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ORCIDEd H. Chi
Ed Huai-hsin Chi – 紀懷新
Person information
- unicode name: 紀懷新
- affiliation: Google, Mountain View, CA, USA
- affiliation: Palo Alto Research Center (PARC), CA, USA
- affiliation (PhD 1999): University of Minnesota, Computer Science Department, Minneapolis, MN, USA
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2020 – today
- 2023
[c168]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, Denny Zhou:
Self-Consistency Improves Chain of Thought Reasoning in Language Models. ICLR 2023- [c167]Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V. Le, Ed H. Chi:
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. ICLR 2023 - [c166]Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, Denny Zhou:
Large Language Models Can Be Easily Distracted by Irrelevant Context. ICML 2023: 31210-31227 - [c165]Jiaxi Tang
, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi:
Improving Training Stability for Multitask Ranking Models in Recommender Systems. KDD 2023: 4882-4893 - [c164]Jianling Wang, Haokai Lu, Sai Zhang, Bart N. Locanthi, Haoting Wang, Dylan Greaves, Benjamin Lipshitz, Sriraj Badam, Ed H. Chi, Cristos J. Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen:
Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation. KDD 2023: 5082-5091 - [c163]Yin Zhang, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi:
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN). KDD 2023: 5608-5617 - [c162]Derek Zhiyuan Cheng, Dhaval Patel, Linsey Pang, Sameep Mehta, Kexin Xie, Ed H. Chi, Wei Liu, Nitesh V. Chawla, James Bailey:
Foundations and Applications in Large-scale AI Models: Pre-training, Fine-tuning, and Prompt-based Learning. KDD 2023: 5853-5854 - [c161]Derek Zhiyuan Cheng, Ruoxi Wang, Wang-Cheng Kang, Benjamin Coleman, Yin Zhang, Jianmo Ni, Jonathan Valverde, Lichan Hong, Ed H. Chi:
Efficient Data Representation Learning in Google-scale Systems. RecSys 2023: 267-271 - [c160]Xinyang Yi, Shao-Chuan Wang, Ruining He, Hariharan Chandrasekaran, Charles Wu, Lukasz Heldt, Lichan Hong, Minmin Chen, Ed H. Chi:
Online Matching: A Real-time Bandit System for Large-scale Recommendations. RecSys 2023: 403-414 - [c159]Kaize Ding, Albert Jiongqian Liang, Bryan Perozzi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng:
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer. SIGIR 2023: 2062-2066 - [c158]Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen:
Latent User Intent Modeling for Sequential Recommenders. WWW (Companion Volume) 2023: 427-431 - [c157]Abhishek Naik, Bo Chang, Alexandros Karatzoglou, Martin Mladenov, Ed H. Chi, Minmin Chen:
Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems. WWW (Companion Volume) 2023: 966-972 - [i72]Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H. Chi, Nathanael Schärli, Denny Zhou:
Large Language Models Can Be Easily Distracted by Irrelevant Context. CoRR abs/2302.00093 (2023) - [i71]Jiaxi Tang, Yoel Drori, Daryl Chang, Maheswaran Sathiamoorthy, Justin Gilmer, Li Wei, Xinyang Yi, Lichan Hong, Ed H. Chi:
Improving Training Stability for Multitask Ranking Models in Recommender Systems. CoRR abs/2302.09178 (2023) - [i70]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) - [i69]Shashank Rajput, Nikhil Mehta, Anima Singh, Raghunandan H. Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, Maheswaran Sathiamoorthy:
Recommender Systems with Generative Retrieval. CoRR abs/2305.05065 (2023) - [i68]Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng:
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction. CoRR abs/2305.06474 (2023) - [i67]Yi Su, Xiangyu Wang, Elaine Ya Le, Liang Liu, Yuening Li, Haokai Lu, Benjamin Lipshitz, Sriraj Badam, Lukasz Heldt, Shuchao Bi, Ed H. Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen:
Value of Exploration: Measurements, Findings and Algorithms. CoRR abs/2305.07764 (2023) - [i66]Benjamin Coleman, Wang-Cheng Kang, Matthew Fahrbach, Ruoxi Wang, Lichan Hong, Ed H. Chi, Derek Zhiyuan Cheng:
