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Sanath Kumar Krishna Murthy
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2020 – today
- 2024
- [c10]Sanath Kumar Krishnamurthy, Adrienne M. Propp, Susan Athey:
Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspective. AISTATS 2024: 2476-2484 - [i14]Sanath Kumar Krishnamurthy, Susan Athey, Emma Brunskill:
Data-driven Error Estimation: Upper Bounding Multiple Errors with No Technical Debt. CoRR abs/2405.04636 (2024) - 2023
- [c9]Aldo Gael Carranza, Sanath Kumar Krishnamurthy, Susan Athey:
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracles. AISTATS 2023: 7190-7212 - [c8]Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill:
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization. NeurIPS 2023 - [i13]Sanath Kumar Krishnamurthy, Tanmay Gangwani, Sumeet Katariya, Branislav Kveton, Anshuka Rangi:
Selective Uncertainty Propagation in Offline RL. CoRR abs/2302.00284 (2023) - [i12]Sanath Kumar Krishnamurthy, Ruohan Zhan, Susan Athey, Emma Brunskill:
Proportional Response: Contextual Bandits for Simple and Cumulative Regret Minimization. CoRR abs/2307.02108 (2023) - 2022
- [i11]Aldo Gael Carranza, Sanath Kumar Krishnamurthy, Susan Athey:
Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracle. CoRR abs/2203.16668 (2022) - [i10]Susan Athey, Undral Byambadalai, Vitor Hadad, Sanath Kumar Krishnamurthy, Weiwen Leung, Joseph Jay Williams:
Contextual Bandits in a Survey Experiment on Charitable Giving: Within-Experiment Outcomes versus Policy Learning. CoRR abs/2211.12004 (2022) - 2021
- [c7]Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey:
Tractable contextual bandits beyond realizability. AISTATS 2021: 1423-1431 - [c6]Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey:
Adapting to misspecification in contextual bandits with offline regression oracles. ICML 2021: 5805-5814 - [i9]Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey:
Adapting to misspecification in contextual bandits with offline regression oracles. CoRR abs/2102.13240 (2021) - [i8]Sanath Kumar Krishnamurthy, Susan Athey:
Optimal Model Selection in Contextual Bandits with Many Classes via Offline Oracles. CoRR abs/2106.06483 (2021) - 2020
- [j1]Siddharth Barman, Sanath Kumar Krishnamurthy:
Approximation Algorithms for Maximin Fair Division. ACM Trans. Economics and Comput. 8(1): 5:1-5:28 (2020) - [i7]Sanath Kumar Krishnamurthy, Susan Athey:
Survey Bandits with Regret Guarantees. CoRR abs/2002.09814 (2020) - [i6]Sanath Kumar Krishnamurthy, Vitor Hadad, Susan Athey:
Tractable contextual bandits beyond realizability. CoRR abs/2010.13013 (2020)
2010 – 2019
- 2019
- [c5]Siddharth Barman, Sanath Kumar Krishnamurthy:
On the Proximity of Markets with Integral Equilibria. AAAI 2019: 1748-1755 - 2018
- [c4]Siddharth Barman, Arpita Biswas, Sanath Kumar Krishna Murthy, Yadati Narahari:
Groupwise Maximin Fair Allocation of Indivisible Goods. AAAI 2018: 917-924 - [c3]Siddharth Barman, Sanath Kumar Krishnamurthy, Rohit Vaish:
Greedy Algorithms for Maximizing Nash Social Welfare. AAMAS 2018: 7-13 - [c2]Siddharth Barman, Sanath Kumar Krishnamurthy, Rohit Vaish:
Finding Fair and Efficient Allocations. EC 2018: 557-574 - [i5]Siddharth Barman, Sanath Kumar Krishna Murthy, Rohit Vaish:
Greedy Algorithms for Maximizing Nash Social Welfare. CoRR abs/1801.09046 (2018) - [i4]Siddharth Barman, Sanath Kumar Krishnamurthy:
On the Proximity of Markets with Integral Equilibria. CoRR abs/1811.08673 (2018) - 2017
- [c1]Siddharth Barman, Sanath Kumar Krishna Murthy:
Approximation Algorithms for Maximin Fair Division. EC 2017: 647-664 - [i3]Siddharth Barman, Sanath Kumar Krishna Murthy:
Approximation Algorithms for Maximin Fair Division. CoRR abs/1703.01851 (2017) - [i2]Siddharth Barman, Sanath Kumar Krishna Murthy, Rohit Vaish:
Finding Fair and Efficient Allocations. CoRR abs/1707.04731 (2017) - [i1]Siddharth Barman, Arpita Biswas, Sanath Kumar Krishna Murthy, Y. Narahari:
Groupwise Maximin Fair Allocation of Indivisible Goods. CoRR abs/1711.07621 (2017)
Coauthor Index
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last updated on 2024-06-14 18:13 CEST by the dblp team
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