| 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 | 153 | 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 | 144 | 2021 |
| 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 | 90 | 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 | 87 | 2020 |
| Transfer learning with deep tabular models R Levin, V Cherepanova, A Schwarzschild, A Bansal, CB Bruss, ... International Conference on Learning Representations 2023, 2023 | 77* | 2023 |
| 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 | 60 | 2020 |
| Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text A Hans, A Schwarzschild, V Cherepanova, H Kazemi, A Saha, ... International Conference on Machine Learning, 2024 | 55* | 2024 |
| 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, ... ICLR 2021 Workshop on Security and Safety in Machine Learning Systems, 2021 | 54 | 2021 |
| Technical challenges for training fair neural networks V Cherepanova, V Nanda, M Goldblum, JP Dickerson, T Goldstein ICLR workshop on Responsible AI, 2021 | 23 | 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 | 19 | 2023 |
| 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 | 18 | 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, 41956-41979, 2023 | 13 | 2023 |
| 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 | 11 | 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 | 10 | 2021 |
| Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs V Cherepanova, J Zou ICML 2024 Workshop on the Next Generation of AI Safety, 2024 | 7 | 2024 |
| Improving LLM Group Fairness on Tabular Data via In-Context Learning V Cherepanova, CJ Lee, NJ Akpinar, R Fogliato, MA Bertran, M Kearns, ... arXiv preprint arXiv:2412.04642, 2024 | 1 | 2024 |
| Adversarial Robustness and Fairness in Deep Learning V Cherepanova University of Maryland, College Park, 2023 | | 2023 |
| Has My System Prompt Been Used? Large Language Model Prompt Membership Inference R Levin, V Cherepanova, A Hans, A Schwarzschild, T Goldstein Neurips Safe Generative AI Workshop 2024, 0 | | |