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems. CoRR abs/2305.12102 (2023) - [i65]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) - [i64]Konstantina Christakopoulou, Alberto Lalama, Cj Adams, Iris Qu, Yifat Amir, Samer Chucri, Pierce Vollucci, Fabio Soldo, Dina Bseiso, Sarah Scodel, Lucas Dixon, Ed H. Chi, Minmin Chen:
Large Language Models for User Interest Journeys. CoRR abs/2305.15498 (2023) - [i63]Kaize Ding, Albert Jiongqian Liang, Bryan Perrozi, Ting Chen, Ruoxi Wang, Lichan Hong, Ed H. Chi, Huan Liu, Derek Zhiyuan Cheng:
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer. CoRR abs/2305.17386 (2023) - [i62]Pan Li, Yuyan Wang, Ed H. Chi, Minmin Chen:
Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations. CoRR abs/2306.01475 (2023) - [i61]Pan Li, Yuyan Wang, Ed H. Chi, Minmin Chen:
Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems. CoRR abs/2306.01476 (2023) - [i60]Jianling Wang, Haokai Lu, Sai Zhang, Bart N. Locanthi, Haoting Wang, Dylan Greaves, Benjamin Lipshitz, Sriraj Badam, Ed H. Chi, Cristos Goodrow, Su-Lin Wu, Lexi Baugher, Minmin Chen:
Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation. CoRR abs/2306.01720 (2023) - [i59]Anima Singh, Trung Vu, Raghunandan H. Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed H. Chi, Maheswaran Sathiamoorthy:
Better Generalization with Semantic IDs: A case study in Ranking for Recommendations. CoRR abs/2306.08121 (2023) - [i58]Xinyang Yi, Shao-Chuan Wang, Ruining He, Hariharan Chandrasekaran, Charles Wu, Lukasz Heldt, Lichan Hong, Minmin Chen, Ed H. Chi:
Online Matching: A Real-time Bandit System for Large-scale Recommendations. CoRR abs/2307.15893 (2023) - [i57]Nikhil Mehta, Anima Singh, Xinyang Yi, Sagar Jain, Lichan Hong, Ed H. Chi:
Density Weighting for Multi-Interest Personalized Recommendation. CoRR abs/2308.01563 (2023) - 2022
- [j23]Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus:
Emergent Abilities of Large Language Models. Trans. Mach. Learn. Res. 2022 (2022) - [c156]Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Prakash Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi:
HyperPrompt: Prompt-based Task-Conditioning of Transformers. ICML 2022: 8678-8690 - [c155]Yuyan Wang, Mohit Sharma, Can Xu, Sriraj Badam, Qian Sun, Lee Richardson, Lisa Chung, Ed H. Chi, Minmin Chen:
Surrogate for Long-Term User Experience in Recommender Systems. KDD 2022: 4100-4109 - [c154]Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi:
Improving Multi-Task Generalization via Regularizing Spurious Correlation. NeurIPS 2022 - [c153]Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed H. Chi, Quoc V. Le, Denny Zhou:
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models. NeurIPS 2022 - [c152]Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, Ed H. Chi:
Off-Policy Actor-critic for Recommender Systems. RecSys 2022: 338-349 - [c151]Furkan Kocayusufoglu, Tao Wu, Anima Singh, Georgios Roumpos, Heng-Tze Cheng, Sagar Jain, Ed H. Chi, Ambuj K. Singh:
Multi-Resolution Attention for Personalized Item Search. WSDM 2022: 508-516 - [c150]Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen:
Learning to Augment for Casual User Recommendation. WWW 2022: 2183-2194 - [c149]Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Li Wei, Ed H. Chi:
Can Small Heads Help? Understanding and Improving Multi-Task Generalization. WWW 2022: 3009-3019 - [c148]Hongyi Wen, Xinyang Yi, Tiansheng Yao, Jiaxi Tang, Lichan Hong, Ed H. Chi:
Distributionally-robust Recommendations for Improving Worst-case User Experience. WWW 2022: 3606-3610 - [i56]Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Agüera y Arcas, Claire Cui, Marian Croak, Ed H. Chi, Quoc Le:
LaMDA: Language Models for Dialog Applications. CoRR abs/2201.08239 (2022) - [i55]Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed H. Chi, Minmin Chen:
