Spremljaj
Ellen Vitercik
Ellen Vitercik
Preverjeni e-poštni naslov na stanford.edu - Domača stran
Naslov
Navedeno
Navedeno
Leto
Learning to branch: Generalization guarantees and limits of data-independent discretization
MF Balcan, T Dick, T Sandholm, E Vitercik
Journal of the ACM, 2024
222*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
83*2018
Sample Complexity of Automated Mechanism Design
MF Balcan, T Sandholm, E Vitercik
Advances In Neural Information Processing Systems, 2083-2091, 2016
752016
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
62*2021
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
622018
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
612017
Synchronization Strings: Channel Simulations and Interactive Coding for Insertions and Deletions
B Haeupler, A Shahrasbi, E Vitercik
arXiv preprint arXiv:1707.04233, 2017
422017
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
322021
Estimating Approximate Incentive Compatibility
E Vitercik, MF Balcan, T Sandholm
ACM Conference on Economics and Computation, 2019
24*2019
Learning combinatorial functions from pairwise comparisons
MF Balcan, E Vitercik, C White
Conference on Learning Theory, 310-335, 2016
202016
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
172020
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
172019
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
132022
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
132022
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
132020
Generalization in portfolio-based algorithm selection
MF Balcan, T Sandholm, E Vitercik
Proceedings of the AAAI Conference on Artificial Intelligence 35 (14), 12225 …, 2021
102021
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
92019
No-Regret Learning in Partially-Informed Auctions
W Guo, M Jordan, E Vitercik
International Conference on Machine Learning, 8039-8055, 2022
62022
Private optimization without constraint violations
A Muñoz Medina, U Syed, S Vassilvtiskii, E Vitercik
International Conference on Artificial Intelligence and Statistics, 2557-2565, 2021
52021
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
W Guo, N Haghtalab, K Kandasamy, E Vitercik
arXiv preprint arXiv:2302.09700, 2023
22023
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