Valeriia Cherepanova

According to our database, Valeriia Cherepanova authored at least 14 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs.

Valeriia Cherepanova, James Zou

CoRR, 2024

TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks.

Benjamin Feuer, Robin Tibor Schirrmeister, Valeriia Cherepanova, Chinmay Hegde, Frank Hutter, Micah Goldblum, Niv Cohen, Colin White

CoRR, 2024

Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text.

Abhimanyu Hans, Avi Schwarzschild, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning.

Valeriia Cherepanova, Roman Levin, Gowthami Somepalli, Jonas Geiping, C. Bayan Bruss, Andrew Gordon Wilson, Tom Goldstein, Micah Goldblum

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Transfer Learning with Deep Tabular Models.

Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Deep Dive into Dataset Imbalance and Bias in Face Identification.

Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, John P. Dickerson, Micah Goldblum, Tom Goldstein

Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 2023

2022

A Deep Dive into Dataset Imbalance and Bias in Face Identification.

Valeriia Cherepanova, Steven Reich, Samuel Dooley, Hossein Souri, Micah Goldblum, Tom Goldstein

CoRR, 2022

2021

Comparing Human and Machine Bias in Face Recognition.

Samuel Dooley, Ryan Downing, George Z. Wei, Nathan Shankar, Bradon Thymes, Gudrun Thorkelsdottir, Tiye Kurtz-Miott, Rachel Mattson, Olufemi Obiwumi, Valeriia Cherepanova, Micah Goldblum, John P. Dickerson, Tom Goldstein

CoRR, 2021

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data.

Arpit Bansal, Micah Goldblum, Valeriia Cherepanova, Avi Schwarzschild, C. Bayan Bruss, Tom Goldstein

CoRR, 2021

DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations.

Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein

CoRR, 2021

Technical Challenges for Training Fair Neural Networks.

Valeriia Cherepanova, Vedant Nanda, Micah Goldblum, John P. Dickerson, Tom Goldstein

CoRR, 2021

LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition.

Valeriia Cherepanova, Micah Goldblum, Harrison Foley, Shiyuan Duan, John P. Dickerson, Gavin Taylor, Tom Goldstein

Proceedings of the 9th International Conference on Learning Representations, 2021

Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff.

Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta

Proceedings of the IEEE International Conference on Acoustics, 2021

2020

Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks.

Micah Goldblum, Steven Reich, Liam Fowl, Renkun Ni, Valeriia Cherepanova, Tom Goldstein

Proceedings of the 37th International Conference on Machine Learning, 2020