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Deep Hash Embedding for Large-Vocab Categorical Feature Representations

Oct 21, 2020
Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi

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DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems

Aug 19, 2020
Ruoxi Wang, Rakesh Shivanna, Derek Z. Cheng, Sagar Jain, Dong Lin, Lichan Hong, Ed H. Chi

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Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval

Aug 19, 2020
Tao Wu, Ellie Ka-In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, Pei Cao

* Accepted at CIKM 2020 

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Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems

Aug 17, 2020
Zhe Chen, Yuyan Wang, Dong Lin, Derek Zhiyuan Cheng, Lichan Hong, Ed H. Chi, Claire Cui

* 9 pages 

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Small Towers Make Big Differences

Aug 13, 2020
Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Ed H. Chi

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Self-supervised Learning for Deep Models in Recommendations

Jul 25, 2020
Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix Yu, Aditya Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi, Kang, Evan Ettinger

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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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Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model

Jun 09, 2020
Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei

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Developing a Recommendation Benchmark for MLPerf Training and Inference

Apr 14, 2020
Carole-Jean Wu, Robin Burke, Ed H. Chi, Joseph Konstan, Julian McAuley, Yves Raimond, Hao Zhang

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Understanding and Improving Knowledge Distillation

Feb 10, 2020
Jiaxi Tang, Rakesh Shivanna, Zhe Zhao, Dong Lin, Anima Singh, Ed H. Chi, Sagar Jain

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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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Quantifying Long Range Dependence in Language and User Behavior to improve RNNs

May 23, 2019
Francois Belletti, Minmin Chen, 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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AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks

Feb 26, 2019
Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi

* Published as a conference paper at ICLR 2019 

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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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Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs

Jan 25, 2019
Dar Gilboa, Bo Chang, Minmin Chen, Greg Yang, Samuel S. Schoenholz, Ed H. Chi, Jeffrey Pennington

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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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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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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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