Xuechen Li - RankMe

26 papers A* 6A 2Journal 15Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	—	`conf`	Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. SP (Workshops) Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto
2024	J	`jnl`	Linguistic Calibration of Language Models. CoRR Neil Band, Xuechen Li, Tengyu Ma, Tatsunori Hashimoto
2024	A*	`conf`	Linguistic Calibration of Long-Form Generations. ICML Neil Band, Xuechen Li, Tengyu Ma, Tatsunori Hashimoto
2023	J	`jnl`	AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback. CoRR Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto
2023	J	`jnl`	Exploiting Programmatic Behavior of LLMs: Dual-Use Through Standard Security Attacks. CoRR Daniel Kang, Xuechen Li, Ion Stoica, Carlos Guestrin, Matei Zaharia, Tatsunori Hashimoto
2023	J	`jnl`	Foundation Models and Fair Use. CoRR Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang
2023	J	`jnl`	Foundation Models and Fair Use. J. Mach. Learn. Res. Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang
2023	J	`jnl`	Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 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
2022	J	`jnl`	A Closer Look at the Calibration of Differentially Private Learners. CoRR Hanlin Zhang, Xuechen Li, Prithviraj Sen, Salim Roukos, Tatsunori Hashimoto
2022	J	`jnl`	Holistic Evaluation of Language Models. CoRR 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
2022	A	`conf`	Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. AISTATS Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
2022	A*	`conf`	Large Language Models Can Be Strong Differentially Private Learners. ICLR Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto
2022	A*	`conf`	When Does Differentially Private Learning Not Suffer in High Dimensions? NeurIPS Xuechen Li, Daogao Liu, Tatsunori B. Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
2022	J	`jnl`	When Does Differentially Private Learning Not Suffer in High Dimensions? CoRR Xuechen Li, Daogao Liu, Tatsunori Hashimoto, Huseyin A. Inan, Janardhan Kulkarni, Yin Tat Lee, Abhradeep Guha Thakurta
2021	J	`jnl`	Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations. CoRR Winnie Xu, Ricky T. Q. Chen, Xuechen Li, David Duvenaud
2021	J	`jnl`	Large Language Models Can Be Strong Differentially Private Learners. CoRR Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto
2021	A*	`conf`	When does preconditioning help or hurt generalization? ICLR Shun-ichi Amari, Jimmy Ba, Roger Baker Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
2020	A	`conf`	Scalable Gradients for Stochastic Differential Equations. AISTATS Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2020	J	`jnl`	Scalable Gradients for Stochastic Differential Equations. CoRR Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2020	J	`jnl`	When Does Preconditioning Help or Hurt Generalization? CoRR Shun-ichi Amari, Jimmy Ba, Roger B. Grosse, Xuechen Li, Atsushi Nitanda, Taiji Suzuki, Denny Wu, Ji Xu
2019	—	`conf`	Scalable Gradients and Variational Inference for Stochastic Differential Equations. AABI Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David Duvenaud
2018	A*	`conf`	Inference Suboptimality in Variational Autoencoders. ICML Chris Cremer, Xuechen Li, David Duvenaud
2018	J	`jnl`	Inference Suboptimality in Variational Autoencoders. CoRR Chris Cremer, Xuechen Li, David Duvenaud
2018	—	`conf`	Isolating Sources of Disentanglement in Variational Autoencoders. ICLR (Workshop) Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
2018	A*	`conf`	Isolating Sources of Disentanglement in Variational Autoencoders. NeurIPS Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud
2018	J	`jnl`	Isolating Sources of Disentanglement in Variational Autoencoders. CoRR Tian Qi Chen, Xuechen Li, Roger B. Grosse, David Duvenaud