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On-Demand Sampling: Learning Optimally from Multiple Distributions


Oct 22, 2022
Nika Haghtalab, Michael I. Jordan, Eric Zhao

* 39 pages, 1 figure. Authors are ordered alphabetically. Appearing at the Thirty-sixth Conference on Neural Information Processing Systems (Neurips 2022) 

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Competition, Alignment, and Equilibria in Digital Marketplaces


Aug 30, 2022
Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab


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Learning in Stackelberg Games with Non-myopic Agents


Aug 19, 2022
Nika Haghtalab, Thodoris Lykouris, Sloan Nietert, Alex Wei

* An extended abstract of this work appeared at the ACM Conference on Economics and Computation (EC) 2022 

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Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries


Mar 08, 2022
Nika Haghtalab, Yanjun Han, Abhishek Shetty, Kunhe Yang

* Under submission 

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One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning


Mar 04, 2021
Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, Han Shao


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Smoothed Analysis with Adaptive Adversaries


Feb 16, 2021
Nika Haghtalab, Tim Roughgarden, Abhishek Shetty


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Maximizing Welfare with Incentive-Aware Evaluation Mechanisms


Nov 03, 2020
Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Jack Z. Wang

* Published in IJCAI 2020 

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Noise in Classification


Oct 10, 2020
Maria-Florina Balcan, Nika Haghtalab

* Chapter 16 of the book Beyond the Worst-Case Analysis of Algorithms 

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Smoothed Analysis of Online and Differentially Private Learning


Jun 17, 2020
Nika Haghtalab, Tim Roughgarden, Abhishek Shetty


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