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Sang Michael Xie
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2020 – today
- 2024
- [j3]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) - [c16]Helen Qu, Sang Michael Xie:
Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations. ICML 2024 - [i21]Helen Qu, Sang Michael Xie:
Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations. CoRR abs/2402.03325 (2024) - [i20]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 abs/2402.16827 (2024) - [i19]Sören Arlt, Haonan Duan, Felix Li, Sang Michael Xie, Yuhuai Wu, Mario Krenn:
Meta-Designing Quantum Experiments with Language Models. CoRR abs/2406.02470 (2024) - 2023
- [j2]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) - [c15]Minae Kwon, Sang Michael Xie, Kalesha Bullard, Dorsa Sadigh:
Reward Design with Language Models. ICLR 2023 - [c14]Hong Liu, Sang Michael Xie, Zhiyuan Li
, Tengyu Ma:
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models. ICML 2023: 22188-22214 - [c13]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. NeurIPS 2023 - [c12]Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang:
Data Selection for Language Models via Importance Resampling. NeurIPS 2023 - [i18]Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang:
Data Selection for Language Models via Importance Resampling. CoRR abs/2302.03169 (2023) - [i17]Minae Kwon, Sang Michael Xie, Kalesha Bullard, Dorsa Sadigh:
Reward Design with Language Models. CoRR abs/2303.00001 (2023) - [i16]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 abs/2305.10429 (2023) - 2022
- [c11]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
Extending the WILDS Benchmark for Unsupervised Adaptation. ICLR 2022 - [c10]Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma:
An Explanation of In-context Learning as Implicit Bayesian Inference. ICLR 2022 - [c9]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: 19847-19878 - [i15]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 abs/2204.00570 (2022) - [i14]Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma:
Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models. CoRR abs/2210.14199 (2022) - [i13]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
Holistic Evaluation of Language Models. CoRR abs/2211.09110 (2022) - 2021
- [c8]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 - [c7]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 S. 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: 5637-5664 - [c6]Sang Michael Xie, Tengyu Ma, Percy Liang:
Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization. ICML 2021: 11424-11435 - [c5]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: 16158-16170 - [i12]Colin Wei, Sang Michael Xie, Tengyu Ma:
Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning. CoRR abs/2106.09226 (2021) - [i11]Fahim Tajwar, Ananya Kumar, Sang Michael Xie, Percy Liang:
No True State-of-the-Art? OOD Detection Methods are Inconsistent across Datasets. CoRR abs/2109.05554 (2021) - [i10]Sang Michael Xie, Aditi Raghunathan, Percy Liang, Tengyu Ma:
An Explanation of In-context Learning as Implicit Bayesian Inference. CoRR abs/2111.02080 (2021) - [i9]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 abs/2112.05090 (2021) - 2020
- [j1]Sherrie Wang
Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery. Remote. Sens. 12(2): 207 (2020) - [c4]Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang:
Understanding and Mitigating the Tradeoff between Robustness and Accuracy. ICML 2020: 7909-7919 - [i8]Aditi Raghunathan, Sang Michael Xie
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. CoRR abs/2002.10716 (2020) - [i7]Sang Michael Xie, Tengyu Ma, Percy Liang:
Simplifying Models with Unlabeled Output Data. CoRR abs/2006.16205 (2020) - [i6]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 abs/2012.04550 (2020) - [i5]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 abs/2012.07421 (2020)
2010 – 2019
- 2019
- [c3]Sang Michael Xie, Stefano Ermon:
Reparameterizable Subset Sampling via Continuous Relaxations. IJCAI 2019: 3919-3925 - [i4]Sang Michael Xie, Stefano Ermon:
Differentiable Subset Sampling. CoRR abs/1901.10517 (2019) - [i3]Aditi Raghunathan, Sang Michael Xie
Adversarial Training Can Hurt Generalization. CoRR abs/1906.06032 (2019) - 2018
- [c2]Neal Jean, Sang Michael Xie, Stefano Ermon:
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. NeurIPS 2018: 5327-5338 - [i2]Neal Jean, Sang Michael Xie, Stefano Ermon:
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance. CoRR abs/1805.10407 (2018) - 2016
- [c1]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: 3929-3935 - 2015
- [i1]Sang Michael Xie, Neal Jean, Marshall Burke, David B. Lobell, Stefano Ermon:
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. CoRR abs/1510.00098 (2015)
Coauthor Index
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last updated on 2024-10-07 21:16 CEST by the dblp team
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