Seuraa
Karthik Abinav Sankararaman
Karthik Abinav Sankararaman
Meta/Facebook, University of Maryland, College Park
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Viittaukset
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Vuosi
Allocation problems in ride-sharing platforms: Online matching with offline reusable resources
JP Dickerson, KA Sankararaman, A Srinivasan, P Xu
ACM Transactions on Economics and Computation (TEAC) 9 (3), 1-17, 2021
1622021
Effective long-context scaling of foundation models
W Xiong, J Liu, I Molybog, H Zhang, P Bhargava, R Hou, L Martin, ...
arXiv preprint arXiv:2309.16039, 2023
1462023
Adversarial bandits with knapsacks
N Immorlica, K Sankararaman, R Schapire, A Slivkins
Journal of the ACM 69 (6), 1-47, 2022
1302022
The impact of neural network overparameterization on gradient confusion and stochastic gradient descent
KA Sankararaman, S De, Z Xu, WR Huang, T Goldstein
Thirty-seventh International Conference on Machine Learning (ICML), 2020
1222020
New algorithms, better bounds, and a novel model for online stochastic matching
B Brubach, KA Sankararaman, A Srinivasan, P Xu
24th Annual European Symposium on Algorithms (ESA 2016), 2016
98*2016
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
V Nanda, P Xu, KA Sankararaman, JP Dickerson, A Srinivasan
Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 2020
832020
Combinatorial semi-bandits with knapsacks
KA Sankararaman, A Slivkins
International Conference on Artificial Intelligence and Statistics, 1760--1770, 2018
612018
Matching Tasks and Workers under Known Arrival Distributions: Online Task Assignment with Two-sided Arrivals
JP Dickerson, K Sankararaman, A Srinivasan, P Xu, Y Xu
ACM Transactions on Economics and Computation 12 (2), 1-28, 2024
60*2024
Dominate or delete: Decentralized competing bandits in serial dictatorship
A Sankararaman, S Basu, KA Sankararaman
International Conference on Artificial Intelligence and Statistics, 1252-1260, 2021
52*2021
Beyond regret for decentralized bandits in matching markets
S Basu, KA Sankararaman, A Sankararaman
International Conference on Machine Learning, 705-715, 2021
472021
Ensuring privacy in location-based services: An approach based on opacity enforcement
YC Wu, KA Sankararaman, S Lafortune
IFAC Proceedings Volumes 47 (2), 33-38, 2014
452014
Stochastic bandits for multi-platform budget optimization in online advertising
V Avadhanula, R Colini Baldeschi, S Leonardi, KA Sankararaman, ...
Proceedings of the Web Conference 2021, 2805-2817, 2021
422021
Balancing relevance and diversity in online bipartite matching via submodularity
JP Dickerson, KA Sankararaman, A Srinivasan, P Xu
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 1877-1884, 2019
382019
Attenuate locally, win globally: Attenuation-based frameworks for online stochastic matching with timeouts
B Brubach, KA Sankararaman, A Srinivasan, P Xu
Algorithmica 82, 64-87, 2020
332020
Online resource allocation with matching constraints
J Dickerson, K Sankararaman, K Sarpatwar, A Srinivasan, KL Wu, P Xu
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2019
302019
Contextual bandits with packing and covering constraints: A modular lagrangian approach via regression
A Slivkins, KA Sankararaman, DJ Foster
The Thirty Sixth Annual Conference on Learning Theory, 4633-4656, 2023
25*2023
Matching algorithms for blood donation
DC McElfresh, C Kroer, S Pupyrev, E Sodomka, KA Sankararaman, ...
Proceedings of the 21st ACM Conference on Economics and Computation, 463-464, 2020
242020
Bandits with knapsacks beyond the worst case
KA Sankararaman, A Slivkins
Advances in Neural Information Processing Systems 34, 23191-23204, 2021
23*2021
Multi-armed bandits with cost subsidy
D Sinha, KA Sankararaman, A Kazerouni, V Avadhanula
International Conference on Artificial Intelligence and Statistics, 3016-3024, 2021
222021
Bayesformer: Transformer with uncertainty estimation
KA Sankararaman, S Wang, H Fang
arXiv preprint arXiv:2206.00826, 2022
202022
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Artikkelit 1–20