POPQORN: Quantifying Robustness of Recurrent Neural Networks. - Ching-Yun Ko; Zhaoyang Lyu; Lily Weng; Luca Daniel; Ngai Wong 001; Dahua Lin (ICML, 2019)

PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach. - Lily Weng; Pin-Yu Chen; Lam M. Nguyen; Mark S. Squillante; Akhilan Boopathy; Ivan V. Oseledets; Luca Daniel (ICML, 2019)

Neural Network Control Policy Verification With Persistent Adversarial Perturbation. - Yuh-Shyang Wang; Lily Weng; Luca Daniel (ICML, 2020)

Fast Training of Provably Robust Neural Networks by SingleProp. - Akhilan Boopathy; Lily Weng; Sijia Liu 001; Pin-Yu Chen; Gaoyuan Zhang; Luca Daniel (AAAI, 2021)

Hidden Cost of Randomized Smoothing. - Jeet Mohapatra; Ching-Yun Ko; Lily Weng; Pin-Yu Chen; Sijia Liu 001; Luca Daniel (AISTATS, 2021)

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. - Ching-Yun Ko; Jeet Mohapatra; Sijia Liu 001; Pin-Yu Chen; Luca Daniel; Lily Weng (ICML, 2022)

Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning. - Alex Gu; Songtao Lu; Parikshit Ram; Lily Weng (CoRR, 2022)

Analyzing Generalization of Neural Networks through Loss Path Kernels. - Yilan Chen 002; Wei Huang 034; Hao Wang; Charlotte Loh; Akash Srivastava; Lam M. Nguyen; Lily Weng (NeurIPS, 2023)

Provable and Efficient Dataset Distillation for Kernel Ridge Regression. - Yilan Chen 002; Wei Huang 034; Lily Weng (NeurIPS, 2024)

VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance. - Divyansh Srivastava; Ge Yan; Lily Weng (NeurIPS, 2024)

Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification. - Yunshi Wen; Tengfei Ma 001; Lily Weng; Lam M. Nguyen; Anak Agung Julius (NeurIPS, 2024)

Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. - Pei-Yau Weng; Minh Hoang; Lam M. Nguyen; My T. Thai; Lily Weng; Trong Nghia Hoang (NeurIPS, 2024)