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Darshan Chakrabarti
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2020 – today
- 2024
- [c8]Darshan Chakrabarti, Gabriele Farina, Christian Kroer:
Efficient Learning in Polyhedral Games via Best-Response Oracles. AAAI 2024: 9564-9572 - [c7]Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken:
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. AAAI 2024: 9626-9635 - [c6]Seyed A. Esmaeili, Darshan Chakrabarti, Hayley Grape, Brian Brubach:
Implications of Distance over Redistricting Maps: Central and Outlier Maps. AAAI 2024: 9679-9687 - [i9]Michael J. Curry, Vinzenz Thoma, Darshan Chakrabarti, Stephen McAleer, Christian Kroer, Tuomas Sandholm, Niao He, Sven Seuken:
Automated Design of Affine Maximizer Mechanisms in Dynamic Settings. CoRR abs/2402.08129 (2024) - [i8]Darshan Chakrabarti, Julien Grand-Clément, Christian Kroer:
Extensive-Form Game Solving via Blackwell Approachability on Treeplexes. CoRR abs/2403.04680 (2024) - 2023
- [c5]Darshan Chakrabarti, Jelena Diakonikolas, Christian Kroer:
Block-Coordinate Methods and Restarting for Solving Extensive-Form Games. NeurIPS 2023 - [i7]Darshan Chakrabarti, Jelena Diakonikolas, Christian Kroer:
Block-Coordinate Methods and Restarting for Solving Extensive-Form Games. CoRR abs/2307.16754 (2023) - [i6]Darshan Chakrabarti, Gabriele Farina, Christian Kroer:
Efficient Learning in Polyhedral Games via Best Response Oracles. CoRR abs/2312.03696 (2023) - 2022
- [c4]Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas:
A New Notion of Individually Fair Clustering: α-Equitable k-Center. AISTATS 2022: 6387-6408 - [c3]Darshan Chakrabarti, Jie Gao, Aditya Saraf, Grant Schoenebeck, Fang-Yi Yu:
Optimal Local Bayesian Differential Privacy over Markov Chains. AAMAS 2022: 1563-1565 - [i5]Darshan Chakrabarti, Jie Gao, Aditya Saraf, Grant Schoenebeck, Fang-Yi Yu:
Optimal Local Bayesian Differential Privacy over Markov Chains. CoRR abs/2206.11402 (2022) - [i4]Gianluca Brero, Alon Eden, Darshan Chakrabarti, Matthias Gerstgrasser, Vincent Li, David C. Parkes:
Learning Stackelberg Equilibria and Applications to Economic Design Games. CoRR abs/2210.03852 (2022) - 2021
- [c2]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas:
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints. AAAI 2021: 6822-6830 - [i3]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Aravind Srinivasan, Leonidas Tsepenekas:
Fairness, Semi-Supervised Learning, and More: A General Framework for Clustering with Stochastic Pairwise Constraints. CoRR abs/2103.02013 (2021) - [i2]Darshan Chakrabarti, John P. Dickerson, Seyed A. Esmaeili, Aravind Srinivasan, Leonidas Tsepenekas:
A New Notion of Individually Fair Clustering: α-Equitable k-Center. CoRR abs/2106.05423 (2021) - 2020
- [c1]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas:
A Pairwise Fair and Community-preserving Approach to k-Center Clustering. ICML 2020: 1178-1189 - [i1]Brian Brubach, Darshan Chakrabarti, John P. Dickerson, Samir Khuller, Aravind Srinivasan, Leonidas Tsepenekas:
A Pairwise Fair and Community-preserving Approach to k-Center Clustering. CoRR abs/2007.07384 (2020)
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