Programming Committee members
Alekh Agarwal
Sebastien Bubeck
Prateek Jain
Akshay Krishnamurthy
Praneeth Netrapalli
Robert Schapire
Vasilis Syrgkanis
Sponsorship Chair
Microsoft attendees
John Langford
Ilya Razenshteyn
Robert Schapire
Chicheng Zhang
Akshay Krishnamurthy
Neeraj Kayal
Prateek Jain
Jerry Li
Abhishek Shetty
Alex Slivkins
Accepted papers
Disagreement-Based Combinatorial Pure Exploration: Sample Complexity Bounds and an Efficient Algorithm
Tongyi Cao, Akshay Krishnamurthy
Improved Path-length Regret Bounds for Bandits
Sébastien Bubeck, Yuanzhi Li, Haipeng Luo, Chen-Yu Wei
Privately Learning High-Dimensional Distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan Ullman
Making the Last Iterate of SGD Information Theoretically Optimal
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Near-optimal method for highly smooth convex optimization
Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford
Learning to Prune: Speeding up Repeated Computations
Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik
Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins, Chicheng Zhang
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
On Mean Estimation for General Norms with Statistical Queries
Jerry Li, Aleksandar Nikolov, Ilya Razenshteyn, Erik Waingarten
Statistical Learning with a Nuisance Component
Dylan Foster, Vasilis Syrgkanis
How Hard is Robust Mean Estimation?
Samuel B. Hopkins, Jerry Li