• Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond
    with Maria-Florina Balcan, Siddharth Prasad, and Tuomas Sandholm
    Conference on Neural Information Processing Systems (NeurIPS) 2021
    [paper]

  • Revenue Maximization via Machine Learning with Noisy Data
    with Tom Yan
    Conference on Neural Information Processing Systems (NeurIPS) 2021
    [paper]

  • How Much Data Is Sufficient to Learn High-performing Algorithms? Generalization Guarantees for Data-driven Algorithm Design
    with Maria-Florina Balcan, Dan DeBlasio, Travis Dick, Carl Kingsford, and Tuomas Sandholm
    ACM Symposium on Theory of Computing (STOC) 2021
    [STOC] [arXiv] [slides] [video] [poster]

  • Private Optimization Without Constraint Violations
    with Andrés Muñoz Medina, Umar Syed, and Sergei Vassilvitskii
    International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
    [paper] [slides] [poster]

  • Generalization in Portfolio-based Algorithm Selection
    with Maria-Florina Balcan and Tuomas Sandholm
    AAAI Conference on Artificial Intelligence 2021
    [paper] [slides] [poster]

  • Refined Bounds for Algorithm Configuration: The Knife-edge of Dual Class Approximability
    with Maria-Florina Balcan and Tuomas Sandholm
    International Conference on Machine Learning (ICML) 2020
    [paper] [slides] [video]

  • Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees
    with Maria-Florina Balcan and Tuomas Sandholm
    AAAI Conference on Artificial Intelligence 2020
    [paper] [poster]

  • Estimating Approximate Incentive Compatibility
    with Maria-Florina Balcan and Tuomas Sandholm
    ACM Conference on Economics and Computation (EC) 2019
    🏆 Exemplary Artificial Intelligence Track Paper Award (EC 2019)
    🏆 Best Presentation by a Student or Postdoctoral Researcher (EC 2019)
    [paper] [slides] [video] [poster]

  • Learning to Prune: Speeding up Repeated Computations
    with Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, and Christos Tzamos
    Conference on Learning Theory (COLT) 2019
    [paper] [slides] [video] [poster]

  • Algorithmic Greenlining: An Approach to Increase Diversity
    with Christian Borgs, Jennifer Chayes, Nika Haghtalab, and Adam Tauman Kalai
    AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) 2019
    [paper] [slides] [poster]

  • Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization
    with Maria-Florina Balcan and Travis Dick
    IEEE Symposium on Foundations of Computer Science (FOCS) 2018
    [paper] [slides] [poster]

  • Learning to Branch
    with Maria-Florina Balcan, Travis Dick, and Tuomas Sandholm
    International Conference on Machine Learning (ICML) 2018
    [paper] [slides] [video]

  • A General Theory of Sample Complexity for Multi-Item Profit Maximization
    with Maria-Florina Balcan and Tuomas Sandholm
    ACM Conference on Economics and Computation (EC) 2018
    [paper] [slides] [video]

  • Synchronization Strings: Channel Simulations and Interactive Coding for Insertions and Deletions
    with Bernhard Haeupler and Amirbehshad Shahrasbi
    International Colloquium on Automata, Languages and Programming (ICALP) 2018
    [paper]

  • Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
    with Maria-Florina Balcan, Vaishnavh Nagarajan, and Colin White
    Conference on Learning Theory (COLT) 2017
    [paper] [slides]

  • Sample Complexity of Automated Mechanism Design
    with Maria-Florina Balcan and Tuomas Sandholm
    Conference on Neural Information Processing Systems (NeurIPS) 2016
    [paper] [slides] [video]

  • Learning Combinatorial Functions from Pairwise Comparisons
    with Maria-Florina Balcan and Colin White
    Conference on Learning Theory (COLT) 2016
    [paper]