Recency Dropout for Recurrent Recommender Systems. CoRR abs/2201.11016 (2022) - [i54]Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, Denny Zhou:
Chain of Thought Prompting Elicits Reasoning in Large Language Models. CoRR abs/2201.11903 (2022) - [i53]Kiran Vodrahalli, Rakesh Shivanna, Maheswaran Sathiamoorthy, Sagar Jain, Ed H. Chi:
Algorithms for Efficiently Learning Low-Rank Neural Networks. CoRR abs/2202.00834 (2022) - [i52]Yun He, Huaixiu Steven Zheng, Yi Tay, Jai Prakash Gupta, Yu Du, Vamsi Aribandi, Zhe Zhao, YaGuang Li, Zhao Chen, Donald Metzler, Heng-Tze Cheng, Ed H. Chi:
HyperPrompt: Prompt-based Task-Conditioning of Transformers. CoRR abs/2203.00759 (2022) - [i51]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Denny Zhou:
Self-Consistency Improves Chain of Thought Reasoning in Language Models. CoRR abs/2203.11171 (2022) - [i50]Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen:
Learning to Augment for Casual User Recommendation. CoRR abs/2204.00926 (2022) - [i49]Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi:
Improving Multi-Task Generalization via Regularizing Spurious Correlation. CoRR abs/2205.09797 (2022) - [i48]Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, Ed H. Chi:
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. CoRR abs/2205.10625 (2022) - [i47]Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, William Fedus:
Emergent Abilities of Large Language Models. CoRR abs/2206.07682 (2022) - [i46]Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V. Le, Ed H. Chi, Denny Zhou:
Rationale-Augmented Ensembles in Language Models. CoRR abs/2207.00747 (2022) - [i45]Konstantina Christakopoulou, Can Xu, Sai Zhang, Sriraj Badam, Trevor Potter, Daniel Li, Hao Wan, Xinyang Yi, Ya Le, Chris Berg, Eric Bencomo Dixon, Ed H. Chi, Minmin Chen:
Reward Shaping for User Satisfaction in a REINFORCE Recommender. CoRR abs/2209.15166 (2022) - [i44]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) - [i43]Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, Jason Wei:
Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them. CoRR abs/2210.09261 (2022) - [i42]Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Y. Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, Jason Wei:
Scaling Instruction-Finetuned Language Models. CoRR abs/2210.11416 (2022) - [i41]Yin Zhang, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi:
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN). CoRR abs/2210.14309 (2022) - [i40]Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen:
Latent User Intent Modeling for Sequential Recommenders. CoRR abs/2211.09832 (2022) - 2021
- [c147]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 - [c146]Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei:
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model. AISTATS 2021: 928-936 - [c145]Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix X. Yu, Ting Chen, Aditya Krishna Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi (Jay) Kang, Evan Ettinger:
Self-supervised Learning for Large-scale Item Recommendations. CIKM 2021: 4321-4330 - [c144]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 - [c143]Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed H. Chi, Honglak Lee, Minmin Chen:
Batch Reinforcement Learning Through Continuation Method. ICLR 2021 - [c142]Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi:
Learning to Embed Categorical Features without Embedding Tables for Recommendation. KDD 2021: 840-850 - [c141]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 - [c140]Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi:
Improving Calibration through the Relationship with Adversarial Robustness. NeurIPS 2021: 14358-14369 - [c139]Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed H. Chi:
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. NeurIPS 2021: 29335-29347 - [c138]Minmin Chen, Yuyan Wang, Can Xu, Ya Le, Mohit Sharma, Lee Richardson, Su-Lin Wu, Ed H. Chi:
