Proof-of-Learning: Definitions and Practice. - Hengrui Jia 001; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Anvith Thudi; Varun Chandrasekaran; Nicolas Papernot (SP, 2021)

SoK: Machine Learning Governance. - Varun Chandrasekaran; Hengrui Jia 001; Anvith Thudi; Adelin Travers; Mohammad Yaghini; Nicolas Papernot (CoRR, 2021)

Unrolling SGD: Understanding Factors Influencing Machine Unlearning. - Anvith Thudi; Gabriel Deza; Varun Chandrasekaran; Nicolas Papernot (EuroS&P, 2022)

On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning. - Anvith Thudi; Hengrui Jia 001; Ilia Shumailov; Nicolas Papernot (USENIX Security Symposium, 2022)

Bounding Membership Inference. - Anvith Thudi; Ilia Shumailov; Franziska Boenisch; Nicolas Papernot (CoRR, 2022)

Selective Classification Via Neural Network Training Dynamics. - Stephan Rabanser; Anvith Thudi; Kimia Hamidieh; Adam Dziedzic; Nicolas Papernot (CoRR, 2022)

On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning. - Congyu Fang; Hengrui Jia 001; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot (CoRR, 2022)

Proof-of-Learning is Currently More Broken Than You Think. - Congyu Fang; Hengrui Jia 001; Anvith Thudi; Mohammad Yaghini; Christopher A. Choquette-Choo; Natalie Dullerud; Varun Chandrasekaran; Nicolas Papernot (EuroS&P, 2023)

Training Private Models That Know What They Don't Know. - Stephan Rabanser; Anvith Thudi; Abhradeep Guha Thakurta; Krishnamurthy Dvijotham; Nicolas Papernot (NeurIPS, 2023)

Better Sparsifiers for Directed Eulerian Graphs. - Sushant Sachdeva; Anvith Thudi; Yibin Zhao 003 (ICALP, 2024)

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD. - Anvith Thudi; Hengrui Jia 001; Casey Meehan; Ilia Shumailov; Nicolas Papernot (USENIX Security Symposium, 2024)

Unlearnable Algorithms for In-context Learning. - Andrei Ioan Muresanu; Anvith Thudi; Michael R. Zhang; Nicolas Papernot (CoRR, 2024)

Finding Optimally Robust Data Mixtures via Concave Maximization. - Anvith Thudi; Chris J. Maddison (CoRR, 2024)

From Differential Privacy to Bounds on Membership Inference: Less can be More. - Anvith Thudi; Ilia Shumailov; Franziska Boenisch; Nicolas Papernot (Trans. Mach. Learn. Res., 2024)

MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures. - Anvith Thudi; Chris J. Maddison (ICLR, 2025)

Sampling Arbitrary Discrete Distributions for RV Commitment Schemes Using the Trimmed-Tree Knuth-Yao Algorithm. - Zoë Ruha Bell; Anvith Thudi (IACR Cryptol. ePrint Arch., 2025)

Selective Prediction via Training Dynamics. - Stephan Rabanser; Anvith Thudi; Kimia Hamidieh; Adam Dziedzic; Israfil Bahceci; Akram Bin Sediq; Hamza Umit Sokun; Nicolas Papernot (Trans. Mach. Learn. Res., 2025)

MixMin: Finding Data Mixtures via Convex Minimization. - Anvith Thudi; Evianne Rovers; Yangjun Ruan; Tristan Thrush; Chris J. Maddison (CoRR, 2025)

Leveraging Per-Instance Privacy for Machine Unlearning. - Nazanin Mohammadi Sepahvand; Anvith Thudi; Berivan Isik; Ashmita Bhattacharyya; Nicolas Papernot; Eleni Triantafillou; Daniel M. Roy 001; Gintare Karolina Dziugaite (CoRR, 2025)