Xinyang Yi

According to our database, Xinyang Yi authored at least 40 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

STAR: A Simple Training-free Approach for Recommendations using Large Language Models.

Dong-Ho Lee, Adam Kraft, Long Jin, Nikhil Mehta, Taibai Xu, Lichan Hong, Ed H. Chi, Xinyang Yi

CoRR, 2024

Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations.

Anima Singh, Trung Vu, Nikhil Mehta, Raghunandan H. Keshavan, Maheswaran Sathiamoorthy, Yilin Zheng, Lichan Hong, Lukasz Heldt, Li Wei, Devansh Tandon, Ed H. Chi, Xinyang Yi

Proceedings of the 18th ACM Conference on Recommender Systems, 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, Junjie Shan, Ben Most, Kaushik Kalyan, Shuchao Bi, Xinyang Yi, Lichan Hong, Ed H. Chi, Liang Liu

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

Aligning Large Language Models with Recommendation Knowledge.

Yuwei Cao, Nikhil Mehta, Xinyang Yi, Raghunandan Hulikal Keshavan, Lukasz Heldt, Lichan Hong, Ed H. Chi, Maheswaran Sathiamoorthy

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2024, 2024

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

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

Density Weighting for Multi-Interest Personalized Recommendation.

Nikhil Mehta, Anima Singh, Xinyang Yi, Sagar Jain, Lichan Hong, Ed H. Chi

CoRR, 2023

Better Generalization with Semantic IDs: A case study in Ranking for Recommendations.

Anima Singh, Trung Vu, Raghunandan H. Keshavan, Nikhil Mehta, Xinyang Yi, Lichan Hong, Lukasz Heldt, Li Wei, Ed H. Chi, Maheswaran Sathiamoorthy

CoRR, 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

Proceedings of the 17th ACM Conference on Recommender Systems, 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

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN).

Yin Zhang, Ruoxi Wang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, James Caverlee, Ed H. Chi

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

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, Minmin Chen

CoRR, 2022

Distributionally-robust Recommendations for Improving Worst-case User Experience.

Hongyi Wen, Xinyang Yi, Tiansheng Yao, Jiaxi Tang, Lichan Hong, Ed H. Chi

Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Improving Multi-Task Generalization via Regularizing Spurious Correlation.

Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, Ed H. Chi

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation.

Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, Ed H. Chi

Proceedings of the WWW '21: The Web Conference 2021, 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

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 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

Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

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

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

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

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

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

Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations.

Ji Yang, Xinyang Yi, Derek Zhiyuan Cheng, Lichan Hong, Yang Li, Simon Xiaoming Wang, Taibai Xu, Ed H. Chi

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

Off-policy Learning in Two-stage Recommender Systems.

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Ji Yang, Minmin Chen, Jiaxi Tang, Lichan Hong, Ed H. Chi

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 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

Proceedings of the Companion of The 2020 Web Conference 2020, 2020

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

Proceedings of the WWW '20: The Web Conference 2020, Taipei, Taiwan, April 20-24, 2020, 2020

2019

Recommending what video to watch next: a multitask ranking system.

Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, Ed H. Chi

Proceedings of the 13th ACM Conference on Recommender Systems, 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, Li Wei, Ed H. Chi

Proceedings of the 13th ACM Conference on Recommender Systems, 2019

Efficient Training on Very Large Corpora via Gramian Estimation.

Walid Krichene, Nicolas Mayoraz, Steffen Rendle, Li Zhang, Xinyang Yi, Lichan Hong, Ed H. Chi, John R. Anderson

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Convex and Nonconvex Formulations for Mixed Regression With Two Components: Minimax Optimal Rates.

Yudong Chen, Xinyang Yi, Constantine Caramanis

IEEE Trans. Inf. Theory, 2018

Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts.

Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, Ed H. Chi

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017

Minimax Gaussian Classification & Clustering.

Tianyang Li, Xinyang Yi, Constantine Caramanis, Pradeep Ravikumar

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Solving a Mixture of Many Random Linear Equations by Tensor Decomposition and Alternating Minimization.

Xinyang Yi, Constantine Caramanis, Sujay Sanghavi

CoRR, 2016

More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning.

Xinyang Yi, Zhaoran Wang, Zhuoran Yang, Constantine Caramanis, Han Liu

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Fast Algorithms for Robust PCA via Gradient Descent.

Xinyang Yi, Dohyung Park, Yudong Chen, Constantine Caramanis

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Regularized EM Algorithms: A Unified Framework and Provable Statistical Guarantees.

Xinyang Yi, Constantine Caramanis

CoRR, 2015

Optimal Linear Estimation under Unknown Nonlinear Transform.

Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Regularized EM Algorithms: A Unified Framework and Statistical Guarantees.

Xinyang Yi, Constantine Caramanis

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Binary Embedding: Fundamental Limits and Fast Algorithm.

Xinyang Yi, Constantine Caramanis, Eric Price

Proceedings of the 32nd International Conference on Machine Learning, 2015

Novel power grid reduction method based on L1 regularization.

Ye Wang, Meng Li, Xinyang Yi, Zhao Song, Michael Orshansky, Constantine Caramanis

Proceedings of the 52nd Annual Design Automation Conference, 2015

2014

Alternating Minimization for Mixed Linear Regression.

Xinyang Yi, Constantine Caramanis, Sujay Sanghavi

Proceedings of the 31th International Conference on Machine Learning, 2014

A Convex Formulation for Mixed Regression with Two Components: Minimax Optimal Rates.

Yudong Chen, Xinyang Yi, Constantine Caramanis

Proceedings of The 27th Conference on Learning Theory, 2014

2013

A Convex Formulation for Mixed Regression: Near Optimal Rates in the Face of Noise.

Yudong Chen, Xinyang Yi, Constantine Caramanis

CoRR, 2013