| Learning to branch: Generalization guarantees and limits of data-independent discretization MF Balcan, T Dick, T Sandholm, E Vitercik Journal of the ACM, 2024 | 221* | 2024 |
| A General Theory of Sample Complexity for Multi-Item Profit Maximization MF Balcan, T Sandholm, E Vitercik Proceedings of the 2018 ACM Conference on Economics and Computation, 173-174, 2018 | 85* | 2018 |
| Sample Complexity of Automated Mechanism Design MF Balcan, T Sandholm, E Vitercik Advances In Neural Information Processing Systems, 2083-2091, 2016 | 76 | 2016 |
| Dispersion for data-driven algorithm design, online learning, and private optimization MF Balcan, T Dick, E Vitercik 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018 | 67 | 2018 |
| How much data is sufficient to learn high-performing algorithms? generalization guarantees for data-driven algorithm design MF Balcan, D DeBlasio, T Dick, C Kingsford, T Sandholm, E Vitercik Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 61* | 2021 |
| Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems MF Balcan, V Nagarajan, E Vitercik, C White Conference on Learning Theory, 213-274, 2017 | 61 | 2017 |
| Synchronization Strings: Channel Simulations and Interactive Coding for Insertions and Deletions B Haeupler, A Shahrasbi, E Vitercik arXiv preprint arXiv:1707.04233, 2017 | 36 | 2017 |
| Sample complexity of tree search configuration: Cutting planes and beyond MFF Balcan, S Prasad, T Sandholm, E Vitercik Advances in Neural Information Processing Systems 34, 4015-4027, 2021 | 31 | 2021 |
| Estimating Approximate Incentive Compatibility E Vitercik, MF Balcan, T Sandholm ACM Conference on Economics and Computation, 2019 | 25* | 2019 |
| Learning combinatorial functions from pairwise comparisons MF Balcan, E Vitercik, C White Conference on Learning Theory, 310-335, 2016 | 20 | 2016 |
| Refined bounds for algorithm configuration: The knife-edge of dual class approximability MF Balcan, T Sandholm, E Vitercik International Conference on Machine Learning, 580-590, 2020 | 17 | 2020 |
| Learning to prune: Speeding up repeated computations D Alabi, AT Kalai, K Liggett, C Musco, C Tzamos, E Vitercik Conference on Learning Theory, 30-33, 2019 | 17 | 2019 |
| Improved Sample Complexity Bounds for Branch-And-Cut MF Balcan, S Prasad, T Sandholm, E Vitercik 28th International Conference on Principles and Practice of Constraint …, 2022 | 13 | 2022 |
| Learning to optimize computational resources: Frugal training with generalization guarantees MF Balcan, T Sandholm, E Vitercik Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3227-3234, 2020 | 13 | 2020 |
| Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts MFF Balcan, S Prasad, T Sandholm, E Vitercik Advances in Neural Information Processing Systems 35, 33890-33903, 2022 | 12 | 2022 |
| Generalization in portfolio-based algorithm selection MF Balcan, T Sandholm, E Vitercik Proceedings of the AAAI Conference on Artificial Intelligence 35 (14), 12225 …, 2021 | 10 | 2021 |
| Algorithmic greenlining: An approach to increase diversity C Borgs, J Chayes, N Haghtalab, AT Kalai, E Vitercik Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 69-76, 2019 | 10 | 2019 |
| No-Regret Learning in Partially-Informed Auctions W Guo, M Jordan, E Vitercik International Conference on Machine Learning, 8039-8055, 2022 | 6 | 2022 |
| Private optimization without constraint violations A Muñoz Medina, U Syed, S Vassilvtiskii, E Vitercik International Conference on Artificial Intelligence and Statistics, 2557-2565, 2021 | 5 | 2021 |
| Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty W Guo, N Haghtalab, K Kandasamy, E Vitercik arXiv preprint arXiv:2302.09700, 2023 | 2 | 2023 |
Maria-Florina BalcanProfessor, School of Computer Science, Carnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
