On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning. - Alireza Fallah 001; Kristian Georgiev; Aryan Mokhtari; Asuman E. Ozdaglar (NeurIPS, 2021)

Implicit Bias of Linear Equivariant Networks. - Hannah Lawrence; Bobak Toussi Kiani; Kristian G. Georgiev; Andrew K. Dienes (ICML, 2022)

Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation. - Kristian Georgiev; Samuel B. Hopkins 001 (NeurIPS, 2022)

Rethinking Backdoor Attacks. - Alaa Khaddaj; Guillaume Leclerc; Aleksandar Makelov; Kristian Georgiev; Hadi Salman; Andrew Ilyas; Aleksander Madry (ICML, 2023)

TRAK: Attributing Model Behavior at Scale. - Sung Min Park; Kristian Georgiev; Andrew Ilyas; Guillaume Leclerc; Aleksander Madry (ICML, 2023)

The Journey, Not the Destination: How Data Guides Diffusion Models. - Kristian Georgiev; Joshua Vendrow; Hadi Salman; Sung Min Park; Aleksander Madry (CoRR, 2023)

ContextCite: Attributing Model Generation to Context. - Benjamin Cohen-Wang; Harshay Shah; Kristian Georgiev; Aleksander Madry (NeurIPS, 2024)

Improving Subgroup Robustness via Data Selection. - Saachi Jain; Kimia Hamidieh; Kristian Georgiev; Andrew Ilyas; Marzyeh Ghassemi; Aleksander Madry (NeurIPS, 2024)

Data Debiasing with Datamodels (D3M): Improving Subgroup Robustness via Data Selection. - Saachi Jain; Kimia Hamidieh; Kristian Georgiev; Andrew Ilyas; Marzyeh Ghassemi; Aleksander Madry (CoRR, 2024)

Attribute-to-Delete: Machine Unlearning via Datamodel Matching. - Kristian Georgiev; Roy Rinberg; Sung Min Park; Shivam Garg; Andrew Ilyas; Aleksander Madry; Seth Neel (CoRR, 2024)

Machine Unlearning via Simulated Oracle Matching. - Kristian Georgiev; Roy Rinberg; Sung Min Park; Shivam Garg; Andrew Ilyas; Aleksander Madry; Seth Neel (ICLR, 2025)