Google Research San Francisco
jschnei@google.com
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About me

Hello! I am a research scientist at Google Research SF 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 organized a half-day workshop on Swap Regret and Strategic Learning at EC 2025.

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

Publications

• Bayesian Conversations
  R. Paes Leme, J. Schneider, H. Shang, and S. Zheng. WINE 2026.

• Swap Regret Minimization Through Response-Based Approachability
  I. Anagnostides, G. Farina, M. Fishelson, H. Luo, and J. Schneider. COLT 2026.

• Near-optimal Swap Regret Minimization for Convex Losses
  L. Hu, J. Schneider, and Y. Wu. COLT 2026.

• Toward Simultaneously Optimal Regret in U-Calibration
  R. Frongillo, H. Luo, N. Mehta, and J. Schneider. COLT 2026.

• Next-Token Prediction and Regret Minimization
  M. Mohri, C. Sanford, J. Schneider, K. Vodrahalli, and Y. Wu. ICML 2026.

• Distributional Alignment Games for Answer-Level Fine-Tuning
  M. Mohri, J. Schneider, and Y. Wu. ICML 2026.

• Combinatorial Optimization using Comparison Oracles
  V. Cohen-Addad, T. d'Orsi, A. Gupta, G. Guruganesh, E. Lee, R. Paes Leme, D. Panigrahi, M. R. Pittu, J. Schneider, and D. P. Woodruff. STOC 2026.

• Efficient Opportunistic Approachability
  T. V. Marinov, M. Mohri, P. Okoroafor, J. Schneider, and J. Zimmert. ALT 2026.

• High-Dimensional Calibration from Swap Regret
  M. Fishelson, N. Golowich, M. Mohri, and J. Schneider. NeurIPS 2025 (Oral).

• Robust Contextual Pricing
  A. Gupta, G. Guruganesh, R. Paes Leme, and J. Schneider. NeurIPS 2025.

• Learning to Play Against Unknown Opponents
  E. R. Arunachaleswaran, N. Collina, and J. Schneider. EC 2025.

• Swap Regret and Correlated Equilibria Beyond Normal-Form Games
  E. R. Arunachaleswaran, N. Collina, Y. Mansour, M. Mohri, J. Schneider, and B. Sivan. EC 2025.
  Best Paper and Best Paper with a Student Lead Author

• Computing Optimal Regularizers for Online Linear Optimization
  K. Gatmiry, J. Schneider, and S. Jegelka. COLT 2025.

• Rate-Preserving Reductions for Blackwell Approachability
  C. Dann, Y. Mansour, M. Mohri, J. Schneider, and B. Sivan. COLT 2025.

• Best of Both Worlds: Regret Minimization versus Minimax Play
  A. Müller, J. Schneider, S. Skoulakis, L. Viano, and V. Cevher. ICML 2025.

• Efficient learning and computation of linear correlated equilibrium in general convex games
  C. Daskalakis, G. Farina, M. Fishelson, C. Pipis, and J. Schneider. STOC 2025.

• Full Swap Regret and Discretized Calibration
  M. Fishelson, R. Kleinberg, P. Okoroafor, R. Paes Leme, J. Schneider, and Y. Teng. ALT 2025.

• Convergence of no-swap-regret dynamics in self-play
  R. Paes Leme, G. Piliouras, and J. Schneider. NeurIPS 2024.

• Contracting with a learning agent
  G. Guruganesh, Y. Kolumbus, J. Schneider, I. Talgam-Cohen, E.-V. Vlatakis-Gkaragkounis, J. Wang, and S. M. Weinberg. NeurIPS 2024.

• Pareto-Optimal Algorithms for Learning in Games
  E. R. Arunachaleswaran, N. Collina, and J. Schneider. EC 2024.

• Strategically-Robust Learning Algorithms for Bidding in First-Price Auctions
  R. Kumar, J. Schneider, and B. Sivan. EC 2024.

• Complex Dynamics in Autobidding Systems
  R. Paes Leme, G. Piliouras, J. Schneider, K. Spendlove, and S. Zuo. EC 2024.

• Online Learning with Bounded Recall
  J. Schneider and K. Vodrahalli. ICML 2024.

• Adversarial Online Learning with Temporal Feedback Graphs
  K. Gatmiry and J. Schneider. COLT 2024.

• Prior-Free Mechanisms with Welfare Guarantees
  G. Guruganesh, J. Schneider, and J. Wang. TheWebConf 2024.

• Is Learning in Games Good for the Learners?
  W. Brown, J. Schneider, and K. Vodrahalli. NeurIPS 2023 (Spotlight).

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

• Optimal No-Regret Learning for One-Sided Lipschitz Functions
  P. Duetting, G. Guruganesh, J. Schneider, and J. Wang. ICML 2023.

• U-Calibration: Forecasting for an Unknown Agent
  R. Kleinberg, R. Paes Leme, J. Schneider, and Y. Teng. COLT 2023.

• The Power of Menus in Contract Design
  G. Guruganesh, J. Schneider, J. Wang, and J. Zhao. EC 2023.

• Bernoulli Factories for Flow-Based Polytopes
  R. Niazadeh, R. Paes Leme, and J. Schneider. SIAM Journal on Discrete Mathematics (SIDMA).

