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Publication search results
found 108 matches
- 2024
- Hansa Srinivasan, Candice Schumann, Aradhana Sinha, David Madras, Gbolahan Oluwafemi Olanubi, Alex Beutel, Susanna Ricco, Jilin Chen:
Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images. CoRR abs/2401.14322 (2024) - 2023
- Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Ed H. Chi, Jilin Chen, Alex Beutel:
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation. EMNLP (Findings) 2023: 127-139 - Preethi Lahoti, Nicholas Blumm, Xiao Ma, Raghavendra Kotikalapudi, Sahitya Potluri, Qijun Tan, Hansa Srinivasan, Ben Packer, Ahmad Beirami, Alex Beutel, Jilin Chen:
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting. EMNLP 2023: 10383-10405 - Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. NeurIPS 2023 - Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi:
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders. RecSys 2023: 1049-1053 - Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, Yao Qin:
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data. CoRR abs/2302.01381 (2023) - Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan, Ed H. Chi, Alex Beutel:
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel. CoRR abs/2302.11188 (2023) - Jindong Gu, Ahmad Beirami, Xuezhi Wang, Alex Beutel, Philip H. S. Torr, Yao Qin:
Towards Robust Prompts on Vision-Language Models. CoRR abs/2304.08479 (2023) - Ananth Balashankar, Xuezhi Wang, Yao Qin, Ben Packer, Nithum Thain, Jilin Chen, Ed H. Chi, Alex Beutel:
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals. CoRR abs/2305.13535 (2023) - Xiao Ma, Swaroop Mishra, Ahmad Beirami, Alex Beutel, Jilin Chen:
Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning. CoRR abs/2306.14308 (2023) - James Atwood, Tina Tian, Ben Packer, Meghana Deodhar, Jilin Chen, Alex Beutel, Flavien Prost, Ahmad Beirami:
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification. CoRR abs/2307.05728 (2023) - Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi:
Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders. CoRR abs/2308.12256 (2023) - Preethi Lahoti, Nicholas Blumm, Xiao Ma, Raghavendra Kotikalapudi, Sahitya Potluri, Qijun Tan, Hansa Srinivasan, Ben Packer, Ahmad Beirami, Alex Beutel, Jilin Chen:
Improving Diversity of Demographic Representation in Large Language Models via Collective-Critiques and Self-Voting. CoRR abs/2310.16523 (2023) - Aradhana Sinha, Ananth Balashankar, Ahmad Beirami, Thi Avrahami, Jilin Chen, Alex Beutel:
Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks. CoRR abs/2310.16955 (2023) - Ananth Balashankar, Xiao Ma, Aradhana Sinha, Ahmad Beirami, Yao Qin, Jilin Chen, Alex Beutel:
Improving Few-shot Generalization of Safety Classifiers via Data Augmented Parameter-Efficient Fine-Tuning. CoRR abs/2310.16959 (2023) - Sidharth Mudgal, Jong Lee, Harish Ganapathy, YaGuang Li, Tao Wang, Yanping Huang, Zhifeng Chen, Heng-Tze Cheng, Michael Collins, Trevor Strohman, Jilin Chen, Alex Beutel, Ahmad Beirami:
Controlled Decoding from Language Models. CoRR abs/2310.17022 (2023) - Lucas Monteiro Paes, Ananda Theertha Suresh, Alex Beutel, Flávio P. Calmon, Ahmad Beirami:
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing. CoRR abs/2312.03867 (2023) - 2022
- Alexander D'Amour, Katherine A. Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yi-An Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley:
Underspecification Presents Challenges for Credibility in Modern Machine Learning. J. Mach. Learn. Res. 23: 226:1-226:61 (2022) - Meghana Deodhar, Xiao Ma, Yixin Cai, Alex Koes, Alex Beutel, Jilin Chen:
A human-ML collaboration framework for improving video content reviews. CIKM Workshops 2022 - Yao Qin, Chiyuan Zhang, Ting Chen, Balaji Lakshminarayanan, Alex Beutel, Xuezhi Wang:
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation. NeurIPS 2022 - Zee Fryer, Vera Axelrod, Ben Packer, Alex Beutel, Jilin Chen, Kellie Webster:
Flexible text generation for counterfactual fairness probing. CoRR abs/2206.13757 (2022) - Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nick Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel:
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations. CoRR abs/2210.07755 (2022) - Meghana Deodhar, Xiao Ma, Yixin Cai, Alex Koes, Alex Beutel, Jilin Chen:
A Human-ML Collaboration Framework for Improving Video Content Reviews. CoRR abs/2210.09500 (2022) - Esther Rolf, Ben Packer, Alex Beutel, Fernando Diaz:
Striving for data-model efficiency: Identifying data externalities on group performance. CoRR abs/2211.06348 (2022) - 2021
- Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. AIES 2021: 873-883 - Ananth Balashankar, Xuezhi Wang, Ben Packer, Nithum Thain, Ed H. Chi, Alex Beutel:
Can We Improve Model Robustness through Secondary Attribute Counterfactuals? EMNLP (1) 2021: 4701-4712 - Pranjal Awasthi, Alex Beutel, Matthäus Kleindessner, Jamie Morgenstern, Xuezhi Wang:
Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information. FAccT 2021: 206-214 - Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi:
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. KDD 2021: 1748-1757 - Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi:
Improving Calibration through the Relationship with Adversarial Robustness. NeurIPS 2021: 14358-14369 - Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel:
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems. WSDM 2021: 436-444
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