Balancing Communication for Multi-party Interactive Coding. - Allison B. Lewko; Ellen Vitercik (CoRR, 2015)

Learning Combinatorial Functions from Pairwise Comparisons. - Maria-Florina Balcan; Ellen Vitercik; Colin White (COLT, 2016)

Sample Complexity of Automated Mechanism Design. - Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik (NIPS, 2016)

Unilateral and equitransitive tilings by squares of four sizes. - Casey Mann; Joseph DiNatale; Emily Peirce; Ellen Vitercik (Ars Math. Contemp., 2016)

Learning the best algorithm for max-cut, clustering, and other partitioning problems. - Maria-Florina Balcan; Vaishnavh Nagarajan; Ellen Vitercik; Colin White (CoRR, 2016)

Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems. - Maria-Florina Balcan; Vaishnavh Nagarajan; Ellen Vitercik; Colin White (COLT, 2017)

Sample Complexity of Multi-Item Profit Maximization. - Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik (CoRR, 2017)

Private and Online Optimization of Piecewise Lipschitz Functions. - Maria-Florina Balcan; Travis Dick; Ellen Vitercik (CoRR, 2017)

Dispersion for Data-Driven Algorithm Design, Online Learning, and Private Optimization. - Maria-Florina Balcan; Travis Dick; Ellen Vitercik (FOCS, 2018)

Synchronization Strings: Channel Simulations and Interactive Coding for Insertions and Deletions. - Bernhard Haeupler; Amirbehshad Shahrasbi; Ellen Vitercik (ICALP, 2018)

Learning to Branch. - Maria-Florina Balcan; Travis Dick; Tuomas Sandholm; Ellen Vitercik (ICML, 2018)

A General Theory of Sample Complexity for Multi-Item Profit Maximization. - Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik (EC, 2018)

Algorithmic Greenlining: An Approach to Increase Diversity. - Christian Borgs; Jennifer T. Chayes; Nika Haghtalab; Adam Tauman Kalai; Ellen Vitercik (AIES, 2019)

Learning to Prune: Speeding up Repeated Computations. - Daniel Alabi; Adam Tauman Kalai; Katrina Ligett; Cameron Musco; Christos Tzamos; Ellen Vitercik (COLT, 2019)

Estimating Approximate Incentive Compatibility. - Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik (EC, 2019)

Learning to Optimize Computational Resources: Frugal Training with Generalization Guarantees. - Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik (AAAI, 2020)

Refined bounds for algorithm configuration: The knife-edge of dual class approximability. - Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik (ICML, 2020)

Generalization in Portfolio-Based Algorithm Selection. - Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik (AAAI, 2021)

Private optimization without constraint violations. - Andrés Muñoz Medina; Umar Syed; Sergei Vassilvitskii; Ellen Vitercik (AISTATS, 2021)

Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond. - Maria-Florina Balcan; Siddharth Prasad; Tuomas Sandholm; Ellen Vitercik (NeurIPS, 2021)

Revenue maximization via machine learning with noisy data. - Ellen Vitercik; Tom Yan (NeurIPS, 2021)

How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design. - Maria-Florina Balcan; Dan F. DeBlasio; Travis Dick; Carl Kingsford; Tuomas Sandholm; Ellen Vitercik (STOC, 2021)

Improved Learning Bounds for Branch-and-Cut. - Maria-Florina Balcan; Siddharth Prasad; Tuomas Sandholm; Ellen Vitercik (CoRR, 2021)

Automated Algorithm and Mechanism Configuration. - Ellen Vitercik (2021)

Improved Sample Complexity Bounds for Branch-And-Cut. - Maria-Florina Balcan; Siddharth Prasad; Tuomas Sandholm; Ellen Vitercik (CP, 2022)

No-Regret Learning in Partially-Informed Auctions. - Wenshuo Guo; Michael I. Jordan; Ellen Vitercik (ICML, 2022)

Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts. - Maria-Florina Balcan; Siddharth Prasad; Tuomas Sandholm; Ellen Vitercik (NeurIPS, 2022)

Disincentivizing Polarization in Social Networks. - Christian Borgs; Jennifer T. Chayes; Christian Ikeokwu; Ellen Vitercik (AiOfAi@IJCAI, 2023)

Algorithmic Contract Design for Crowdsourced Ranking. - Kiriaki Frangias; Andrew Lin; Ellen Vitercik; Manolis Zampetakis (CoRR, 2023)

Sorting from Crowdsourced Comparisons using Expert Verifications. - Ellen Vitercik; Manolis Zampetakis; David Zhang (CoRR, 2023)

MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation. - Alexandre Hayderi; Amin Saberi; Ellen Vitercik; Anders Wikum (ICML, 2024)

Bandit Profit-Maximization for Targeted Marketing. - Joon Suk Huh; Ellen Vitercik; Kirthevasan Kandasamy (EC, 2024)

New Sequence-Independent Lifting Techniques for Cutting Planes and When They Induce Facets. - Siddharth Prasad; Ellen Vitercik; Maria-Florina Balcan; Tuomas Sandholm (CoRR, 2024)

From Large to Small Datasets: Size Generalization for Clustering Algorithm Selection. - Vaggos Chatziafratis; Ishani Karmarkar; Ellen Vitercik (CoRR, 2024)

Algorithmic Content Selection and the Impact of User Disengagement. - Emilio Calvano; Nika Haghtalab; Ellen Vitercik; Eric Zhao 003 (CoRR, 2024)

Wait-Less Offline Tuning and Re-solving for Online Decision Making. - Jingruo Sun; Wenzhi Gao; Ellen Vitercik; Yinyu Ye 001 (CoRR, 2024)

Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty. - Wenshuo Guo; Nika Haghtalab; Kirthevasan Kandasamy; Ellen Vitercik (SIGecom Exch., 2024)

Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization. - Maria-Florina Balcan; Travis Dick; Tuomas Sandholm; Ellen Vitercik (J. ACM, 2024)

How Much Data Is Sufficient to Learn High-Performing Algorithms? - Maria-Florina Balcan; Dan F. DeBlasio; Travis Dick; Carl Kingsford; Tuomas Sandholm; Ellen Vitercik (J. ACM, 2024)

LLMs for Cold-Start Cutting Plane Separator Configuration. - Connor Lawless; Yingxi Li; Anders Wikum; Madeleine Udell; Ellen Vitercik (CPAIOR (2), 2025)

Generalization Guarantees for Multi-Item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms. - Maria-Florina Balcan; Tuomas Sandholm; Ellen Vitercik (Oper. Res., 2025)

Algorithms with Calibrated Machine Learning Predictions. - Judy Hanwen Shen; Ellen Vitercik; Anders Wikum (CoRR, 2025)

EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations. - Haotian Zhai; Connor Lawless; Ellen Vitercik; Liu Leqi (CoRR, 2025)

Subsampling Graphs with GNN Performance Guarantees. - Mika Sarkin Jain; Stefanie Jegelka; Ishani Karmarkar; Luana Ruiz; Ellen Vitercik (CoRR, 2025)

Primal-Dual Neural Algorithmic Reasoning. - Yu He; Ellen Vitercik (CoRR, 2025)

DSR-Bench: Evaluating the Structural Reasoning Abilities of LLMs via Data Structures. - Yu He; Yingxi Li; Colin White; Ellen Vitercik (CoRR, 2025)