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2020 – today
- 2024
[c11]Yilan Chen, Wei Huang, Lily Weng:
Provable and Efficient Dataset Distillation for Kernel Ridge Regression. NeurIPS 2024- [c10]Divyansh Srivastava, Ge Yan, Lily Weng:
VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance. NeurIPS 2024 - [c9]Yunshi Wen, Tengfei Ma, Lily Weng, Lam M. Nguyen, Anak Agung Julius:
Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification. NeurIPS 2024 - [c8]Pei-Yau Weng, Minh Hoang, Lam Nguyen, My T. Thai, Lily Weng, Trong Nghia Hoang:
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data. NeurIPS 2024 - 2023
- [c7]Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng:
Analyzing Generalization of Neural Networks through Loss Path Kernels. NeurIPS 2023 - 2022
- [c6]Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng:
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. ICML 2022: 11387-11412 - [i2]Alex Gu, Songtao Lu, Parikshit Ram, Lily Weng:
Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning. CoRR abs/2203.01924 (2022) - 2021
- [c5]Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Luca Daniel:
Fast Training of Provably Robust Neural Networks by SingleProp. AAAI 2021: 6803-6811 - [c4]Jeet Mohapatra, Ching-Yun Ko, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel:
Hidden Cost of Randomized Smoothing. AISTATS 2021: 4033-4041 - [i1]Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng:
Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework. CoRR abs/2112.04468 (2021) - 2020
- [c3]Yuh-Shyang Wang, Lily Weng, Luca Daniel:
Neural Network Control Policy Verification With Persistent Adversarial Perturbation. ICML 2020: 10050-10059
2010 – 2019
- 2019
- [c2]Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin:
POPQORN: Quantifying Robustness of Recurrent Neural Networks. ICML 2019: 3468-3477 - [c1]Lily Weng, Pin-Yu Chen, Lam M. Nguyen, Mark S. Squillante, Akhilan Boopathy, Ivan V. Oseledets, Luca Daniel:
PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach. ICML 2019: 6727-6736
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