Workshops

Fall 2022

Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

Sep 12, 2022 to Sep 16, 2022

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All talks are listed in Pacific Time.

Monday, September 12th, 2022
8:45 am–9:10 am		Coffee and Check-In
9:10 am–9:15 am		Opening Remarks
9:15 am–10:00 am		Dynamically Aggregating Diverse Information Annie Liang (Northwestern University)
10:00 am–10:45 am		Retrospective Search: Exploration and Ambition on Uncharted Terrain Can Urgun (Princeton University)
10:45 am–11:15 am		Break
11:15 am–12:00 pm		Expert Advice in Complex Environments Steve Callander (Stanford University)
12:00 pm–2:00 pm		Lunch
2:00 pm–2:45 pm		Greedy Approximation Algorithms for Active Sequential Hypothesis Testing Kyra Gan (Harvard University)
2:45 pm–3:30 pm		Causal Matrix Completion: Applications to Offline Causal Reinforcement Learning Anish Agarwal (MIT)
3:30 pm–4:00 pm		Break
4:00 pm–4:45 pm		Inference and Interference in Marketplace Experimentation Hannah Li (Stanford)
4:45 pm–5:45 pm		Reception

Tuesday, September 13th, 2022
8:45 am–9:15 am		Coffee and Check-In
9:15 am–10:00 am		Markovian Interference and the Differences in Q’s Estimator Vivek Farias (Massachusetts Institute of Technology)
10:00 am–10:45 am		Causal Inference in Complex Systems: Network Interference, Strategic Agents, and Beyond Panos Toulis (University of Chicago)
10:45 am–11:15 am		Break
11:15 am–12:00 pm		Contextual Inverse Optimization: Offline and Online Learning Ilan Lobel (NYU Stern)
12:00 pm–2:00 pm		Lunch
2:00 pm–2:45 pm		Corruption-Robust Contextual Search Chara Podimata (UC Berkeley)
2:45 pm–3:30 pm		Best of Both World Algorithms from I.I.D. to Adversarial Data Julian Zimmert (Google Research)
3:30 pm–4:00 pm		Break
4:00 pm–4:45 pm		Best Of Both Worlds: Stochastic & Adversarial Best-Arm Identification Victor Gabillon (Queensland University of Technology)

Wednesday, September 14th, 2022
8:45 am–9:15 am		Coffee and Check-In
9:15 am–10:00 pm		Flow Time Scheduling with Uncertain Processing Time Yossi Azar (Tel-Aviv University)
10:00 am–10:45 am		Machine Learning for Faster Optimization Ben Moseley (Carnegie Mellon University)
10:45 am–11:15 am		Break
11:15 am–12:00 pm		Parsimonious Learning-Augmented Algorithms Ravi Kumar (Google)
12:00 pm–12:10 pm		Poster Session 1 Preview (In Person Only)
12:10 pm–1:40 pm		Lunch
1:40 pm–2:40 pm		Poster Session 1 (In Person Only)
2:40 pm–2:50 pm		Break
2:50 pm–3:50 pm		Panel (In Person Only)
3:50 pm–4:00 pm		Poster Session 2 Preview (In Person Only)
4:00 pm–4:10 pm		Break
4:10 pm–5:10 pm		Poster Session 2 (In Person Only)

Thursday, September 15th, 2022
8:45 am–9:15 am		Coffee and Check-In
9:15 am–10:00 am		Markov Persuasion Process and its Reinforcement Learning Haifeng Xu (University of Chicago)
10:00 am–10:45 am		Attributes: Selective Learning and Influence Arjada Bardhi (Duke University)
10:45 am–11:15 am		Break
11:15 am–12:00 pm		Incentivized Exploration Alex Slivkins (Microsoft Research)
12:00 pm–2:00 pm		Lunch
2:00 pm–2:45 pm		Information Collection Through Strategic Agents Marco Ottaviani (Bocconi University)
2:45 pm–3:30 pm		Adaptive Monopoly Regulation Modibo Camara (Northwestern University)
3:30 pm–4:00 pm		Break
4:00 pm–4:45 pm		Oracle-Efficient Online Learning or: How to Use Non-Robust Optimization for Robust Learning Nika Haghtalab (UC Berkeley)

Friday, September 16th, 2022
8:45 am–9:15 am		Coffee and Check-In
9:15 am–10:00 am		Dynamic Regret Minimization for Bandits without Prior Knowledge Chen-Yu Wei (University of Southern California)
10:00 am–10:45 am		When Can We Use Weak Function Approximation to Solve Large Scale Planning Problems in MDPs? Csaba Szepesvári (University of Alberta, Google DeepMind)
10:45 am–11:15 am		Break
11:15 am–12:00 pm		Adaptivity and Confounding in Multi-armed Bandit Experiments Daniel Russo (Columbia University)
12:00 pm–2:00 pm		Lunch
2:00 pm–2:45 pm		Designing Experiments: Pure Exploration for Contextual Multi-armed Bandits Emma Brunskill (Stanford University)
2:45 pm–3:30 pm		Generalization and Robustness in Offline Reinforcement Learning Wen Sun (Cornell University)