Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks. - Micah Goldblum; Steven Reich; Liam Fowl; Renkun Ni; Valeriia Cherepanova; Tom Goldstein (ICML, 2020)

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 (ICASSP, 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 (ICLR, 2021)

Technical Challenges for Training Fair Neural Networks. - Valeriia Cherepanova; Vedant Nanda; Micah Goldblum; John P. Dickerson; 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)

MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data. - Arpit Bansal; Micah Goldblum; Valeriia Cherepanova; Avi Schwarzschild; C. Bayan Bruss; Tom Goldstein (CoRR, 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; et al. (CoRR, 2021)

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)

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 (AIES, 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 (ICLR, 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 (NeurIPS, 2023)

Adversarial Robustness and Fairness in Deep Learning. - Valeriia Cherepanova (2023)

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 (ICML, 2024)

TuneTables: Context Optimization for Scalable Prior-Data Fitted Networks. - Benjamin Feuer; Robin Schirrmeister; Valeriia Cherepanova; Chinmay Hegde; Frank Hutter; Micah Goldblum; Niv Cohen; Colin White (NeurIPS, 2024)

Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs. - Valeriia Cherepanova; James Zou (CoRR, 2024)

Improving LLM Group Fairness on Tabular Data via In-Context Learning. - Valeriia Cherepanova; Chia-Jung Lee; Nil-Jana Akpinar; Riccardo Fogliato; Martín Bertrán; Michael Kearns; James Zou (CoRR, 2024)

Transformers Boost the Performance of Decision Trees on Tabular Data across Sample Sizes. - Mayuka Jayawardhana; Renbo Tu; Samuel Dooley; Valeriia Cherepanova; Andrew Gordon Wilson; Frank Hutter; Colin White; Tom Goldstein; Micah Goldblum (CoRR, 2025)

Has My System Prompt Been Used? Large Language Model Prompt Membership Inference. - Roman Levin; Valeriia Cherepanova; Abhimanyu Hans; Avi Schwarzschild; Tom Goldstein (CoRR, 2025)