Lily Weng

According to our database, Lily Weng authored at least 13 papers between 2019 and 2026.

Collaborative distances:

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

2026

Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions.

Saleh Afroogh, Seyd Ishtiaque Ahmed, Petra Ahrweiler, David Alvarez-Melis, Mansur Maturidi Arief, Emilia Barakova, Falco J. Bargagli-Stoffi, Erdem Biyik, Hanjie Chen, Xiang 'Anthony' Chen, Robert Clements, Keeley Crockett, Amit Dhurandhar, Fethiye Irmak Dogan, Mollie Dollinger, Motahhare Eslami, Aldo A. Faisal, Arya Farahi, Melanie Fernandez Pradie, Saadia Gabrie, Diego Garcia-Olano, Marzyeh Ghassemi, Shaona Ghosh, Hatice Gunes, Ehsan Hajiramezanali, Stefan Haufe, Biwei Huang, Angel Hwang, Md Tauhidul Islam, Junfeng Jiao, Amir-Hossein Karimi, Saber Kazeminasab, Anastasia Kuzminykh, William G. La Cava, Brian Y. Lim, Xiaofeng Liu, Mohammad R. K. Mofrad, Alicia Parrish, María Pérez-Ortiz, Shriti Raj, Swabha Swayamdipta, Salmon Talebi, Kush R. Varshney, Mihaela Vorvoreanu, Lily Weng, Alice Xiang, Yiming Xu, Ding Zhao, Jieyu Zhao

CoRR, February, 2026

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

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Abstracted Shapes as Tokens - A Generalizable and Interpretable Model for Time-series Classification.

Yunshi Wen, Tengfei Ma, Lily Weng, Lam M. Nguyen, Anak Agung Julius

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

VLG-CBM: Training Concept Bottleneck Models with Vision-Language Guidance.

Divyansh Srivastava, Ge Yan, Lily Weng

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Provable and Efficient Dataset Distillation for Kernel Ridge Regression.

Yilan Chen, Wei Huang, Lily Weng

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Analyzing Generalization of Neural Networks through Loss Path Kernels.

Yilan Chen, Wei Huang, Hao Wang, Charlotte Loh, Akash Srivastava, Lam M. Nguyen, Lily Weng

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

2022

Min-Max Bilevel Multi-objective Optimization with Applications in Machine Learning.

Alex Gu, Songtao Lu, Parikshit Ram, Lily Weng

CoRR, 2022

Revisiting Contrastive Learning through the Lens of Neighborhood Component Analysis: an Integrated Framework.

Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng

Proceedings of the International Conference on Machine Learning, 2022

2021

Hidden Cost of Randomized Smoothing.

Jeet Mohapatra, Ching-Yun Ko, Lily Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Fast Training of Provably Robust Neural Networks by SingleProp.

Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Luca Daniel

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Neural Network Control Policy Verification With Persistent Adversarial Perturbation.

Yuh-Shyang Wang, Lily Weng, Luca Daniel

Proceedings of the 37th International Conference on Machine Learning, 2020

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

Proceedings of the 36th International Conference on Machine Learning, 2019

POPQORN: Quantifying Robustness of Recurrent Neural Networks.

Ching-Yun Ko, Zhaoyang Lyu, Lily Weng, Luca Daniel, Ngai Wong, Dahua Lin

Proceedings of the 36th International Conference on Machine Learning, 2019