| A cookbook of self-supervised learning (2023) R Balestriero, M Ibrahim, V Sobal, A Morcos, S Shekhar, T Goldstein, ... arXiv preprint arXiv:2304.12210, 0 | 462* | |
| 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 | 386* | 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 | 356 | 2021 |
| 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 | 324* | 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 | 243 | 2023 |
| 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 | 200 | 2020 |
| Tofu: A task of fictitious unlearning for llms P Maini, Z Feng, A Schwarzschild, ZC Lipton, JZ Kolter arXiv preprint arXiv:2401.06121, 2024 | 112* | 2024 |
| Transfer learning with deep tabular models R Levin, V Cherepanova, A Schwarzschild, A Bansal, CB Bruss, ... arXiv preprint arXiv:2206.15306, 2022 | 84* | 2022 |
| 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 | 77 | 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 | 74* | 2024 |
| Neftune: Noisy embeddings improve instruction finetuning N Jain, P Chiang, Y Wen, J Kirchenbauer, HM Chu, G Somepalli, ... arXiv preprint arXiv:2310.05914, 2023 | 72* | 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 | 47 | 2019 |
| 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 | 44* | 2022 |
| 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 | 33* | 2021 |
| Transformers can do arithmetic with the right embeddings S McLeish, A Bansal, A Stein, N Jain, J Kirchenbauer, B Bartoldson, ... Advances in Neural Information Processing Systems 37, 108012-108041, 2025 | 32* | 2025 |
| Rethinking llm memorization through the lens of adversarial compression A Schwarzschild, Z Feng, P Maini, Z Lipton, JZ Kolter Advances in Neural Information Processing Systems 37, 56244-56267, 2025 | 32 | 2025 |
| Avi Schwarzschild, C G Somepalli, M Goldblum Bayan Bruss, and Tom Goldstein. Saint: Improved neural networks for tabular …, 2021 | 28 | 2021 |
| Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2023. Universal guidance for diffusion models A Bansal, HM Chu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 0 | 13 | |
| 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 | 11 | 2021 |
| The Uncanny Similarity of Recurrence and Depth A Schwarzschild, A Gupta, M Goldblum, T Goldstein International Conference on Learning Representations (ICLR) 2022, 2022 | 10 | 2022 |
Micah GoldblumColumbia UniversityE-mail megerősítve itt: columbia.edu