Headless Horseman: Adversarial Attacks on Transfer Learning Models. - Ahmed Abdelkader; Michael J. Curry; Liam Fowl; Tom Goldstein; Avi Schwarzschild; Manli Shu; Christoph Studer; Chen Zhu 001 (ICASSP, 2020)

Truth or backpropaganda? An empirical investigation of deep learning theory. - Micah Goldblum; Jonas Geiping; Avi Schwarzschild; Michael Moeller 001; Tom Goldstein (ICLR, 2020)

Adversarial Attacks on Machine Learning Systems for High-Frequency Trading. - Micah Goldblum; Avi Schwarzschild; Naftali Cohen; Tucker Balch; Ankit B. Patel; Tom Goldstein (CoRR, 2020)

Adversarial attacks on machine learning systems for high-frequency trading. - Micah Goldblum; Avi Schwarzschild; Ankit B. Patel; Tom Goldstein (ICAIF, 2021)

Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. - Avi Schwarzschild; Micah Goldblum; Arjun Gupta; John P. Dickerson; Tom Goldstein (ICML, 2021)

Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. - Avi Schwarzschild; Eitan Borgnia; Arjun Gupta; Furong Huang; Uzi Vishkin; Micah Goldblum; Tom Goldstein (NeurIPS, 2021)

Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems. - Avi Schwarzschild; Arjun Gupta; Micah Goldblum; Tom Goldstein (CoRR, 2021)

SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. - Gowthami Somepalli; Micah Goldblum; Avi Schwarzschild; C. Bayan Bruss; Tom Goldstein (CoRR, 2021)

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. - Arpit Bansal; Micah Goldblum; Valeriia Cherepanova; Avi Schwarzschild; C. Bayan Bruss; Tom Goldstein (CoRR, 2021)

Datasets for Studying Generalization from Easy to Hard Examples. - Avi Schwarzschild; Eitan Borgnia; Arjun Gupta; Arpit Bansal; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (CoRR, 2021)

The Uncanny Similarity of Recurrence and Depth. - Avi Schwarzschild; Arjun Gupta; Amin Ghiasi; Micah Goldblum; Tom Goldstein (ICLR, 2022)

End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking. - Arpit Bansal; Avi Schwarzschild; Eitan Borgnia; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (NeurIPS, 2022)

End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking. - Arpit Bansal; Avi Schwarzschild; Eitan Borgnia; Zeyad Emam; Furong Huang; Micah Goldblum; Tom Goldstein (CoRR, 2022)

Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective. - Avi Schwarzschild; Max Cembalest; Karthik Rao; Keegan Hines; John P. Dickerson (AIES, 2023)

Universal Guidance for Diffusion Models. - Arpit Bansal; Hong-Min Chu; Avi Schwarzschild; Soumyadip Sengupta; Micah Goldblum; Jonas Geiping; Tom Goldstein (CVPR Workshops, 2023)

Transfer Learning with Deep Tabular Models. - Roman Levin; Valeriia Cherepanova; Avi Schwarzschild; Arpit Bansal; C. Bayan Bruss; Tom Goldstein; Andrew Gordon Wilson; Micah Goldblum (ICLR, 2023)

Neural Auctions Compromise Bidder Information. - Alex Stein; Avi Schwarzschild; Michael J. Curry; Tom Goldstein; John P. Dickerson (CoRR, 2023)

A Cookbook of Self-Supervised Learning. - Randall Balestriero; Mark Ibrahim; Vlad Sobal; Ari Morcos; Shashank Shekhar; Tom Goldstein; Florian Bordes; Adrien Bardes; Grégoire Mialon; Yuandong Tian; Avi Schwarzschild; Andrew Gordon Wilson; et al. (CoRR, 2023)

Baseline Defenses for Adversarial Attacks Against Aligned Language Models. - Neel Jain; Avi Schwarzschild; Yuxin Wen; Gowthami Somepalli; John Kirchenbauer; Ping-yeh Chiang; Micah Goldblum; Aniruddha Saha; Jonas Geiping; Tom Goldstein (CoRR, 2023)

Effective Backdoor Mitigation Depends on the Pre-training Objective. - Sahil Verma; Gantavya Bhatt; Avi Schwarzschild; Soumye Singhal; Arnav Mohanty Das; Chirag Shah 001; John P. Dickerson; Jeff A. Bilmes (CoRR, 2023)

