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Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning

Jun 04, 2021
Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi

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Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective

May 20, 2021
Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel

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Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities

May 06, 2021
Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen

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Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Feb 16, 2021
Pranjal Awasthi, Alex Beutel, Matthaeus Kleindessner, Jamie Morgenstern, Xuezhi Wang

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Measuring Recommender System Effects with Simulated Users

Jan 12, 2021
Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel

* Presented at Second Workshop on Fairness, Accountability, Transparency, Ethics and Society on the Web (FATES 2020) with the title "Beyond Next Step Bias: Trajectory Simulation for Understanding Recommender System Behavior" 

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Learned Indexes for a Google-scale Disk-based Database

Dec 23, 2020
Hussam Abu-Libdeh, Deniz Altınbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou, Li, Andy Ly, Christopher Olston

* 4 pages, Presented at Workshop on ML for Systems at NeurIPS 2020 

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Underspecification Presents Challenges for Credibility in Modern Machine Learning

Nov 06, 2020
Alexander D'Amour, Katherine 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, Yian Ma, Cory 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

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Measuring and Reducing Gendered Correlations in Pre-trained Models

Oct 12, 2020
Kellie Webster, Xuezhi Wang, Ian Tenney, Alex Beutel, Emily Pitler, Ellie Pavlick, Jilin Chen, Slav Petrov

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CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation

Oct 05, 2020
Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed Chi

* 6 pages, accepted to EMNLP 2020 

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Improving Uncertainty Estimates through the Relationship with Adversarial Robustness

Jun 29, 2020
Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi

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Fairness without Demographics through Adversarially Reweighted Learning

Jun 24, 2020
Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi

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Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems

Nov 06, 2019
Xuezhi Wang, Nithum Thain, Anu Sinha, Ed H. Chi, Jilin Chen, Alex Beutel

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Toward a better trade-off between performance and fairness with kernel-based distribution matching

Oct 25, 2019
Flavien Prost, Hai Qian, Qiuwen Chen, Ed H. Chi, Jilin Chen, Alex Beutel

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Transfer of Machine Learning Fairness across Domains

Jun 26, 2019
Candice Schumann, Xuezhi Wang, Alex Beutel, Jilin Chen, Hai Qian, Ed H. Chi

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Fairness in Recommendation Ranking through Pairwise Comparisons

Mar 02, 2019
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, Cristos Goodrow

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Towards Neural Mixture Recommender for Long Range Dependent User Sequences

Feb 22, 2019
Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi

* Accepted at WWW 2019 

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Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements

Jan 14, 2019
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi

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Top-K Off-Policy Correction for a REINFORCE Recommender System

Dec 06, 2018
Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed Chi

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Counterfactual Fairness in Text Classification through Robustness

Sep 27, 2018
Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel

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The Case for Learned Index Structures

Apr 30, 2018
Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, Neoklis Polyzotis

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The Many Faces of Link Fraud

Sep 11, 2017
Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos

* "full" version of the ICDM2017 short paper, "The Many Faces of Link Fraud" 

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Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations

Jul 07, 2017
Alex Beutel, Jilin Chen, Zhe Zhao, Ed H. Chi

* Presented as a poster at the 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017) 

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BIRDNEST: Bayesian Inference for Ratings-Fraud Detection

Mar 07, 2016
Bryan Hooi, Neil Shah, Alex Beutel, Stephan Gunnemann, Leman Akoglu, Mohit Kumar, Disha Makhija, Christos Faloutsos

* 9 pages; v2: minor typos corrected 

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Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering

Dec 06, 2015
Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola

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ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly

Dec 31, 2014
Alex Beutel, Amr Ahmed, Alexander J. Smola

* 22 pages, under review for conference publication 

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Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective

Oct 15, 2014
Neil Shah, Alex Beutel, Brian Gallagher, Christos Faloutsos

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