Academic Appointments


2025-26 Courses


Stanford Advisees


All Publications


  • Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty OPERATIONS RESEARCH Guo, W., Haghtalab, N., Kandasamy, K., Vitercik, E. 2026
  • Smoothed Analysis of Online Metric Matching with a Single Sample: Beyond Metric Distortion Li, Y., Vitercik, E., Yang, M. edited by Saraf, S. SCHLOSS DAGSTUHL, LEIBNIZ CENTER INFORMATICS. 2026
  • LLMs for Cold-Start Cutting Plane Separator Configuration Lawless, C., Li, Y., Wikum, A., Udell, M., Vitercik, E. edited by Tack, G. SPRINGER INTERNATIONAL PUBLISHING AG. 2025: 51-69
  • New Sequence-Independent Lifting Techniques for Cover Inequalities and When They Induce Facets Prasad, S., Vitercik, E., Balcan, M., Sandholm, T. edited by Kwok, J. IJCAI-INT JOINT CONF ARTIF INTELL. 2025: 2675-2683
  • EquivaMap: Leveraging LLMs for Automatic Equivalence Checking of Optimization Formulations Zhai, H., Lawless, C., Vitercik, E., Liu Leqi edited by Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., Zhu, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025: 74288-74305
  • Primal-Dual Neural Algorithmic Reasoning He, Y., Vitercik, E. edited by Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., Zhu, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025: 22743-22766
  • Algorithms with Calibrated Machine Learning Predictions Shen, J., Vitercik, E., Wikum, A. edited by Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., Zhu, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025: 54476-54498
  • Wait-Less Offline Tuning and Re-solving for Online Decision Making Sun, J., Gao, W., Vitercik, E., Ye, Y. edited by Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., Zhu, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025: 57419-57449
  • How Much Data Is Sufficient to Learn High-Performing Algorithms? JOURNAL OF THE ACM Balcan, M., Deblasio, D., Dick, T., Kingsford, C., Sandholm, T., Vitercik, E. 2024; 71 (5)

    View details for DOI 10.1145/3676278

    View details for Web of Science ID 001366880900006

  • Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty ACM SIGECOM EXCHANGES Guo, W., Haghtalab, N., Kandasamy, K., Vitercik, E. 2024; 22 (1): 74-82
  • Learning to Branch: Generalization Guarantees and Limits of Data-Independent Discretization JOURNAL OF THE ACM Balcan, M., Dick, T., Sandholm, T., Vitercik, E. 2024; 71 (2)

    View details for DOI 10.1145/3637840

    View details for Web of Science ID 001208839800001

  • Generalization Guarantees for Multi-Item Profit Maximization: Pricing, Auctions, and Randomized Mechanisms OPERATIONS RESEARCH Balcan, M., Sandholm, T., Vitercik, E. 2023
  • No-Regret Learning in Partially-Informed Auctions Guo, W., Jordan, M. I., Vitercik, E., Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022
  • Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts Balcan, M., Prasad, S., Sandholm, T., Vitercik, E. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022