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Quantifying Uncertainty: Stochastic, Adversarial, and Beyond
Program
Data-Driven Decision Processes
Date
Monday, Sept. 12
–
Friday, Sept. 16, 2022
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The Workshop
Schedule
Videos
All talks are listed in Pacific Time.
Monday, Sept. 12, 2022
8:45
–
9:10 a.m.
Coffee and Check-In
9:10
–
9:15 a.m.
Opening Remarks
9:15
–
10 a.m.
Dynamically Aggregating Diverse Information
Annie Liang (Northwestern University)
Video
10
–
10:45 a.m.
Retrospective Search: Exploration and Ambition on Uncharted Terrain
Can Urgun (Princeton University)
Video
10:45
–
11:15 a.m.
Break
11:15 a.m.
–
12 p.m.
Expert Advice in Complex Environments
Steve Callander (Stanford University)
Video
12
–
2 p.m.
Lunch
2
–
2:45 p.m.
Greedy Approximation Algorithms for Active Sequential Hypothesis Testing
Kyra Gan (Harvard University)
Video
2:45
–
3:30 p.m.
Causal Matrix Completion: Applications to Offline Causal Reinforcement Learning
Anish Agarwal (MIT)
Video
3:30
–
4 p.m.
Break
4
–
4:45 p.m.
Inference and Interference in Marketplace Experimentation
Hannah Li (Stanford)
Video
4:45
–
5:45 p.m.
Reception
Tuesday, Sept. 13, 2022
8:45
–
9:15 a.m.
Coffee and Check-In
9:15
–
10 a.m.
Markovian Interference and the Differences in Q’s Estimator
Vivek Farias (Massachusetts Institute of Technology)
Video
10
–
10:45 a.m.
Causal Inference in Complex Systems: Network Interference, Strategic Agents, and Beyond
Panos Toulis (University of Chicago)
Video
10:45
–
11:15 a.m.
Break
11:15 a.m.
–
12 p.m.
Contextual Inverse Optimization: Offline and Online Learning
Ilan Lobel (NYU Stern)
Video
12
–
2 p.m.
Lunch
2
–
2:45 p.m.
Corruption-Robust Contextual Search
Chara Podimata (UC Berkeley)
Video
2:45
–
3:30 p.m.
Best of Both World Algorithms from I.I.D. to Adversarial Data
Julian Zimmert (Google Research)
Video
3:30
–
4 p.m.
Break
4
–
4:45 p.m.
Best Of Both Worlds: Stochastic & Adversarial Best-Arm Identification
Victor Gabillon (Queensland University of Technology)
Video
Wednesday, Sept. 14, 2022
8:45
–
9:15 a.m.
Coffee and Check-In
9:15 a.m.
–
10 p.m.
Flow Time Scheduling with Uncertain Processing Time
Yossi Azar (Tel-Aviv University)
Video
10
–
10:45 a.m.
Machine Learning for Faster Optimization
Ben Moseley (Carnegie Mellon University)
Video
10:45
–
11:15 a.m.
Break
11:15 a.m.
–
12 p.m.
Parsimonious Learning-Augmented Algorithms
Ravi Kumar (Google)
Video
12
–
12:10 p.m.
Poster Session 1 Preview (In Person Only)
12:10
–
1:40 p.m.
Lunch
1:40
–
2:40 p.m.
Poster Session 1 (In Person Only)
2:40
–
2:50 p.m.
Break
2:50
–
3:50 p.m.
Panel (In Person Only)
3:50
–
4 p.m.
Poster Session 2 Preview (In Person Only)
4
–
4:10 p.m.
Break
4:10
–
5:10 p.m.
Poster Session 2 (In Person Only)
Thursday, Sept. 15, 2022
8:45
–
9:15 a.m.
Coffee and Check-In
9:15
–
10 a.m.
Markov Persuasion Process and its Reinforcement Learning
Haifeng Xu (University of Chicago)
Video
10
–
10:45 a.m.
Attributes: Selective Learning and Influence
Arjada Bardhi (Duke University)
Video
10:45
–
11:15 a.m.
Break
11:15 a.m.
–
12 p.m.
Incentivized Exploration
Alex Slivkins (Microsoft Research)
Video
12
–
2 p.m.
Lunch
2
–
2:45 p.m.
Information Collection Through Strategic Agents
Marco Ottaviani (Bocconi University)
Video
2:45
–
3:30 p.m.
Adaptive Monopoly Regulation
Modibo Camara (Northwestern University)
Video
3:30
–
4 p.m.
Break
4
–
4:45 p.m.
Oracle-Efficient Online Learning or: How to Use Non-Robust Optimization for Robust Learning
Nika Haghtalab (UC Berkeley)
Video
Friday, Sept. 16, 2022
8:45
–
9:15 a.m.
Coffee and Check-In
9:15
–
10 a.m.
Dynamic Regret Minimization for Bandits without Prior Knowledge
Chen-Yu Wei (University of Southern California)
Video
10
–
10:45 a.m.
When Can We Use Weak Function Approximation to Solve Large Scale Planning Problems in MDPs?
Csaba Szepesvári (University of Alberta, Google DeepMind)
Video
10:45
–
11:15 a.m.
Break
11:15 a.m.
–
12 p.m.
Adaptivity and Confounding in Multi-armed Bandit Experiments
Daniel Russo (Columbia University)
Video
12
–
2 p.m.
Lunch
2
–
2:45 p.m.
Designing Experiments: Pure Exploration for Contextual Multi-armed Bandits
Emma Brunskill (Stanford University)
Video
2:45
–
3:30 p.m.
Generalization and Robustness in Offline Reinforcement Learning
Wen Sun (Cornell University)
Video
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