday)

Time	Schedule
9:00 - 10:00	Invited speaker: Richard Samworth
10:10 - 11:30	Oral Session 1.1: Gaussian processes Session chair: Aki Vehtari GLASSES: Relieving The Myopia Of Bayesian Optimisation Javier Gonzalez, Michael Osborne, Neil Lawrence Optimization as Estimation with Gaussian Processes in Bandit Settings Zi Wang, Bolei Zhou, Stefanie Jegelka, Scalable Gaussian Process Classification via Expectation Propagation Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato Control Functionals for Quasi-Monte Carlo Integration Chris Oates, Mark Girolami
11:30 - 14:00	Poster session 1
14:00 - 15:30	Lunch (on your own)
15:30 - 16:50	Oral Session 1.2: Deep learning and reinforcement learning Session chair: Byron Boots Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation Søren Hauberg, Oren Freifeld, Anders Boesen Lindbo Larsen Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization Changyou Chen, David Carlson, Zhe Gan, Chunyuan Li, Lawrence Carin Stochastic Neural Networks with Monotonic Activation Functions Siamak Ravanbakhsh, Barnabas Poczos, Jeff Schneider, Dale Schuurmans, Russell Greiner Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamics Michael Herman, Tobias Gindele, Jörg Wagner, Felix Schmitt, Wolfram Burgard
16:50 - 17:20	Coffee break
17:20 - 18:40	Oral Session 1.3: Monte Carlo methods for Bayesian inference Session chair: Lawrence Murray K2-ABC: Approximate Bayesian Computation with Kernel Embeddings Mijung Park, Wittawat Jitkrittum, Dino Sejdinovic C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching Daniel Ritchie, Andreas Stuhlmüller, Noah Goodman Parallel Markov Chain Monte Carlo via Spectral Clustering Guillaume Basse, Aaron Smith, Natesh Pillai Provable Bayesian Inference via Particle Mirror Descent Bo Dai, Niao He, Hanjun Dai, Le Song
18:40	Welcome speech from the organizers
19:30	Welcome cocktail

May 10 (Tuesday)

Time	Schedule
9:00 - 10:00	Invited speaker: Kamalika Chaudhuri
10:10 - 11:30	Oral Session 2.1: Graphical models Session chair: Dino Sejdinovic Survey Propagation beyond Constraint Satisfaction Problems Christopher Srinivasa, Siamak Ravanbakhsh, Brendan Frey Tightness of LP Relaxations for Almost Balanced Models Adrian Weller, David Sontag Active Learning Algorithms for Graphical Model Selection Gautamd Dasarathy, Aarti Singh, Maria-Florina Balcan, Jong Park Tight Variational Bounds via Random Projections and I-Projections Lun-Kai Hsu, Tudor Achim, Stefano Ermon
11:30 - 14:00	Poster session 2
14:00 - 15:30	Lunch (on your own)
15:30 - 16:50	Oral Session 2.2: Theory of learning Session chair: Ambuj Tewari Probability Inequalities for Kernel Embeddings in Sampling without Replacement Markus Schneider Nearly optimal classification for semimetrics Lee-Ad Gottlieb, Aryeh Kontorovich, Pinhas Nisnevitch Online learning with noisy side observations Tomáš Kocák, Gergely Neu, Michal Valko Differentially Private Causal Inference Matt Kusner, Yu Sun, Karthik Sridharan, Kilian Weinberger
16:50 - 17:20	Coffee break
17:20 - 18:40	Oral Session 2.3: Matrix and tensor methods Session chair: Yiming Ying Multiresolution Matrix Compression Nedelina Teneva, Pramod Kaushik Mudrakarta, Risi Kondor Low-Rank and Sparse Structure Pursuit via Alternating Minimization Quanquan Gu, Zhaoran Wang, Han Liu Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations Anima Anandkumar, Prateek Jain, Yang Shi, Niranjan Uma Naresh Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization Fanhua Shang, Yuanyuan Liu, James Cheng
19:30	Dinner at Bodegas Gonzalez-Byass. Buses leave at 19:30

May 11 (Wednesday)

Time	Schedule
9:00 - 10:00	Invited speaker: Adam Tauman Kalai
10:10 - 11:30	Oral Session 3.1: Large-scale learning Session chair: Anima Anandkumar Large-Scale Semi-Supervised Learning Using Streaming Approximation Sujith Ravi, Qiming Diao A Convex Surrogate Operator for General Non-Modular Loss Functions Jiaqian Yu, Matthew Blaschko Ordered Weighted l1 Regularized Regression with Strongly Correlated Covariates: Theoretical Aspects Mario Figueiredo, Robert Nowak Scalable geometric density estimation Ye Wang, Antonio Canale, David Dunson
11:30 - 14:00	Poster session 3 and MLSS posters.
14:00 - 15:30	Lunch (on your own)
15:30 - 16:50	Oral Session 3.2: Sensing and Information Session chair: Mladen Kolar DUAL-LOCO: Distributing Statistical Estimation Using Random Projections Christina Heinze, Brian McWilliams, Nicolai Meinshausen Controlling Bias in Adaptive Data Analysis Using Information Theory Daniel Russo, James Zou Unsupervised Ensemble Learning with Dependent Classifiers Ethan Fetaya, Boaz Nadler, Ariel Jaffe, Ting Ting Jiang, Yuval Kluger Learning Sparse Additive Models with Interactions in High Dimensions Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause
16:50 - 17:20	Coffee break
17:20 - 18:40	Oral Session 3.3: Variational methods and point processes Session chair: Iain Murray Early Stopping as Nonparametric Variational Inference David Duvenaud, Dougal Maclaurin, Ryan Adams Efficient Sampling for k-Determinantal Point Processes Chengtao Li, Stefanie Jegelka, Suvrit Sra Universal Models of Multivariate Temporal Point Processes Asela Gunawardana, Chris Meek