Valeriia Cherepanova
Valeriia Cherepanova
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Cited by
Cited by
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
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
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
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
Transfer Learning with Deep Tabular Models
R Levin, V Cherepanova, A Schwarzschild, A Bansal, CB Bruss, ...
International Conference on Learning Representations 2023, 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
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
Technical challenges for training fair neural networks
V Cherepanova, V Nanda, M Goldblum, JP Dickerson, T Goldstein
arXiv preprint arXiv:2102.06764, 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
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
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
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
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
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
Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs
V Cherepanova, J Zou
arXiv preprint arXiv:2404.17120, 2024
Adversarial Robustness and Fairness in Deep Learning
V Cherepanova
University of Maryland, College Park, 2023
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