Sang Michael Xie

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 38

Top Topics

Weakly Supervised

Kernel Learning

Top Venues

Publications

Helen Qu, Sang Michael Xie
Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations. CoRR (2024)
Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang
A Survey on Data Selection for Language Models. CoRR (2024)
Alon Albalak, Yanai Elazar, Sang Michael Xie, Shayne Longpre, Nathan Lambert, Xinyi Wang, Niklas Muennighoff, Bairu Hou, Liangming Pan, Haewon Jeong, Colin Raffel, Shiyu Chang, Tatsunori Hashimoto, William Yang Wang
A Survey on Data Selection for Language Models. Trans. Mach. Learn. Res. 2024 (2024)
Sören Arlt, Haonan Duan, Felix Li, Sang Michael Xie, Yuhuai Wu, Mario Krenn
Meta-Designing Quantum Experiments with Language Models. CoRR (2024)
Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang
Data Selection for Language Models via Importance Resampling. CoRR (2023)
Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models. ICML (2023)
Minae Kwon, Sang Michael Xie, Kalesha Bullard, Dorsa Sadigh
Reward Design with Language Models. CoRR (2023)
Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy S. Liang, Quoc V. Le, Tengyu Ma, Adams Wei Yu
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining. NeurIPS (2023)
Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, Adams Wei Yu
DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining. CoRR (2023)
Minae Kwon, Sang Michael Xie, Kalesha Bullard, Dorsa Sadigh
Reward Design with Language Models. ICLR (2023)
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
Holistic Evaluation of Language Models. Trans. Mach. Learn. Res. 2023 (2023)
Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang
Data Selection for Language Models via Importance Resampling. NeurIPS (2023)
Kendrick Shen, Robbie Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. HaoChen, Tengyu Ma, Percy Liang
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation. CoRR (2022)
Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models. CoRR (2022)
Kendrick Shen, Robbie M. Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. HaoChen, Tengyu Ma, Percy Liang
Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation. ICML (2022)
Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma
An Explanation of In-context Learning as Implicit Bayesian Inference. ICLR (2022)
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
Holistic Evaluation of Language Models. CoRR (2022)
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
Extending the WILDS Benchmark for Unsupervised Adaptation. ICLR (2022)
Colin Wei, Sang Michael Xie, Tengyu Ma
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning. NeurIPS (2021)
Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma
An Explanation of In-context Learning as Implicit Bayesian Inference. CoRR (2021)
Colin Wei, Sang Michael Xie, Tengyu Ma
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning. CoRR (2021)
Fahim Tajwar, Ananya Kumar, Sang Michael Xie, Percy Liang
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets. CoRR (2021)
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara M. Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang
WILDS: A Benchmark of in-the-Wild Distribution Shifts. ICML (2021)
Sang Michael Xie, Tengyu Ma, Percy Liang
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization. ICML (2021)
Sang Michael Xie, Ananya Kumar, Robbie Jones, Fereshte Khani, Tengyu Ma, Percy Liang
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness. ICLR (2021)
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
Extending the WILDS Benchmark for Unsupervised Adaptation. CoRR (2021)
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang
WILDS: A Benchmark of in-the-Wild Distribution Shifts. CoRR (2020)
Sang Michael Xie, Tengyu Ma, Percy Liang
Simplifying Models with Unlabeled Output Data. CoRR (2020)
Sherrie Wang, William Chen, Sang Michael Xie, George Azzari, David B. Lobell
Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery. Remote. Sens. 12 (2) (2020)
Sang Michael Xie, Ananya Kumar, Robbie Jones, Fereshte Khani, Tengyu Ma, Percy Liang
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness. CoRR (2020)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Understanding and Mitigating the Tradeoff between Robustness and Accuracy. ICML (2020)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. CoRR (2020)
Sang Michael Xie, Stefano Ermon
Differentiable Subset Sampling. CoRR (2019)
Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang
Adversarial Training Can Hurt Generalization. CoRR (2019)
Sang Michael Xie, Stefano Ermon
Reparameterizable Subset Sampling via Continuous Relaxations. IJCAI (2019)
Neal Jean, Sang Michael Xie, Stefano Ermon
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. NeurIPS (2018)
Neal Jean, Sang Michael Xie, Stefano Ermon
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. CoRR (2018)
Sang Michael Xie, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. AAAI (2016)
Sang Michael Xie, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. CoRR (2015)