Values of User Exploration in Recommender Systems. RecSys 2021: 85-95 - [c137]Zhe Chen, Yuyan Wang, Dong Lin, Derek Zhiyuan Cheng, Lichan Hong, Ed H. Chi, Claire Cui:
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems. WSDM 2021: 76-84 - [c136]Minmin Chen, Bo Chang, Can Xu, Ed H. Chi:
User Response Models to Improve a REINFORCE Recommender System. WSDM 2021: 121-129 - [c135]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 - [c134]Ruoxi Wang, Rakesh Shivanna, Derek Zhiyuan Cheng, Sagar Jain, Dong Lin, Lichan Hong, Ed H. Chi:
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems. WWW 2021: 1785-1797 - [c133]Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, Ed H. Chi:
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation. WWW 2021: 2220-2231 - [c132]Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen:
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. WWW 2021: 3872-3883 - [i39]Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Recommender System Effects with Simulated Users. CoRR abs/2101.04526 (2021) - [i38]Ruohan Zhan, Konstantina Christakopoulou, Ya Le, Jayden Ooi, Martin Mladenov, Alex Beutel, Craig Boutilier, Ed H. Chi, Minmin Chen:
Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities. CoRR abs/2105.02377 (2021) - [i37]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. CoRR abs/2105.09985 (2021) - [i36]Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi:
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. CoRR abs/2106.02705 (2021) - [i35]Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, Ed H. Chi:
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning. CoRR abs/2106.03760 (2021) - 2020
- [c131]Tao Wu, Ellie Ka In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John R. Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, Pei Cao:
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval. CIKM 2020: 2821-2828 - [c130]Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi:
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. EMNLP (1) 2020: 5141-5146 - [c129]Sungryull Sohn, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed H. Chi, Craig Boutilier:
BRPO: Batch Residual Policy Optimization. IJCAI 2020: 2824-2830 - [c128]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. NeurIPS 2020 - [c127]Konstantina Christakopoulou, Madeleine Traverse, Trevor Potter, Emma Marriott, Daniel Li, Chris Haulk, Ed H. Chi, Minmin Chen:
Deconfounding User Satisfaction Estimation from Response Rate Bias. RecSys 2020: 450-455 - [c126]Ed H. Chi:
From Missing Data to Boltzmann Distributions and Time Dynamics: The Statistical Physics of Recommendation. WSDM 2020: 1-2 - [c125]Ji Yang, Xinyang Yi, Derek Zhiyuan Cheng, Lichan Hong, Yang Li, Simon Xiaoming Wang, Taibai Xu, Ed H. Chi:
Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations. WWW (Companion Volume) 2020: 441-447 - [c124]Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi:
Off-policy Learning in Two-stage Recommender Systems. WWW 2020: 463-473 - [c123]Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen, Xinyang Yi, Dong Lin, Lichan Hong, Ed H. Chi:
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems. WWW (Companion Volume) 2020: 562-566 - [c122]Jyun-Yu Jiang, Tao Wu, Georgios Roumpos, Heng-Tze Cheng, Xinyang Yi, Ed H. Chi, Harish Ganapathy, Nitin Jindal, Pei Cao, Wei Wang:
End-to-End Deep Attentive Personalized Item Retrieval for Online Content-sharing Platforms. WWW 2020: 2870-2877 - [i34]Jiaxi Tang, Rakesh Shivanna, Zhe Zhao, Dong Lin, Anima Singh, Ed H. Chi, Sagar Jain:
Understanding and Improving Knowledge Distillation. CoRR abs/2002.03532 (2020) - [i33]Ge Liu, Rui Wu, Heng-Tze Cheng, Jing Wang, Jayden Ooi, Lihong Li, Ang Li, Wai Lok Sibon Li, Craig Boutilier, Ed H. Chi:
Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing. CoRR abs/2002.05229 (2020) - [i32]Sungryull Sohn, Yinlam Chow, Jayden Ooi, Ofir Nachum, Honglak Lee, Ed H. Chi, Craig Boutilier:
