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ORCIDAnvith Thudi
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2020 – today
- 2024
[j1]Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot:
From Differential Privacy to Bounds on Membership Inference: Less can be More. Trans. Mach. Learn. Res. 2024 (2024)- [c7]Sushant Sachdeva, Anvith Thudi, Yibin Zhao:
Better Sparsifiers for Directed Eulerian Graphs. ICALP 2024: 119:1-119:20 - [c6]Anvith Thudi, Hengrui Jia, Casey Meehan, Ilia Shumailov, Nicolas Papernot:
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. USENIX Security Symposium 2024 - [i12]Andrei Ioan Muresanu, Anvith Thudi, Michael R. Zhang, Nicolas Papernot:
Unlearnable Algorithms for In-context Learning. CoRR abs/2402.00751 (2024) - [i11]Anvith Thudi, Chris J. Maddison:
Finding Optimally Robust Data Mixtures via Concave Maximization. CoRR abs/2406.01477 (2024) - 2023
- [c5]Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot:
Proof-of-Learning is Currently More Broken Than You Think. EuroS&P 2023: 797-816 - [c4]Stephan Rabanser, Anvith Thudi, Abhradeep Guha Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot:
Training Private Models That Know What They Don't Know. NeurIPS 2023 - [i10]Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot:
Training Private Models That Know What They Don't Know. CoRR abs/2305.18393 (2023) - [i9]Anvith Thudi, Hengrui Jia, Casey Meehan, Ilia Shumailov, Nicolas Papernot:
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. CoRR abs/2307.00310 (2023) - [i8]Sushant Sachdeva, Anvith Thudi, Yibin Zhao:
Better Sparsifiers for Directed Eulerian Graphs. CoRR abs/2311.06232 (2023) - 2022
- [c3]Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot:
Unrolling SGD: Understanding Factors Influencing Machine Unlearning. EuroS&P 2022: 303-319 - [c2]Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot:
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. USENIX Security Symposium 2022: 4007-4022 - [i7]Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot:
Bounding Membership Inference. CoRR abs/2202.12232 (2022) - [i6]Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot:
Selective Classification Via Neural Network Training Dynamics. CoRR abs/2205.13532 (2022) - [i5]Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot:
On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. CoRR abs/2208.03567 (2022) - 2021
- [c1]Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot:
Proof-of-Learning: Definitions and Practice. SP 2021: 1039-1056 - [i4]Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot:
Proof-of-Learning: Definitions and Practice. CoRR abs/2103.05633 (2021) - [i3]Varun Chandrasekaran, Hengrui Jia, Anvith Thudi, Adelin Travers, Mohammad Yaghini, Nicolas Papernot:
SoK: Machine Learning Governance. CoRR abs/2109.10870 (2021) - [i2]Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot:
Unrolling SGD: Understanding Factors Influencing Machine Unlearning. CoRR abs/2109.13398 (2021) - [i1]Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot:
On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. CoRR abs/2110.11891 (2021)
Coauthor Index
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last updated on 2024-08-10 01:25 CEST by the dblp team
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