Conference Schedule

Schedule in PDF format.


Thursday, March 21, 2019

19:00 – 22:00 Reception with appetizers (Streeterville room)

Friday, March 22, 2019

08:45 – 09:00 Opening remarks
09:00 – 11:00 Tutorial 1: Exploration-Exploitation in Reinforcement Learning
Alessandro Lazaric, Matteo Pirotta and Ronan Fruit
11:00 – 11:30 Break
11:30 – 13:00 Session 1: Sequential Learning
11:30 Online Non-Additive Path Learning under Full and Partial Information
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian and Manfred Warmuth
11:48 Dynamic Pricing with Finitely Many Unknown Valuations
Nicolo Cesa-Bianchi, Tommaso Renato Cesari and Vianney Perchet
12:06 Online Influence Maximization with Local Observations
Julia Olkhovskaya, Gergely Neu and Gabor Lugosi
12:24 Competitive ratio vs regret minimization: achieving the best of both worlds
Amit Daniely and Yishay Mansour
12:42 Average-Case Information Complexity of Learning
Ido Nachum and Amir Yehudayoff
13:00 – 14:00 Lunch break
14:00 – 15:00 Plenary talk 1: Why is fair machine learning hard and how can theory help?
Jennifer Wortman Vaughan
15:00 – 15:15 Break
15:15 – 17:03 Session 2: Learning theory I
15:15 Adaptive Exact Learning of Decision Trees from Membership Queries
Nader Bshouty and Catherine Haddad-Zaknoon
15:33 Limit Learning Equivalence Structures
Ekaterina Fokina, Timo Kötzing and Luca San Mauro
15:51 Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning
Brian Bullins, Elad Hazan, Adam Kalai and Roi Livni
16:09 Attribute-efficient learning of monomials over highly-correlated variables
Alexandr Andoni, Rishabh Dudeja, Daniel Hsu and Kiran Vodrahalli
16:27 A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes
Steve Hanneke and Aryeh Kontorovich
16:45 Improved generalization bounds for robust learning
Idan Attias, Aryeh Kontorovich and Yishay Mansour
17:03 – 17:30 Walk to boat trip departure point at 401 N Michigan Ave, Chicago, IL 60611
17:30 – 19:00 Boat trip

Saturday, March 23, 2019

09:00 – 11:00 Tutorial 2: Structured Random Matrices
Ramon van Handel
11:00 – 11:30 Break
11:30 – 13:00 Session 3: Bandits, partial feedback, privacy, fairness
11:30 Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Tor Lattimore and Csaba Szepesvári
11:48 PAC Battling Bandits in the Plackett-Luce Model
Aadirupa Saha and Aditya Gopalan
12:06 Differentially Private Empirical Risk Minimization in Non-interactive Local Model via Polynomial of Inner Product Approximation
Di Wang, Adam Smith and Jinhui Xu
12:24 Old Techniques in Differentially Private Linear Regression
Or Sheffet
12:42 PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review
Ivan Stelmakh, Nihar Shah and Aarti Singh
13:00 – 14:00 Lunch break
14:00 – 15:00 Plenary talk 2: Theory for Representation Learning
Sanjeev Arora
15:00 – 15:15 Break
15:15 – 16:45 Session 4: Optimization
15:15 Two-Player Games for Efficient Non-Convex Constrained Optimization
Andrew Cotter, Heinrich Jiang and Karthik Sridharan
15:33 General parallel optimization without metric
Xuedong Shang, Emilie Kaufmann and Michal Valko
15:51 Online Linear Optimization with Sparsity Constraints
Chi-Jen Lu, Jun-Kun Wang and Shou-De Lin
16:09 Stochastic Nonconvex Optimization with Large Minibatches
Weiran Wang and Nathan Srebro
16:27 A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption
Peter Bartlett, Victor Gabillon and Michal Valko
16:45 – 17:00 Break
17:00 – 18:48 Session 5: Statistics and Learning I
17:00 Interplay of minimax estimation and minimax support recovery under sparsity
Mohamed Ndaoud
17:18 Uniform regret bounds over R^d for the sequential linear regression problem with the square loss
Pierre Gaillard, Sebastien Gerchinovitz, Malo Huard and Gilles Stoltz
17:36 Ising Models with Latent Conditional Gaussian Variables
Frank Nussbaum and Joachim Giesen
17:54 Exploiting geometric structure in mixture proportion estimation with generalised Blanchard-Lee-Scott estimators
Henry Reeve and Ata Kaban
18:12 A minimax near-optimal algorithm for adaptive rejection sampling
Juliette Achdou, Joseph Lam, Alexandra Carpentier and Gilles Blanchard
18:30 An Exponential Efron-Stein Inequality for Lq Stable Learning Rules. The Deleted Estimate Case
Karim Abou-Moustafa and Csaba Szepesvári
18:48 – 19:00 Break
19:00 – 19:30 Business meeting
19:30 – 22:30 Banquet at the conference hotel (Lakeshore East room)

Sunday, March 24, 2019

09:00 – 11:00 Tutorial 3: Computation and the Brain
Christos Papadimitriou
11:00 – 11:30 Break
11:30 – 13:00 Session 6: Learning theory II
11:30 Hardness of Improper One-sided Learning of Conjunctions For All Uniformly Falsifiable CSPs
Alexander Durgin and Brendan Juba
11:48 Optimal Collusion-Free Teaching
David Kirkpatrick, Hans Simon and Sandra Zilles
12:06 Sample Compression for Real-Valued Learners
Steve Hanneke, Aryeh Kontorovich and Menachem Sadigurschi
12:24 On Learning Graphs with Edge-Detecting Queries
Hasan Abasi and Nader Bshouty
12:42 Can Adversarially Robust Learning Leverage Computational Hardness?
Saeed Mahloujifar and Mohammad Mahmoody
13:00 – 14:00 Lunch break
14:00 – 15:30 Session 7: Statistics and Learning II
14:00 Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds
Odalric-Ambrym Maillard
14:18 Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach
Mastane Achab, Anna Korba and Stéphan Clémençon
14:36 Minimax Learning of Ergodic Markov Chains
Geoffrey Wolfer and Aryeh Kontorovich
14:54 A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation
Clayton Scott
15:12 A Tight Excess Risk Bound via a Unified PAC-Bayesian–Rademacher–Shtarkov–MDL Complexity
Peter Grünwald and Nishant Mehta
15:45 – 16:00 Break
16:00 – 18:30 Workshop: When Smaller Sample Sizes Suffice for Learning