A Convex Formulation for Mixed Regression: Near Optimal Rates in the Face of Noise. - Yudong Chen 001; Xinyang Yi; Constantine Caramanis (CoRR, 2013)

A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates. - Yudong Chen 001; Xinyang Yi; Constantine Caramanis (COLT, 2014)

Alternating Minimization for Mixed Linear Regression. - Xinyang Yi; Constantine Caramanis; Sujay Sanghavi (ICML, 2014)

Novel power grid reduction method based on L1 regularization. - Ye Wang 014; Meng Li 004; Xinyang Yi; Zhao Song 002; Michael Orshansky; Constantine Caramanis (DAC, 2015)

Binary Embedding: Fundamental Limits and Fast Algorithm. - Xinyang Yi; Constantine Caramanis; Eric Price 001 (ICML, 2015)

Regularized EM Algorithms: A Unified Framework and Statistical Guarantees. - Xinyang Yi; Constantine Caramanis (NIPS, 2015)

Optimal Linear Estimation under Unknown Nonlinear Transform. - Xinyang Yi; Zhaoran Wang 001; Constantine Caramanis; Han Liu 001 (NIPS, 2015)

Regularized EM Algorithms: A Unified Framework and Provable Statistical Guarantees. - Xinyang Yi; Constantine Caramanis (CoRR, 2015)

Fast Algorithms for Robust PCA via Gradient Descent. - Xinyang Yi; Dohyung Park; Yudong Chen 001; Constantine Caramanis (NIPS, 2016)

More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning. - Xinyang Yi; Zhaoran Wang 001; Zhuoran Yang; Constantine Caramanis; Han Liu 001 (NIPS, 2016)

Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization. - Xinyang Yi; Constantine Caramanis; Sujay Sanghavi (CoRR, 2016)

Minimax Gaussian Classification & Clustering. - Tianyang Li; Xinyang Yi; Constantine Caramanis; Pradeep Ravikumar (AISTATS, 2017)

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts. - Jiaqi Ma 001; Zhe Zhao 001; Xinyang Yi; Jilin Chen; Lichan Hong; Ed H. Chi (KDD, 2018)

Convex and Nonconvex Formulations for Mixed Regression With Two Components: Minimax Optimal Rates. - Yudong Chen 001; Xinyang Yi; Constantine Caramanis (IEEE Trans. Inf. Theory, 2018)

Efficient Training on Very Large Corpora via Gramian Estimation. - Walid Krichene; Nicolas Mayoraz; Steffen Rendle; Li Zhang 001; Xinyang Yi; Lichan Hong; Ed H. Chi; John R. Anderson (ICLR (Poster), 2019)

Sampling-bias-corrected neural modeling for large corpus item recommendations. - Xinyang Yi; Ji Yang; Lichan Hong; Derek Zhiyuan Cheng; Lukasz Heldt; Aditee Kumthekar; Zhe Zhao 001; Li Wei; Ed H. Chi (RecSys, 2019)

Recommending what video to watch next: a multitask ranking system. - Zhe Zhao 001; Lichan Hong; Li Wei; Jilin Chen; Aniruddh Nath; Shawn Andrews; Aditee Kumthekar; Maheswaran Sathiamoorthy; Xinyang Yi; Ed H. Chi (RecSys, 2019)

End-to-End Deep Attentive Personalized Item Retrieval for Online Content-sharing Platforms. - Jyun-Yu Jiang; Tao Wu; Georgios Roumpos; Heng-Tze Cheng; Xinyang Yi; Ed H. Chi; Harish Ganapathy; Nitin Jindal; Pei Cao; Wei Wang 010 (WWW, 2020)

Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems. - Wang-Cheng Kang; Derek Zhiyuan Cheng; Ting Chen; Xinyang Yi; Dong Lin; Lichan Hong; Ed H. Chi (WWW (Companion Volume), 2020)

Off-policy Learning in Two-stage Recommender Systems. - Jiaqi Ma 001; Zhe Zhao 001; Xinyang Yi; Ji Yang; Minmin Chen; Jiaxi Tang; Lichan Hong; Ed H. Chi (WWW, 2020)

Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations. - Ji Yang; Xinyang Yi; Derek Zhiyuan Cheng; Lichan Hong; Yang Li 150; Simon Xiaoming Wang; Taibai Xu; Ed H. Chi (WWW (Companion Volume), 2020)

Self-supervised Learning for Deep Models in Recommendations. - Tiansheng Yao; Xinyang Yi; Derek Zhiyuan Cheng; Felix X. Yu; Aditya Krishna Menon; Lichan Hong; Ed H. Chi; Steve Tjoa; Jieqi Kang; Evan Ettinger (CoRR, 2020)

