Avi Schwarzschild
Avi Schwarzschild
Verified email at - Homepage
Cited by
Cited by
Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses
M Goldblum, D Tsipras, C Xie, X Chen, A Schwarzschild, D Song, ...
IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 1563-1580, 2022
Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
G Somepalli, M Goldblum, A Schwarzschild, CB Bruss, T Goldstein
arXiv preprint arXiv:2106.01342, 2021
Just how toxic is data poisoning? a unified benchmark for backdoor and data poisoning attacks
A Schwarzschild, M Goldblum, A Gupta, JP Dickerson, T Goldstein
International Conference on Machine Learning (ICML) 2021, 2020
Baseline defenses for adversarial attacks against aligned language models
N Jain, A Schwarzschild, Y Wen, G Somepalli, J Kirchenbauer, P Chiang, ...
arXiv preprint arXiv:2309.00614, 2023
Universal guidance for diffusion models
A Bansal, HM Chu, A Schwarzschild, S Sengupta, M Goldblum, J Geiping, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023
Can you learn an algorithm? generalizing from easy to hard problems with recurrent networks
A Schwarzschild, E Borgnia, A Gupta, F Huang, U Vishkin, M Goldblum, ...
Advances in Neural Information Processing Systems 34, 6695-6706, 2021
Transfer learning with deep tabular models
R Levin, V Cherepanova, A Schwarzschild, A Bansal, CB Bruss, ...
arXiv preprint arXiv:2206.15306, 2022
Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann LeCun, and Micah Goldblum
R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ...
A cookbook of self-supervised learning 2, 2023
Truth or backpropaganda? An empirical investigation of deep learning theory
M Goldblum, J Geiping, A Schwarzschild, M Moeller, T Goldstein
International Conference on Learning Representations (ICLR) 2020, 2019
Tofu: A task of fictitious unlearning for llms
P Maini, Z Feng, A Schwarzschild, ZC Lipton, JZ Kolter
arXiv preprint arXiv:2401.06121, 2024
End-to-end Algorithm Synthesis with Recurrent Networks: Logical Extrapolation Without Overthinking
A Bansal, A Schwarzschild, E Borgnia, Z Emam, F Huang, M Goldblum, ...
36th Conference on Neural Information Processing Systems (NeurIPS 2022), 2022
Neftune: Noisy embeddings improve instruction finetuning
N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ...
arXiv preprint arXiv:2310.05914, 2023
Adversarial attacks on machine learning systems for high-frequency trading
M Goldblum, A Schwarzschild, A Patel, T Goldstein
Proceedings of the Second ACM International Conference on AI in Finance, 1-9, 2021
Spotting llms with binoculars: Zero-shot detection of machine-generated text
A Hans, A Schwarzschild, V Cherepanova, H Kazemi, A Saha, ...
arXiv preprint arXiv:2401.12070, 2024
The Uncanny Similarity of Recurrence and Depth
A Schwarzschild, A Gupta, M Goldblum, T Goldstein
International Conference on Learning Representations (ICLR) 2022, 2022
Datasets for studying generalization from easy to hard examples
A Schwarzschild, E Borgnia, A Gupta, A Bansal, Z Emam, F Huang, ...
arXiv preprint arXiv:2108.06011, 2021
MetaBalance: high-performance neural networks for class-imbalanced data
A Bansal, M Goldblum, V Cherepanova, A Schwarzschild, CB Bruss, ...
arXiv preprint arXiv:2106.09643, 2021
Headless horseman: Adversarial attacks on transfer learning models
A Abdelkader, MJ Curry, L Fowl, T Goldstein, A Schwarzschild, M Shu, ...
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020
Rethinking llm memorization through the lens of adversarial compression
A Schwarzschild, Z Feng, P Maini, ZC Lipton, JZ Kolter
arXiv preprint arXiv:2404.15146, 2024
Reckoning with the disagreement problem: Explanation consensus as a training objective
A Schwarzschild, M Cembalest, K Rao, K Hines, J Dickerson
Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 662-678, 2023
The system can't perform the operation now. Try again later.
Articles 1–20