Anvith Thudi

Published

• "Efficient Public Verification of Private ML via Regularization" Zoë Ruha Bell, Anvith Thudi, Olive Franzese-McLaughlin, Nicolas Papernot, Shafi Goldwasser. Proceedings of the 43rd International Conference on Machine Learning
• "Gauss-Newton Unlearning for the LLM Era" Lev McKinney, Anvith Thudi , Juhan Bae, Tara Rezaei Kheirkhah, Nicolas Papernot, Sheila A. McIlraith, Roger Baker Grosse. Proceedings of the 4th IEEE Conference on Secure and Trustworthy Machine Learning
• " k-Nearest Neighbour Adaptive Sampling (kNN-AS), a Simple Tool to Efficiently Explore Conformational Space" Evianne M. Rovers, Anvith Thudi, Jérôme Hénin Chris Maddison, Matthieu Schapira. Journal of Chemical Theory and Computation
• "MixMin: Finding Data Mixtures via Convex Minimization"(Code) Anvith Thudi , Evianne Rovers, Yangjun Ruan, Tristan Thrush, Chris J. Maddison. Proceedings of the 42nd International Conference on Machine Learning
• "Leveraging Per-Instance Privacy for Machine Unlearning" Nazanin Mohammadi Sepahvand, Anvith Thudi , Berivan Isik, Ashmita Bhattacharyya, Nicolas Papernot, Eleni Triantafillou, Daniel M. Roy, Gintare Karolina Dziugaite. Proceedings of the 42nd International Conference on Machine Learning. Oral at TPDP workshop 2025
• "Fast Exact Unlearning for In-context Learning Data for LLMs" Andrei Muresanu, Anvith Thudi , Michael R. Zhang, Nicolas Papernot. Proceedings of the 42nd International Conference on Machine Learning
• "MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures"(Code) Anvith Thudi , Chris J. Maddison. Proceedings of the 13th International Conference on Learning Representations
• "Selective Classification via Neural Training Dynamics" Stephan Rabanser, Anvith Thudi , Kimia Hamidieh, Adam Dziedzic, Nicolas Papernot. Transactions on Machine Learning Research
• "Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD" (slides) (talk) Anvith Thudi , Hengrui Jia, Casey Meehan, Ilia Shumailov, Nicolas Papernot. Proceedings of the 33rd USENIX Security Symposium
• "Better Sparsifiers for Directed Eulerian Graphs" Sushant Sachdeva, Anvith Thudi , Yibin Zhao. Proceedings of the 51st EATCS International Colloquium on Automata, Languages and Programming
• "From Differential Privacy to Bounds on Membership Inference: Less can be More"Anvith Thudi, Ilia Shumailov, Franziska Boenisch, Nicolas Papernot. Transactions on Machine Learning Research
• "Training Private Models That Know What They Don't Know" Stephan Rabanser, Anvith Thudi , Abhradeep Thakurta, Krishnamurthy Dvijotham, Nicolas Papernot. Proceedings of the 37th Conference on Neural Information Processing Systems
• "Proof-of-Learning is Currently More Broken Than You Think" Congyu Fang, Hengrui Jia, Anvith Thudi, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Varun Chandrasekaran, Nicolas Papernot. Proceedings of the 8th IEEE European Symposium on Security and Privacy
• "On the Necessity of Auditable Algorithmic Definitions for Machine Unleaning" (slides) (talk) Anvith Thudi, Hengrui Jia, Ilia Shumailov, Nicolas Papernot. Proceedings of the 31st USENIX Security Symposium
• "Unrolling SGD: Understanding Factors Influencing Machine Unlearning" (slides) Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot. Proceedings of the 7th IEEE European Symposium on Security and Privacy
• "Proof of Learning: Definitions and Practice"Hengrui Jia, Mohammad Yaghini, Christopher A. Choquette-Choo, Natalie Dullerud, Anvith Thudi, Varun Chandrasekaran, Nicolas Papernot. Proceedings of the 42nd IEEE Symposium on Security and Privacy

Preprints

• "SoK: Machine Learning Governance" Varun Chandrasekaran, Hengrui Jia, Anvith Thudi, Adelin Travers, Mohammad Yaghini, Nicolas Papernot

A collection of expository essays written while studying C* algebras and their classification under Prof. George Elliott during my undergrad. The goal of these essays was to present the main prerequisite ideas for a particular problem in one relatively short document (as a sort of reference guide with a common theme).

1. Category Theory and Classification
2. The Classification of "Nice" C* Algebras
3. The Atiyah-Singer Index Theorem (Note: indirectly motivated by a graduate course in Riemannian Geometry and the notion of index in terms of variations)
4. Graphs and C* Algebras (Note: was taking a graduate course in combinatorics and hence had graphs on my mind)
5. Irrational Rotational Algebras (Note: this is often the first concrete example on the classification of C* algebras one sees)