• Eligibility Mechanisms: Auctions Meet Information Retrieval
  G. Goel, R. Paes Leme, J. Schneider, D. Thompson, and H. Zhang. WWW 2023.

• Multiparameter Bernoulli Factories
  R. Paes Leme and J. Schneider. Annals of Applied Probability.

• Pseudonorm Approachability and Applications to Regret Minimization
  C. Dann, Y. Mansour, M. Mohri, J. Schneider, and B. Sivan. ALT 2023.

• Anonymous Bandits for Multi-User Systems
  H. Esfandiari, V. Mirrokni, and J. Schneider. NeurIPS 2022.

• Strategizing against Learners in Bayesian Games
  Y. Mansour, M. Mohri, J. Schneider, and B. Sivan. COLT 2022.

• Density-Based Algorithms for Corruption-Robust Contextual Search and Convex Optimization
  R. Paes Leme, C. Podimata, and J. Schneider. COLT 2022.

• Contextual Recommendations and Low-Regret Cutting-Plane Algorithms
  S. Gollapudi, G. Guruganesh, K. Kollias, P. Manurangsi, R. Paes Leme, and J. Schneider. NeurIPS 2021.

• Margin-Independent Online Multiclass Learning via Convex Geometry
  G. Guruganesh, A. Liu, J. Schneider, and J. Wang. NeurIPS 2021.

• Variable Decomposition for Prophet Inequalities and Optimal Ordering
  A. Liu, R. Paes Leme, M. Pal, J. Schneider, and B. Sivan. EC 2021.

• Contracts under Moral Hazard and Adverse Selection
  G. Guruganesh, J. Schneider, and J. Wang. EC 2021.

• Prior-free Dynamic Mechanism Design With Limited Liability
  M. Braverman, J. Schneider, and S. M. Weinberg. EC 2021.

• Learning Product Rankings Robust to Fake Users
  N. Golrezaei, V. Manshadi, J. Schneider, and S. Sekar. EC 2021.

• Combinatorial Bernoulli Factories: Matchings, Flows and Other Polytopes
  R. Niazadeh, R. Paes Leme, and J. Schneider. FOCS 2021.

• Reserve Price Optimization for First Price Auctions
  Z. Feng, S. Lahaie, J. Schneider, and J. Ye. ICML 2021.

• Jointly Learning Prices and Product Features
  E. Emamjomeh-Zadeh, R. Paes Leme, J. Schneider, and B. Sivan. IJCAI 2021.

• Optimal Contextual Pricing and Extensions
  A. Liu, R. Paes Leme, and J. Schneider. SODA 2021.

• Myersonian Regression
  A. Liu, R. Paes Leme, and J. Schneider. NeurIPS 2020.

• Costly Zero-Order Oracles
  R. Paes Leme and J. Schneider. COLT 2020.

• Strategizing against no-regret learners
  Y. Deng, J. Schneider, and B. Sivan. NeurIPS 2019 (Oral).

• Prior-free dynamic auctions with low-regret buyers
  Y. Deng, J. Schneider, and B. Sivan. NeurIPS 2019.

• Contextual bandits with cross-learning
  S. Balseiro, N. Golrezaei, M. Mahdian, V. Mirrokni, and J. Schneider. NeurIPS 2019.

• Multi-armed bandit problems with strategic arms
  M. Braverman, J. Mao, J. Schneider, and S. M. Weinberg. COLT 2019.

• The space complexity of mirror games
  S. Garg and J. Schneider. ITCS 2019.

• Contextual pricing for Lipschitz buyers
  J. Mao, R. Paes Leme, and J. Schneider. NeurIPS 2018.

• Contextual search via intrinsic volumes
  R. Paes Leme and J. Schneider. FOCS 2018.

• Selling to a no regret buyer
  M. Braverman, J. Mao, J. Schneider, and S. M. Weinberg. EC 2018.
  Best Full Paper and Best Paper with a Student Lead Author

• Competitive analysis of the top-K ranking problem
  X. Chen, S. Gopi, J. Mao, and J. Schneider. SODA 2018.

• Condorcet-consistent and approximately strategyproof tournament rules
  J. Schneider, A. Schvartzman, and S. M. Weinberg. ITCS 2018.

• Tight space-noise tradeoffs in computing the ergodic measure
  M. Braverman, C. Rojas, and J. Schneider. Sbornik: Mathematics 208.12 (2017): 1758.

• Information complexity is computable
  M. Braverman and J. Schneider. ICALP 2016.

• Space-bounded Church-Turing thesis and computational tractability of closed systems
  M. Braverman, J. Schneider, and C. Rojas. Physical Review Letters 115 (9), 2015.

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

Working papers

• Optimally Pacing Budget Spending and Learning
  M. Braverman, J. Liu, J. Mao, J. Schneider, and E. Xue.

• Note on Finite-Automata Bernoulli Factories for Rational Functions
  R. Paes Leme and J. Schneider.

• Generalized Distributional Alignment Games for Unbiased Answer-Level Fine-Tuning
  M. Mohri, J. Schneider, and Y. Zhong.

• A New Benchmark for Online Learning with Budget-Balancing Constraints
  M. Braverman, J. Liu, J. Mao, J. Schneider, and E. Xue.

• Nonbossy Mechanisms: Mechanism Design Robust to Secondary Goals
  R. Paes Leme, J. Schneider, and H. Zhang.