Xuechen Li

According to our database, Xuechen Li authored at least 15 papers between 2018 and 2024.

Collaborative distances:

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

2024

Linguistic Calibration of Language Models.

Neil Band, Xuechen Li, Tengyu Ma, Tatsunori Hashimoto

CoRR, 2024

Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks.

Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto

Proceedings of the IEEE Security and Privacy, 2024

Linguistic Calibration of Long-Form Generations.

Neil Band, Xuechen Li, Tengyu Ma, Tatsunori Hashimoto

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Holistic Evaluation of Language Models.

Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, Benjamin Newman, Binhang Yuan, Bobby Yan, Ce Zhang, Christian Cosgrove, Christopher D. Manning, Christopher Ré, Diana Acosta-Navas, Drew A. Hudson, Eric Zelikman, Esin Durmus, Faisal Ladhak, Frieda Rong, Hongyu Ren, Huaxiu Yao, Jue Wang, Keshav Santhanam, Laurel J. Orr, Lucia Zheng, Mert Yüksekgönül, Mirac Suzgun, Nathan Kim, Neel Guha, Niladri S. Chatterji, Omar Khattab, Peter Henderson, Qian Huang, Ryan Chi, Sang Michael Xie, Shibani Santurkar, Surya Ganguli, Tatsunori Hashimoto, Thomas Icard, Tianyi Zhang, Vishrav Chaudhary, William Wang, Xuechen Li, Yifan Mai, Yuhui Zhang, Yuta Koreeda

Trans. Mach. Learn. Res., 2023

Foundation Models and Fair Use.

Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang

J. Mach. Learn. Res., 2023

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback.

Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto

CoRR, 2023

2022

A Closer Look at the Calibration of Differentially Private Learners.

Hanlin Zhang, Xuechen Li, Prithviraj Sen, Salim Roukos, Tatsunori Hashimoto

CoRR, 2022

When Does Differentially Private Learning Not Suffer in High Dimensions?

Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Large Language Models Can Be Strong Differentially Private Learners.

Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto

Proceedings of the Tenth International Conference on Learning Representations, 2022

Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations.

Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

When does preconditioning help or hurt generalization?

Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Scalable Gradients for Stochastic Differential Equations.

Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Scalable Gradients and Variational Inference for Stochastic Differential Equations.

Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud

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

2018

Isolating Sources of Disentanglement in Variational Autoencoders.

Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Inference Suboptimality in Variational Autoencoders.

Chris Cremer, Xuechen Li, David Duvenaud

Proceedings of the 35th International Conference on Machine Learning, 2018