Rapid Model Comparison by Amortizing Across Models. - Lily H. Zhang; Michael C. Hughes (AABI, 2019)

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models. - Lily H. Zhang; Mark Goldstein; Rajesh Ranganath (ICML, 2021)

Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations. - Aahlad Manas Puli; Lily H. Zhang; Eric K. Oermann; Rajesh Ranganath (CoRR, 2021)

Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations. - Aahlad Manas Puli; Lily H. Zhang; Eric Karl Oermann; Rajesh Ranganath (ICLR, 2022)

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets. - Lily H. Zhang; Veronica Tozzo; John M. Higgins; Rajesh Ranganath (ICML, 2022)

Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection. - Lily H. Zhang; Rajesh Ranganath (AAAI, 2023)

When More is Less: Incorporating Additional Datasets Can Hurt Performance By Introducing Spurious Correlations. - Rhys Compton; Lily H. Zhang; Aahlad Manas Puli; Rajesh Ranganath (MLHC, 2023)

Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy. - Aahlad Manas Puli; Lily H. Zhang; Yoav Wald; Rajesh Ranganath (NeurIPS, 2023)

Preference Learning Algorithms Do Not Learn Preference Rankings. - Angelica Chen; Sadhika Malladi; Lily H. Zhang; Xinyi Chen 001; Qiuyi (Richard) Zhang; Rajesh Ranganath; Kyunghyun Cho (NeurIPS, 2024)

Learning from others' mistakes: Finetuning machine translation models with span-level error annotations. - Lily H. Zhang; Hamid Dadkhahi; Mara Finkelstein; Firas Trabelsi; Jiaming Luo; Markus Freitag (CoRR, 2024)

Robust anomaly detection for particle physics using multi-background representation learning. - Abhijith Gandrakota; Lily H. Zhang; Aahlad Manas Puli; Kyle Cranmer; Jennifer Ngadiuba; Rajesh Ranganath; Nhan Tran (Mach. Learn. Sci. Technol., 2024)

Towards Minimal Targeted Updates of Language Models with Targeted Negative Training. - Lily H. Zhang; Rajesh Ranganath; Arya Tafvizi (Trans. Mach. Learn. Res., 2024)

Preference learning made easy: Everything should be understood through win rate. - Lily H. Zhang; Rajesh Ranganath (CoRR, 2025)