Google Research New York
111 8th Ave, New York, NY 10011
jschnei@google.com
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About me

Hello! I am a research scientist at Google Research NYC in the Algorithms and Optimization group. Before this, I completed my PhD in Computer Science at Princeton University under the supervision of Prof. Mark Braverman.

My current research interests include online learning, game theory, contract design, and convex geometry/optimization. I am especially interested in questions in the overlap between game theory and online learning: what learning algorithms should you play in repeated games? What is your best response if you know your opponent is running (a specific class of) learning algorithm?

Publications

• Is Learning in Games Good for the Learners?
  with William Brown and Kiran Vodrahalli. NeurIPS 2023 (Spotlight).

• Optimal cross-learning for contextual bandits with unknown context distributions
  with Julian Zimmert. NeurIPS 2023.

• Optimal No-Regret Learning for One-Sided Lipschitz Functions
  with Paul Duetting, Guru Guruganesh, and Joshua Wang. ICML 2023.

• U-Calibration: Forecasting for an Unknown Agent
  with Robert Kleinberg, Renato Paes Leme, and Yifeng Teng. COLT 2023.

• The Power of Menus in Contract Design
  with Guru Guruganesh, Joshua Wang, and Junyao Zhao. EC 2023.

• Bernoulli Factories for Flow-Based Polytopes
  with Rad Niazadeh and Renato Paes Leme. SIAM Journal on Discrete Mathematics (SIDMA).

• Eligibility Mechanisms: Auctions Meet Information Retrieval
  with Gagan Goel, Renato Paes Leme, David Thompson, and Hanrui Zhang. WWW 2023.

• Multiparameter Bernoulli Factories
  with Renato Paes Leme. Annals of Applied Probability.

• Pseudonorm Approachability and Applications to Regret Minimization
  with Christoph Dann, Yishay Mansour, Mehryar Mohri, and Balasubramanian Sivan. ALT 2023.

• Anonymous Bandits for Multi-User Systems
  with Hossein Esfandiari and Vahab Mirrokni. NeurIPS 2022.

• Strategizing against Learners in Bayesian Games
  with Yishay Mansour, Mehryar Mohri, and Balasubramanian Sivan. COLT 2022.

• Corruption-Robust Contextual Search through Density Updates
  with Renato Paes Leme and Chara Podimata. COLT 2022.

• Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
  with Sreenivas Gollapudi, Guru Guruganesh, Kostas Kollias, Pasin Manurangsi, and Renato Paes Leme. NeurIPS 2021.

• Margin-Independent Online Multiclass Learning via Convex Geometry
  with Guru Guruganesh, Allen Liu, and Joshua Wang. NeurIPS 2021.

• Competing Optimally Against An Imperfect Prophet
  with Allen Liu, Renato Paes Leme, Martin Pal, and Balasubramanian Sivan. EC 2021.

• Contracts under Moral Hazard and Adverse Selection
  with Guru Guruganesh and Joshua Wang. EC 2021.

• Prior-free Dynamic Mechanism Design With Limited Liability
  with Mark Braverman and Matthew Weinberg. EC 2021.

• Learning Product Rankings Robust to Fake Users
  with Negin Golrezaei, Vahideh Manshadi, and Shreyas Sekar. EC 2021.

• Combinatorial Bernoulli Factories: Matchings, Flows and Other Polytopes
  with Rad Niazadeh and Renato Paes Leme. FOCS 2021.

• Reserve Price Optimization for First Price Auctions
  with Zhe Feng, Sebastien Lahaie, and Jinchao Ye. ICML 2021.

• Jointly Learning Prices and Product Features
  with Ehsan Emamjomeh-Zadeh, Renato Paes Leme, and Balasubramanian Sivan. IJCAI 2021.

• Optimal Contextual Pricing and Extensions
  with Allen Liu and Renato Paes Leme. SODA 2021.

• Myersonian Regression
  with Allen Liu and Renato Paes Leme. NeurIPS 2020.

• Costly Zero-Order Oracles
  with Renato Paes Leme. COLT 2020.

• Strategizing against no-regret learners
  with Yuan Deng and Balasubramanian Sivan. NeurIPS 2019 (Oral).

• Prior-free dynamic auctions with low-regret buyers
  with Yuan Deng and Balasubramanian Sivan. NeurIPS 2019.

• Contextual bandits with cross-learning
  with Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni. NeurIPS 2019.

• Multi-armed bandit problems with strategic arms
  with Mark Braverman, Jieming Mao, and Matthew Weinberg. COLT 2019.

• The space complexity of mirror games
  with Sumegha Garg. ITCS 2019.

• Contextual pricing for Lipschitz buyers
  with Jieming Mao and Renato Paes Leme. NeurIPS 2018.

• Contextual search via intrinsic volumes
  with Renato Paes Leme. FOCS 2018.

• Selling to a no regret buyer
  with Mark Braverman, Jieming Mao, and Matthew Weinberg. EC 2018.
  Best Full Paper and Best Paper with a Student Lead Author

• Competitive analysis of the top-K ranking problem
  with Xi Chen, Sivakanth Gopi, and Jieming Mao. SODA 2018.

• Condorcet-consistent and approximately strategyproof tournament rules
  with Ariel Schvartzman and Matthew Weinberg. ITCS 2018.

• Tight space-noise tradeoffs in computing the ergodic measure
  with Mark Braverman and Cristobal Rojas. Sbornik: Mathematics 208.12 (2017): 1758.

• Information complexity is computable
  with Mark Braverman. ICALP 2016.

• Space-bounded Church-Turing thesis and computational tractability of closed systems
  with Mark Braverman and Cristobal Rojas. Physical Review Letters 115 (9), 2015.

• Polynomial sequences of binomial-type arising in graph theory
  Electronic Journal of Combinatorics, 21 (1), 2014.

Working papers

• Is Learning in Games Good for the Learners?
  with William Brown and Kiran Vodrahalli.

• Nonbossy Mechanisms: Mechanism Design Robust to Secondary Goals
  with Renato Paes Leme and Hanrui Zhang.

• Bayesian Conversations
  with Renato Paes Leme and Shuran Zhang.

• History-Restricted Online Learning
  with Kiran Vodrahalli.