Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations. - Amin Ghiasi; Hamid Kazemi; Steven Reich; Chen Zhu 001; Micah Goldblum; Tom Goldstein (ICML, 2022)

What do Vision Transformers Learn? A Visual Exploration. - Amin Ghiasi; Hamid Kazemi; Eitan Borgnia; Steven Reich; Manli Shu; Micah Goldblum; Andrew Gordon Wilson; Tom Goldstein (CoRR, 2022)

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries. - Yuxin Wen; Arpit Bansal; Hamid Kazemi; Eitan Borgnia; Micah Goldblum; Jonas Geiping; Tom Goldstein (ICLR, 2023)

Cold Diffusion: Inverting Arbitrary Image Transforms Without Noise. - Arpit Bansal; Eitan Borgnia; Hong-Min Chu; Jie Li 070; Hamid Kazemi; Furong Huang; Micah Goldblum; Jonas Geiping; Tom Goldstein (NeurIPS, 2023)

Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text. - Abhimanyu Hans; Avi Schwarzschild; Valeriia Cherepanova; Hamid Kazemi; Aniruddha Saha; Micah Goldblum; Jonas Geiping; Tom Goldstein (ICML, 2024)

Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs. - Abhimanyu Hans; John Kirchenbauer; Yuxin Wen; Neel Jain; Hamid Kazemi; Prajwal Singhania; Siddharth Singh; Gowthami Somepalli; Jonas Geiping; Abhinav Bhatele; Tom Goldstein (NeurIPS, 2024)

What do we learn from inverting CLIP models? - Hamid Kazemi; Atoosa Malemir Chegini; Jonas Geiping; Soheil Feizi; Tom Goldstein (CoRR, 2024)

Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion. - Hossein Souri; Arpit Bansal; Hamid Kazemi; Liam Fowl; Aniruddha Saha; Jonas Geiping; Andrew Gordon Wilson; Rama Chellappa; Tom Goldstein; Micah Goldblum (CoRR, 2024)

SALSA: Soup-based Alignment Learning for Stronger Adaptation in RLHF. - Atoosa Malemir Chegini; Hamid Kazemi; Iman Mirzadeh; Dong Yin; Maxwell Horton; Moin Nabi; Mehrdad Farajtabar; Keivan Alizadeh (CoRR, 2024)