• From Large to Small Datasets: Size Generalization for Clustering Algorithm Selection
    Vaggos Chatziafratis, Ishani Karmarkar, and Ellen Vitercik
    Preprint
    [paper] [slides] [video]

  • Bandit Profit Maximization for Targeted Marketing
    Joon Suk Huh, Ellen Vitercik, Kirthevasan Kandasamy
    Preprint
    [paper]

  • New Sequence-Independent Lifting Techniques for Cutting Planes and When They Induce Facets
    Siddharth Prasad, Ellen Vitercik, Maria-Florina Balcan, and Tuomas Sandholm
    Preprint
    [paper]

  • Sorting from Crowdsourced Comparisons using Expert Verifications
    Ellen Vitercik, Manolis Zampetakis, and David Zhang
    Preprint
    [paper]

  • Algorithmic Contract Design for Crowdsourced Ranking
    Kiriaki Frangias, Andrew Lin, Ellen Vitercik, and Manolis Zampetakis
    Preprint
    [paper]

  • Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization
    Maria-Florina Balcan, Travis Dick, Tuomas Sandholm, and Ellen Vitercik
    Journal of the ACM (JACM), 2024
    Supersedes the ICML’20 and ICML’18 papers below
    [paper]

  • Generalization Guarantees for Multi-Item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms
    Maria-Florina Balcan, Tuomas Sandholm, and Ellen Vitercik
    To appear in Operations Research (OR)
    Supersedes the EC’18 paper below
    [paper]

  • Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
    Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, and Ellen Vitercik
    ACM Conference on Economics and Computation (EC) 2023
    🏆 Exemplary Artificial Intelligence Track Paper Award (EC 2023)
    [paper] [slides] [video]

  • Disincentivizing Polarization in Social Networks
    Christian Borgs, Jennifer Chayes, Christian Ikeokwu, and Ellen Vitercik
    ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) 2023
    [paper]

  • Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts
    Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik
    Conference on Neural Information Processing Systems (NeurIPS) 2022
    [paper]

  • No-Regret Learning in Partially-Informed Auctions
    Wenshuo Guo, Michael I. Jordan, and Ellen Vitercik
    International Conference on Machine Learning (ICML) 2022
    [paper]

  • Improved Sample Complexity Bounds for Branch-and-Cut
    Maria-Florina Balcan, Siddharth Prasad, Tuomas Sandholm, and Ellen Vitercik
    International Conference on Principles and Practice of Constraint Programming (CP) 2022
    [paper]

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

  • Revenue Maximization via Machine Learning with Noisy Data
    Ellen Vitercik and 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
    Maria-Florina Balcan, Dan DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, and Ellen Vitercik
    ACM Symposium on Theory of Computing (STOC) 2021
    [STOC] [arXiv] [slides] [video] [poster]

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

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

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

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

  • Estimating Approximate Incentive Compatibility
    Maria-Florina Balcan, Tuomas Sandholm, and Ellen Vitercik
    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
    Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, and Ellen Vitercik
    Conference on Learning Theory (COLT) 2019
    [paper] [slides] [video] [poster]

  • Algorithmic Greenlining: An Approach to Increase Diversity
    Christian Borgs, Jennifer Chayes, Nika Haghtalab, Adam Tauman Kalai, and Ellen Vitercik
    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
    Maria-Florina Balcan, Travis Dick, and Ellen Vitercik
    IEEE Symposium on Foundations of Computer Science (FOCS) 2018
    [paper] [slides] [poster]

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

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

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

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

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

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