受强制性开放获取政策约束的文章 - Karthik Abinav Sankararaman了解详情
可在其他位置公开访问的文章：16 篇
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
强制性开放获取政策： US National Science Foundation, US Department of Defense
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
强制性开放获取政策： US National Science Foundation, US Department of Defense
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
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation, US Department of Defense
Combinatorial semi-bandits with knapsacks
KA Sankararaman, A Slivkins
International Conference on Artificial Intelligence and Statistics, 1760--1770, 2018
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： European Commission, Government of Italy
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
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation
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
强制性开放获取政策： US National Science Foundation
A unified approach to online matching with conflict-aware constraints
P Xu, Y Shi, H Cheng, J Dickerson, KA Sankararaman, A Srinivasan, ...
Proceedings of the AAAI conference on artificial intelligence 33 (01), 2221-2228, 2019
强制性开放获取政策： US National Science Foundation, 国家自然科学基金委员会
Algorithms to approximate column-sparse packing problems
B Brubach, KA Sankararaman, A Srinivasan, P Xu
ACM Transactions on Algorithms (TALG) 16 (1), 1-32, 2019
强制性开放获取政策： US National Science Foundation
Mix and match: Markov chains and mixing times for matching in rideshare
M Curry, JP Dickerson, KA Sankararaman, A Srinivasan, Y Wan, P Xu
Web and Internet Economics: 15th International Conference, WINE 2019, New …, 2019
强制性开放获取政策： US National Science Foundation, US Department of Defense
Allocation Problem in Remote Teleoperation: Online Matching with Offline Reusable Resources and Delayed Assignments
OA Viden, Y Trabelsi, P Xu, KA Sankararaman, O Maksimov, S Kraus
强制性开放获取政策： US National Science Foundation, European Commission
Stability of linear structural equation models of causal inference
KA Sankararaman, A Louis, N Goyal
Uncertainty in Artificial Intelligence, 323-333, 2019
强制性开放获取政策： US National Science Foundation, Department of Science & Technology, India
Robust identifiability in linear structural equation models of causal inference
KA Sankararaman, A Louis, N Goyal
Uncertainty in Artificial Intelligence, 1728-1737, 2022
强制性开放获取政策： Department of Science & Technology, India
Online minimum matching with uniform metric and random arrivals
SVS Duppala, KA Sankararaman, P Xu
Operations Research Letters 50 (1), 45-49, 2022
强制性开放获取政策： US National Science Foundation
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