Deep Hash Embedding for Large-Vocab Categorical Feature Representations. - Wang-Cheng Kang; Derek Zhiyuan Cheng; Tiansheng Yao; Xinyang Yi; Ting Chen; Lichan Hong; Ed H. Chi (CoRR, 2020)

Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model. - Jiaqi Ma 001; Xinyang Yi; Weijing Tang; Zhe Zhao 001; Lichan Hong; Ed H. Chi; Qiaozhu Mei (AISTATS, 2021)

Self-supervised Learning for Large-scale Item Recommendations. - 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 (CIKM, 2021)

Learning to Embed Categorical Features without Embedding Tables for Recommendation. - Wang-Cheng Kang; Derek Zhiyuan Cheng; Tiansheng Yao; Xinyang Yi; Ting Chen; Lichan Hong; Ed H. Chi (KDD, 2021)

A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation. - Yin Zhang 011; Derek Zhiyuan Cheng; Tiansheng Yao; Xinyang Yi; Lichan Hong; Ed H. Chi (WWW, 2021)

Improving Multi-Task Generalization via Regularizing Spurious Correlation. - Ziniu Hu; Zhe Zhao 001; Xinyang Yi; Tiansheng Yao; Lichan Hong; Yizhou Sun; Ed H. Chi (NeurIPS, 2022)

Distributionally-robust Recommendations for Improving Worst-case User Experience. - Hongyi Wen; Xinyang Yi; Tiansheng Yao; Jiaxi Tang; Lichan Hong; Ed H. Chi (WWW, 2022)

Reward Shaping for User Satisfaction in a REINFORCE Recommender. - 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; et al. (CoRR, 2022)

Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN). - Yin Zhang 011; Ruoxi Wang; Derek Zhiyuan Cheng; Tiansheng Yao; Xinyang Yi; Lichan Hong; James Caverlee; Ed H. Chi (KDD, 2023)

Improving Training Stability for Multitask Ranking Models in Recommender Systems. - Jiaxi Tang; Yoel Drori; Daryl Chang; Maheswaran Sathiamoorthy; Justin Gilmer; Li Wei; Xinyang Yi; Lichan Hong; Ed H. Chi (KDD, 2023)

Online Matching: A Real-time Bandit System for Large-scale Recommendations. - Xinyang Yi; Shao-Chuan Wang; Ruining He; Hariharan Chandrasekaran; Charles Wu; Lukasz Heldt; Lichan Hong; Minmin Chen; Ed H. Chi (RecSys, 2023)

Better Generalization with Semantic IDs: A case study in Ranking for Recommendations. - Anima Singh; Trung Vu 002; Raghunandan H. Keshavan; Nikhil Mehta 002; Xinyang Yi; Lichan Hong; Lukasz Heldt; Li Wei; Ed H. Chi; Maheswaran Sathiamoorthy (CoRR, 2023)

Density Weighting for Multi-Interest Personalized Recommendation. - Nikhil Mehta 002; Anima Singh; Xinyang Yi; Sagar Jain; Lichan Hong; Ed H. Chi (CoRR, 2023)

Leveraging LLM Reasoning Enhances Personalized Recommender Systems. - Alicia Tsai; Adam Kraft; Long Jin; Chenwei Cai; Anahita Hosseini; Taibai Xu; Zemin Zhang; Lichan Hong; Ed Huai-hsin Chi; Xinyang Yi (ACL (Findings), 2024)

Aligning Large Language Models with Recommendation Knowledge. - Yuwei Cao; Nikhil Mehta 002; Xinyang Yi; Raghunandan Hulikal Keshavan; Lukasz Heldt; Lichan Hong; Ed H. Chi; Maheswaran Sathiamoorthy (NAACL-HLT (Findings), 2024)

Short-form Video Needs Long-term Interests: An Industrial Solution for Serving Large User Sequence Models. - Yuening Li; Diego Uribe; Chuan He; Jiaxi Tang; Qingyun Liu 003; Junjie Shan; Ben Most; Kaushik Kalyan; Shuchao Bi; Xinyang Yi; Lichan Hong; Ed H. Chi; et al. (RecSys, 2024)

Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations. - Anima Singh; Trung Vu 002; Nikhil Mehta 002; Raghunandan H. Keshavan; Maheswaran Sathiamoorthy; Yilin Zheng; Lichan Hong; Lukasz Heldt; Li Wei; Devansh Tandon; Ed H. Chi; Xinyang Yi (RecSys, 2024)

STAR: A Simple Training-free Approach for Recommendations using Large Language Models. - Dong-Ho Lee; Adam Kraft; Long Jin; Nikhil Mehta 002; Taibai Xu; Lichan Hong; Ed H. Chi; Xinyang Yi (CoRR, 2024)