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?

Update: Rachitesh Kumar, Balu Sivan and I are organizing a half-day workshop on Swap Regret and Strategic Learning at EC 2025 this year. Hope to see you there!

See here for an earlier tutorial on Strategic Play with Learning Agents organized by Yoav Kolumbus and I at EC 2024.

Publications

• Learning to Play Against Unknown Opponents
  with Eshwar Ram Arunachaleswaran and Natalie Collina. EC 2025.

• Swap Regret and Correlated Equilibria Beyond Normal-Form Games
  with Eshwar Ram Arunachaleswaran, Natalie Collina, Yishay Mansour, Mehryar Mohri, and Balasubramanian Sivan. EC 2025.

• Computing Optimal Regularizers for Online Linear Optimization
  with Khashayar Gatmiry and Stefanie Jegelka. COLT 2025.

• Rate-Preserving Reductions for Blackwell Approachability
  with Christoph Dann, Yishay Mansour, Mehryar Mohri, and Balasubramanian Sivan. COLT 2025.

• Best of Both Worlds: Regret Minimization versus Minimax Play
  with Adrian Müller, Stratis Skoulakis, Luca Viano, and Volkan Cevher. ICML 2025.

• Efficient learning and computation of linear correlated equilibrium in general convex games
  with Constantinos Daskalakis, Gabriele Farina, Maxwell Fishelson and Charilaos Pipis. ALT 2025.

• Full Swap Regret and Discretized Calibration
  with Maxwell Fishelson, Robert Kleinberg, Princewill Okoroafor, Renato Paes Leme, and Yifeng Teng. ALT 2025.

• Convergence of no-swap-regret dynamics in self-play
  with Renato Paes Leme and Georgios Piliouras. NeurIPS 2024.

• Contracting with a learning agent
  with Guru Guruganesh, Yoav Kolumbus, Inbal Talgam-Cohen, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Joshua Wang, and Matthew Weinberg. NeurIPS 2024.

• Pareto-Optimal Algorithms for Learning in Games
  with Eshwar Ram Arunachaleswaran and Natalie Collina. EC 2024.

• Strategically-Robust Learning Algorithms for Bidding in First-Price Auctions
  with Rachitesh Kumar and Balasubramanian Sivan. EC 2024.

• Complex Dynamics in Autobidding Systems
  with Renato Paes Leme, Georgios Piliouras, Kelly Spendlove, and Song Zuo. EC 2024.

• Online Learning with Bounded Recall
  with Kiran Vodrahalli. ICML 2024.

• Adversarial Online Learning with Temporal Feedback Graphs
  with Khashayar Gatmiry. COLT 2024.

• Prior-Free Mechanisms with Welfare Guarantees
  with Guru Guruganesh and Joshua Wang. TheWebConf 2024.

• 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

• High-Dimensional Calibration from Swap Regret
  with Maxwell Fishelson, Noah Golowich, and Mehryar Mohri.

• A New Benchmark for Online Learning with Budget-Balancing Constraints
  with Mark Braverman, Jingyi Liu, Jieming Mao, and Eric Xue.

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

• Bayesian Conversations
  with Renato Paes Leme and Shuran Zheng.