Lily H. Zhang

According to our database, Lily H. Zhang authored at least 9 papers between 2019 and 2024.

Collaborative distances:

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

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

CoRR, 2024

2023

Don't blame Dataset Shift! Shortcut Learning due to Gradients and Cross Entropy.

Aahlad Manas Puli, Lily H. Zhang, Yoav Wald, Rajesh Ranganath

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

Proceedings of the Machine Learning for Healthcare Conference, 2023

Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection.

Lily H. Zhang, Rajesh Ranganath

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets.

Lily H. Zhang, Veronica Tozzo, John M. Higgins, Rajesh Ranganath

Proceedings of the International Conference on Machine Learning, 2022

Out-of-distribution Generalization in the Presence of Nuisance-Induced Spurious Correlations.

Aahlad Manas Puli, Lily H. Zhang, Eric Karl Oermann, Rajesh Ranganath

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Predictive Modeling in the Presence of Nuisance-Induced Spurious Correlations.

Aahlad Manas Puli, Lily H. Zhang, Eric K. Oermann, Rajesh Ranganath

CoRR, 2021

Understanding Failures in Out-of-Distribution Detection with Deep Generative Models.

Lily H. Zhang, Mark Goldstein, Rajesh Ranganath

Proceedings of the 38th International Conference on Machine Learning, 2021

2019

Rapid Model Comparison by Amortizing Across Models.

Lily H. Zhang, Michael C. Hughes

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019