Avi Schwarzschild

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 39

Top Topics

Diffusion Model

Recurrent Networks

Machine Learning Systems

Top Venues

Publications

Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. ICLR (2024)
Yiming Zhang, Avi Schwarzschild, Nicholas Carlini, Zico Kolter, Daphne Ippolito
Forcing Diffuse Distributions out of Language Models. CoRR (2024)
Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein
Transformers Can Do Arithmetic with the Right Embeddings. CoRR (2024)
Avi Schwarzschild, Zhili Feng, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter
Rethinking LLM Memorization through the Lens of Adversarial Compression. CoRR (2024)
Sean McLeish, Avi Schwarzschild, Tom Goldstein
Benchmarking ChatGPT on Algorithmic Reasoning. CoRR (2024)
Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, J. Zico Kolter
TOFU: A Task of Fictitious Unlearning for LLMs. CoRR (2024)
Larisa Markeeva, Sean McLeish, Borja Ibarz, Wilfried Bounsi, Olga Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild, Petar Velickovic
The CLRS-Text Algorithmic Reasoning Language Benchmark. CoRR (2024)
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, Tom Goldstein
NEFTune: Noisy Embeddings Improve Instruction Finetuning. ICLR (2024)
Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text. CoRR (2024)
Sahil Verma, Gantavya Bhatt, Avi Schwarzschild, Soumye Singhal, Arnav Mohanty Das, Chirag Shah, John P. Dickerson, Jeff A. Bilmes
Effective Backdoor Mitigation Depends on the Pre-training Objective. CoRR (2023)
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
Transfer Learning with Deep Tabular Models. ICLR (2023)
Alex Stein, Avi Schwarzschild, Michael J. Curry, Tom Goldstein, John P. Dickerson
Neural Auctions Compromise Bidder Information. CoRR (2023)
Avi Schwarzschild, Max Cembalest, Karthik Rao, Keegan Hines, John P. Dickerson
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective. CoRR (2023)
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. CVPR Workshops (2023)
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, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum
A Cookbook of Self-Supervised Learning. CoRR (2023)
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein
Baseline Defenses for Adversarial Attacks Against Aligned Language Models. CoRR (2023)
Avi Schwarzschild, Max Cembalest, Karthik Rao, Keegan Hines, John P. Dickerson
Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective. AIES (2023)
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein
Universal Guidance for Diffusion Models. CoRR (2023)
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, Tom Goldstein
NEFTune: Noisy Embeddings Improve Instruction Finetuning. CoRR (2023)
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. IEEE Trans. Pattern Anal. Mach. Intell. 45 (2) (2023)
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking. NeurIPS (2022)
Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum
Transfer Learning with Deep Tabular Models. CoRR (2022)
Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein
The Uncanny Similarity of Recurrence and Depth. ICLR (2022)
Arpit Bansal, Avi Schwarzschild, Eitan Borgnia, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking. CoRR (2022)
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. NeurIPS (2021)
Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. ICML (2021)
Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein
Adversarial attacks on machine learning systems for high-frequency trading. ICAIF (2021)
Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. CoRR (2021)
Avi Schwarzschild, Arjun Gupta, Micah Goldblum, Tom Goldstein
Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems. CoRR (2021)
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training. CoRR (2021)
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
Datasets for Studying Generalization from Easy to Hard Examples. CoRR (2021)
Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein
Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks. CoRR (2021)
Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu
Headless Horseman: Adversarial Attacks on Transfer Learning Models. ICASSP (2020)
Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein
Truth or backpropaganda? An empirical investigation of deep learning theory. ICLR (2020)
Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein
Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks. CoRR (2020)
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, Tom Goldstein
Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses. CoRR (2020)
Micah Goldblum, Avi Schwarzschild, Naftali Cohen, Tucker Balch, Ankit B. Patel, Tom Goldstein
Adversarial Attacks on Machine Learning Systems for High-Frequency Trading. CoRR (2020)
Ahmed Abdelkader, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, Chen Zhu
Headless Horseman: Adversarial Attacks on Transfer Learning Models. CoRR (2020)
Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory. CoRR (2019)