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. - Micah Goldblum; Dimitris Tsipras; Chulin Xie; Xinyun Chen; Avi Schwarzschild; Dawn Song; Aleksander Madry; Bo Li 026; Tom Goldstein (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Deep Thinking Systems: Logical Extrapolation with Recurrent Neural Networks. - Avi Schwarzschild (2023)

NEFTune: Noisy Embeddings Improve Instruction Finetuning. - Neel Jain; Ping-yeh Chiang; Yuxin Wen; John Kirchenbauer; Hong-Min Chu; Gowthami Somepalli; Brian R. Bartoldson; Bhavya Kailkhura; Avi Schwarzschild; Aniruddha Saha; Micah Goldblum; Jonas Geiping; et al. (ICLR, 2024)

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)

Easy2Hard-Bench: Standardized Difficulty Labels for Profiling LLM Performance and Generalization. - Mucong Ding; Chenghao Deng; Jocelyn Choo; Zichu Wu; Aakriti Agrawal; Avi Schwarzschild; Tianyi Zhou 001; Tom Goldstein; John Langford 001; Animashree Anandkumar; Furong Huang (NeurIPS, 2024)

Transformers Can Do Arithmetic with the Right Embeddings. - Sean McLeish; Arpit Bansal; Alex Stein; Neel Jain; John Kirchenbauer; Brian R. Bartoldson; Bhavya Kailkhura; Abhinav Bhatele; Jonas Geiping; Avi Schwarzschild; Tom Goldstein (NeurIPS, 2024)

Rethinking LLM Memorization through the Lens of Adversarial Compression. - Avi Schwarzschild; Zhili Feng; Pratyush Maini; Zachary C. Lipton; J. Zico Kolter (NeurIPS, 2024)

TOFU: A Task of Fictitious Unlearning for LLMs. - Pratyush Maini; Zhili Feng; Avi Schwarzschild; Zachary C. Lipton; J. Zico Kolter (CoRR, 2024)

Benchmarking ChatGPT on Algorithmic Reasoning. - Sean McLeish; Avi Schwarzschild; Tom Goldstein (CoRR, 2024)

Forcing Diffuse Distributions out of Language Models. - Yiming Zhang; Avi Schwarzschild; Nicholas Carlini; Zico Kolter; Daphne Ippolito (CoRR, 2024)

The CLRS-Text Algorithmic Reasoning Language Benchmark. - Larisa Markeeva; Sean McLeish; Borja Ibarz; Wilfried Bounsi; Olga Kozlova; Alex Vitvitskyi; Charles Blundell; Tom Goldstein; Avi Schwarzschild; Petar Velickovic (CoRR, 2024)

Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers. - Joshua Nathaniel Williams; Avi Schwarzschild; J. Zico Kolter (CoRR, 2024)

Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective. - Sahil Verma; Gantavya Bhatt; Avi Schwarzschild; Soumye Singhal; Arnav Mohanty Das; Chirag Shah 001; John P. Dickerson; Pin-Yu Chen; Jeff Bilmes (Trans. Mach. Learn. Res., 2025)

Self-Improving Transformers Overcome Easy-to-Hard and Length Generalization Challenges. - Nayoung Lee; Ziyang Cai; Avi Schwarzschild; Kangwook Lee 001; Dimitris Papailiopoulos (CoRR, 2025)

Has My System Prompt Been Used? Large Language Model Prompt Membership Inference. - Roman Levin; Valeriia Cherepanova; Abhimanyu Hans; Avi Schwarzschild; Tom Goldstein (CoRR, 2025)

Antidistillation Sampling. - Yash Savani; Asher Trockman; Zhili Feng; Avi Schwarzschild; Alexander Robey; Marc Finzi; J. Zico Kolter (CoRR, 2025)

Existing Large Language Model Unlearning Evaluations Are Inconclusive. - Zhili Feng; Yixuan Even Xu; Alexander Robey; Robert Kirk; Xander Davies; Yarin Gal; Avi Schwarzschild; J. Zico Kolter (CoRR, 2025)

Extrapolation by Association: Length Generalization Transfer in Transformers. - Ziyang Cai; Nayoung Lee; Avi Schwarzschild; Samet Oymak; Dimitris Papailiopoulos (CoRR, 2025)

Command-V: Pasting LLM Behaviors via Activation Profiles. - Barry Wang; Avi Schwarzschild; Alexander Robey; Ali Payani; Charles Fleming; Mingjie Sun; Daphne Ippolito (CoRR, 2025)