BRPO: Batch Residual Policy Optimization. CoRR abs/2002.05522 (2020) - [i31]Wang-Cheng Kang, Derek Zhiyuan Cheng, Ting Chen, Xinyang Yi, Dong Lin, Lichan Hong, Ed H. Chi:
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems. CoRR abs/2002.08530 (2020) - [i30]Carole-Jean Wu, Robin Burke, Ed H. Chi, Joseph A. Konstan, Julian J. McAuley, Yves Raimond, Hao Zhang:
Developing a Recommendation Benchmark for MLPerf Training and Inference. CoRR abs/2003.07336 (2020) - [i29]Jiaqi Ma, Xinyang Yi, Weijing Tang, Zhe Zhao, Lichan Hong, Ed H. Chi, Qiaozhu Mei:
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model. CoRR abs/2006.05067 (2020) - [i28]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. CoRR abs/2006.13114 (2020) - [i27]Yao Qin, Xuezhi Wang, Alex Beutel, Ed H. Chi:
Improving Uncertainty Estimates through the Relationship with Adversarial Robustness. CoRR abs/2006.16375 (2020) - [i26]Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix X. Yu, Aditya Krishna Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, Evan Ettinger:
Self-supervised Learning for Deep Models in Recommendations. CoRR abs/2007.12865 (2020) - [i25]Tao Wu, Ellie Ka In Chio, Heng-Tze Cheng, Yu Du, Steffen Rendle, Dima Kuzmin, Ritesh Agarwal, Li Zhang, John R. Anderson, Sarvjeet Singh, Tushar Chandra, Ed H. Chi, Wen Li, Ankit Kumar, Xiang Ma, Alex Soares, Nitin Jindal, Pei Cao:
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval. CoRR abs/2008.02930 (2020) - [i24]Yuyan Wang, Zhe Zhao, Bo Dai, Christopher Fifty, Dong Lin, Lichan Hong, Ed H. Chi:
Small Towers Make Big Differences. CoRR abs/2008.05808 (2020) - [i23]Zhe Chen, Yuyan Wang, Dong Lin, Derek Zhiyuan Cheng, Lichan Hong, Ed H. Chi, Claire Cui:
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems. CoRR abs/2008.07032 (2020) - [i22]Ruoxi Wang, Rakesh Shivanna, Derek Zhiyuan Cheng, Sagar Jain, Dong Lin, Lichan Hong, Ed H. Chi:
DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems. CoRR abs/2008.13535 (2020) - [i21]Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, Ed H. Chi:
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation. CoRR abs/2010.02338 (2020) - [i20]Wang-Cheng Kang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Ting Chen, Lichan Hong, Ed H. Chi:
Deep Hash Embedding for Large-Vocab Categorical Feature Representations. CoRR abs/2010.10784 (2020) - [i19]Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, Ed H. Chi:
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation. CoRR abs/2010.15982 (2020) - [i18]Hussam Abu-Libdeh, Deniz Altinbüken, Alex Beutel, Ed H. Chi, Lyric Doshi, Tim Kraska, Xiaozhou Li, Andy Ly, Christopher Olston:
Learned Indexes for a Google-scale Disk-based Database. CoRR abs/2012.12501 (2020)
2010 – 2019
- 2019
- [c121]Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, Ed H. Chi:
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning. AAAI 2019: 216-223 - [c120]Sahaj Garg, Vincent Perot, Nicole Limtiaco, Ankur Taly, Ed H. Chi, Alex Beutel:
Counterfactual Fairness in Text Classification through Robustness. AIES 2019: 219-226 - [c119]Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi:
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. AIES 2019: 453-459 - [c118]Tim Kraska, Mohammad Alizadeh, Alex Beutel, Ed H. Chi, Ani Kristo, Guillaume Leclerc, Samuel Madden, Hongzi Mao, Vikram Nathan:
SageDB: A Learned Database System. CIDR 2019 - [c117]Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi:
AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks. ICLR (Poster) 2019 - [c116]Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed H. Chi, John R. Anderson:
Efficient Training on Very Large Corpora via Gramian Estimation. ICLR (Poster) 2019 - [c115]Francois Belletti, Minmin Chen, Ed H. Chi:
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs. KDD 2019: 1317-1327 - [c114]