LowKey: leveraging adversarial attacks to protect social media users from facial recognition V Cherepanova, M Goldblum, H Foley, S Duan, J Dickerson, G Taylor, ... International Conference on Learning Representations, 2021 | 124 | 2021 |
Strong data augmentation sanitizes poisoning and backdoor attacks without an accuracy tradeoff E Borgnia, V Cherepanova, L Fowl, A Ghiasi, J Geiping, M Goldblum, ... ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 109 | 2021 |
Unraveling meta-learning: Understanding feature representations for few-shot tasks M Goldblum, S Reich, L Fowl, R Ni, V Cherepanova, T Goldstein International Conference on Machine Learning, 3607-3616, 2020 | 77 | 2020 |
Deep learning of HIV field-based rapid tests V Turbé, C Herbst, T Mngomezulu, S Meshkinfamfard, N Dlamini, ... Nature medicine 27 (7), 1165-1170, 2021 | 61 | 2021 |
Transfer Learning with Deep Tabular Models R Levin, V Cherepanova, A Schwarzschild, A Bansal, CB Bruss, ... International Conference on Learning Representations 2023, 2022 | 48* | 2022 |
Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients O Blyuss, A Zaikin, V Cherepanova, D Munblit, EM Kiseleva, ... British journal of cancer 122 (5), 692-696, 2020 | 48 | 2020 |
Dp-instahide: Provably defusing poisoning and backdoor attacks with differentially private data augmentations E Borgnia, J Geiping, V Cherepanova, L Fowl, A Gupta, A Ghiasi, ... arXiv preprint arXiv:2103.02079, 2021 | 40 | 2021 |
Technical challenges for training fair neural networks V Cherepanova, V Nanda, M Goldblum, JP Dickerson, T Goldstein arXiv preprint arXiv:2102.06764, 2021 | 17 | 2021 |
Comparing human and machine bias in face recognition S Dooley, R Downing, G Wei, N Shankar, B Thymes, G Thorkelsdottir, ... arXiv preprint arXiv:2110.08396, 2021 | 13 | 2021 |
A deep dive into dataset imbalance and bias in face identification V Cherepanova, S Reich, S Dooley, H Souri, J Dickerson, M Goldblum, ... Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 229-247, 2023 | 9 | 2023 |
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 | 6* | 2024 |
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 | 5 | 2021 |
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning V Cherepanova, R Levin, G Somepalli, J Geiping, CB Bruss, AG Wilson, ... Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks B Feuer, RT Schirrmeister, V Cherepanova, C Hegde, F Hutter, ... arXiv preprint arXiv:2402.11137, 2024 | 1 | 2024 |
Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs V Cherepanova, J Zou arXiv preprint arXiv:2404.17120, 2024 | | 2024 |
Adversarial Robustness and Fairness in Deep Learning V Cherepanova University of Maryland, College Park, 2023 | | 2023 |