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AI & Statistics 2017

AISTATS 2017 Program of Events

Best Paper Awards

A Sub-Quadratic Exact Medoid Algorithm
James Newling, Francois Fleuret

Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
Sohail Bahmani, Justin Romberg

Reparameterization Gradients through Acceptance- Rejection Sampling Algorithms
Christian Naesseth, Francisco Ruiz, Scott Linderman, David Blei

19-Apr (Wed)

18:30-20:30 Registration Desk

20-Apr (Thu)

7:30-8:50 Breakfast, Windows on the Green & Chart Room

8-10 Registration Desk

8:50-9pm Welcome and award announcement

9:00-10:00 Invited Talk: Csaba Szepesvari. Crystal Ballroom 1, 2
Stochastic linear bandits. See abstract. See slides.

10:00-10:30 Coffee Break, Crystal Atrium

10:30-12:10 Online Learning, Crystal Ballroom 1, 2
Session Chair: Csaba Szepesvari
63 Linear Thompson Sampling Revisited
217 Horde of Bandits using Gaussian Markov Random Fields
225 The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits
304 Improved Strongly Adaptive Online Learning using Coin Betting

12:10-2:00 Lunch on your own

1:00-3:00 Registration Desk

2:00-3:40 Nonparametric methods, Crystal Ballroom 1, 2
Session Chair: Byron Boots
97 Poisson intensity estimation with reproducing kernels
249 Attributing Hacks
248 Regression Uncertainty on the Grassmannian
401 Modal-set estimation with an application to clustering

3:40-4:10 Coffee break, Crystal Atrium

4:10-7:00 Poster Session (with light snacks), Crystal Ballroom 3, 4
See poster list.

21-Apr (Fri)

7:30-9:00 Breakfast, Windows on the Green & Chart Room

8-10 Registration Desk

9:00-10:00 Invited Talk, Cynthia Rudin, Crystal Ballroom 1, 2
What Are We Afraid Of?: Computational Hardness vs the Holy Grail of Interpretability in Machine Learning. See abstract. See slides.

10:00-10:30 Coffee Break, Crystal Atrium

10:30-12:10 Theory, Crystal Ballroom 1, 2
Session Chair: Sanjoy Dasgupta
94 Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
68 A Sub-Quadratic Exact Medoid Algorithm
456 On the Interpretability of Conditional Probability Estimates in the Agnostic Setting
209 Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers

12:10-2:00 Lunch on your own

1:00-3:00 Registration Desk

2:00-3:40 Approximate Inference and MCMC, Crystal Ballroom 1, 2
Session Chair: Simon Lacoste-Julien
51 Annular Augmentation Sampling
101 Removing Phase Transitions from Gibbs Measures
170 Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
174 Asymptotically exact inference in differentiable generative models

3:40-4:10 Coffee Break, Crystal Atrium

4:10-7:00 Poster Session (with light snacks), Crystal Ballroom 3, 4
See poster list.

7:15-9:00 Dinner Buffet, Panorama Ballroom

22-Apr (Sat)

7:30-9:00 Breakfast, Panorama Ballroom C, D & Terrace

8-10 Registration Desk

9:00-10:00 Invited Talk: Sanjoy Dasgupta. Panorama Ballroom A, B
Towards a Theory of Interactive Learning. See abstract. See slides.

10:00-10:30 Coffee Break, Panorama Foyer

10:30-12:10 Bayesian Methods, Panorama Ballroom A, B
Session Chair: Rebecca Steorts
420 Signal-based Bayesian Seismic Monitoring
180 Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
82 Near-optimal Bayesian Active Learning with Correlated and Noisy Tests
298 On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior

12:10-1:30 Lunch on your own (note shorter lunch)

1:30-3:10 Large-scale learning, Panorama Ballroom A, B
Session Chair: Pradeep Ravikumar
417 Communication-Efficient Learning of Deep Networks from Decentralized Data
520 Automated Inference with Adaptive Batches
224 Adaptive ADMM with Spectral Penalty Parameter Selection
372 Identifying groups of strongly correlated variables through Smoothed Ordered Weighted L_1-norms

3:10-3:40 Coffee Break Panorama Foyer

3:40-5:20 Sketching, Panorama Ballroom A, B
Session Chair: Anastasios (Tasos) Kyrillidis
384 Sketching Meets Random Projection in the Dual: A Provable Recovery Algorithm for Big and High-dimensional Data
399 Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
192 Co-Occurring Directions Sketching for Approximate Matrix Multiply
117 Random Consensus Robust PCA