Avi Schwarzschild

According to our database, Avi Schwarzschild authored at least 29 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

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

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

CoRR, 2024

Rethinking LLM Memorization through the Lens of Adversarial Compression.

Avi Schwarzschild, Zhili Feng, Pratyush Maini, Zachary C. Lipton, J. Zico Kolter

CoRR, 2024

Forcing Diffuse Distributions out of Language Models.

Yiming Zhang, Avi Schwarzschild, Nicholas Carlini, Zico Kolter, Daphne Ippolito

CoRR, 2024

Benchmarking ChatGPT on Algorithmic Reasoning.

Sean McLeish, Avi Schwarzschild, Tom Goldstein

CoRR, 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

CoRR, 2024

TOFU: A Task of Fictitious Unlearning for LLMs.

Pratyush Maini, Zhili Feng, Avi Schwarzschild, Zachary C. Lipton, J. Zico Kolter

CoRR, 2024

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, Tom Goldstein

IEEE Trans. Pattern Anal. Mach. Intell., 2023

Effective Backdoor Mitigation Depends on the Pre-training Objective.

Sahil Verma, Gantavya Bhatt, Avi Schwarzschild, Soumye Singhal, Arnav Mohanty Das, Chirag Shah, John P. Dickerson, Jeff A. Bilmes

CoRR, 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, Tom Goldstein

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

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, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, Micah Goldblum

CoRR, 2023

Neural Auctions Compromise Bidder Information.

Alex Stein, Avi Schwarzschild, Michael J. Curry, Tom Goldstein, John P. Dickerson

CoRR, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Universal Guidance for Diffusion Models.

Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

Reckoning with the Disagreement Problem: Explanation Consensus as a Training Objective.

Avi Schwarzschild, Max Cembalest, Karthik Rao, Keegan Hines, John P. Dickerson

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

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

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Uncanny Similarity of Recurrence and Depth.

Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein

Proceedings of the Tenth International Conference on Learning Representations, 2022

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

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data.

Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, 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

Thinking Deeply with Recurrence: Generalizing from Easy to Hard Sequential Reasoning Problems.

Avi Schwarzschild, Arjun Gupta, Micah Goldblum, Tom Goldstein

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 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

Proceedings of the 38th International Conference on Machine Learning, 2021

Adversarial attacks on machine learning systems for high-frequency trading.

Micah Goldblum, Avi Schwarzschild, Ankit B. Patel, Tom Goldstein

Proceedings of the ICAIF'21: 2nd ACM International Conference on AI in Finance, Virtual Event, November 3, 2021

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

Truth or backpropaganda? An empirical investigation of deep learning theory.

Micah Goldblum, Jonas Geiping, Avi Schwarzschild, Michael Moeller, Tom Goldstein

Proceedings of the 8th International Conference on Learning Representations, 2020

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

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020