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ORCIDNivasini Ananthakrishnan
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2020 – today
- 2026
[i8]Nivasini Ananthakrishnan, Alireza Fallah, Michael I. Jordan:
Signaling in Data Markets via Free Samples. CoRR abs/2602.16919 (2026)- [i7]Nivasini Ananthakrishnan, Meena Jagadeesan
:
Power and Limitations of Aggregation in Compound AI Systems. CoRR abs/2602.21556 (2026) - [i6]Nivasini Ananthakrishnan, Mark Bedaywi, Michael I. Jordan, Stuart Russell, Nika Haghtalab:
Provably Optimal Learning Algorithms for Assistance Games. CoRR abs/2607.08012 (2026) - 2025
- [i5]Nivasini Ananthakrishnan, Yuval Dagan, Kunhe Yang:
Learning Local Stackelberg Equilibria from Repeated Interactions with a Learning Agent. CoRR abs/2510.22471 (2025) - 2024
- [c7]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. AISTATS 2024: 478-486 - [c6]Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan:
Privacy Can Arise Endogenously in an Economic System with Learning Agents. FORC 2024: 9:1-9:22 - [c5]Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata, Kunhe Yang:
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions. NeurIPS 2024 - [i4]Nivasini Ananthakrishnan, Tiffany Ding, Mariel A. Werner, Sai Praneeth Karimireddy, Michael I. Jordan:
Privacy Can Arise Endogenously in an Economic System with Learning Agents. CoRR abs/2404.10767 (2024) - [i3]Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata, Kunhe Yang:
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interaction. CoRR abs/2408.08272 (2024) - 2023
- [i2]Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan, Nika Haghtalab:
Delegating Data Collection in Decentralized Machine Learning. CoRR abs/2309.01837 (2023) - 2021
- [c4]Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner:
Classification Confidence Scores with Point-wise Guarantees. SafeAI@AAAI 2021 - [c3]Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner:
Open Problem: Are all VC-classes CPAC learnable? COLT 2021: 4636-4641 - [c2]Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner:
Identifying regions of trusted predictions. UAI 2021: 2125-2134 - [i1]Tosca Lechner, Shai Ben-David, Sushant Agarwal, Nivasini Ananthakrishnan:
Impossibility results for fair representations. CoRR abs/2107.03483 (2021) - 2020
- [c1]Sushant Agarwal, Nivasini Ananthakrishnan, Shai Ben-David, Tosca Lechner, Ruth Urner:
On Learnability wih Computable Learners. ALT 2020: 48-60
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