Minmin Chen

According to our database, Minmin Chen authored at least 68 papers between 2008 and 2024.

Collaborative distances:

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

2024

EVOLvE: Evaluating and Optimizing LLMs For Exploration.

Allen Nie, Yi Su, Bo Chang, Jonathan N. Lee, Ed H. Chi, Quoc V. Le, Minmin Chen

CoRR, 2024

NATURAL PLAN: Benchmarking LLMs on Natural Language Planning.

Huaixiu Steven Zheng, Swaroop Mishra, Hugh Zhang, Xinyun Chen, Minmin Chen, Azade Nova, Le Hou, Heng-Tze Cheng, Quoc V. Le, Ed H. Chi, Denny Zhou

CoRR, 2024

LLMs for User Interest Exploration: A Hybrid Approach.

Jianling Wang, Haokai Lu, Yifan Liu, He Ma, Yueqi Wang, Yang Gu, Shuzhou Zhang, Ningren Han, Shuchao Bi, Lexi Baugher, Ed H. Chi, Minmin Chen

CoRR, 2024

Diversifying by Intent in Recommender Systems.

Yuyan Wang, Cheenar Banerjee, Samer Chucri, Fabio Soldo, Sriraj Badam, Ed H. Chi, Minmin Chen

CoRR, 2024

Large Language Models as Data Augmenters for Cold-Start Item Recommendation.

Jianling Wang, Haokai Lu, James Caverlee, Ed H. Chi, Minmin Chen

Proceedings of the Companion Proceedings of the ACM on Web Conference 2024, 2024

Cluster Anchor Regularization to Alleviate Popularity Bias in Recommender Systems.

Bo Chang, Changping Meng, He Ma, Shuo Chang, Yang Gu, Yajun Peng, Jingchen Feng, Yaping Zhang, Shuchao Bi, Ed H. Chi, Minmin Chen

Proceedings of the Companion Proceedings of the ACM on Web Conference 2024, 2024

Fresh Content Recommendation at Scale: A Multi-funnel Solution and the Potential of LLMs.

Jianling Wang, Haokai Lu, Minmin Chen

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

Long-Term Value of Exploration: Measurements, Findings and Algorithms.

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

Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

LLMs for User Interest Exploration in Large-scale Recommendation Systems.

Jianling Wang, Haokai Lu, Yifan Liu, He Ma, Yueqi Wang, Yang Gu, Shuzhou Zhang, Ningren Han, Shuchao Bi, Lexi Baugher, Ed H. Chi, Minmin Chen

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

Multi-Task Neural Linear Bandit for Exploration in Recommender Systems.

Yi Su, Haokai Lu, Yuening Li, Liang Liu, Shuchao Bi, Ed H. Chi, Minmin Chen

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

TSMO 2024: Two-sided Marketplace Optimization.

Mihajlo Grbovic, Vladan Radosavljevic, Minmin Chen, Katerina Iliakopoulou-Zanos, Thanasis Noulas, Amit Goyal, Fabrizio Silvestri

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

Exploration: Measurements and Systems.

Minmin Chen

Proceedings of the 2024 ACM SIGIR International Conference on Theory of Information Retrieval, 2024

2023

Correction with Backtracking Reduces Hallucination in Summarization.

Zhenzhen Liu, Chao Wan, Varsha Kishore, Jin Peng Zhou, Minmin Chen, Kilian Q. Weinberger

CoRR, 2023

Hierarchical Reinforcement Learning for Modeling User Novelty-Seeking Intent in Recommender Systems.

Pan Li, Yuyan Wang, Ed H. Chi, Minmin Chen

CoRR, 2023

Prompt Tuning Large Language Models on Personalized Aspect Extraction for Recommendations.

Pan Li, Yuyan Wang, Ed H. Chi, Minmin Chen

CoRR, 2023

Large Language Models for User Interest Journeys.

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

CoRR, 2023

Value of Exploration: Measurements, Findings and Algorithms.

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

CoRR, 2023

Investigating Action-Space Generalization in Reinforcement Learning for Recommendation Systems.

Abhishek Naik, Bo Chang, Alexandros Karatzoglou, Martin Mladenov, Ed H. Chi, Minmin Chen

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 2023

Latent User Intent Modeling for Sequential Recommenders.

Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen

Proceedings of the Companion Proceedings of the ACM Web Conference 2023, 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

Nonlinear Bandits Exploration for Recommendations.

Yi Su, Minmin Chen

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Towards Companion Recommenders Assisting Users' Long-Term Journeys.

Konstantina Christakopoulou, Minmin Chen

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Fresh Content Needs More Attention: Multi-funnel Fresh Content Recommendation.

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

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

2022

Incentivized self-rebalancing fleet in electric vehicle sharing.

Yuguang Wu, Minmin Chen, Xin Wang

IISE Trans., 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

Recency Dropout for Recurrent Recommender Systems.

Bo Chang, Can Xu, Matthieu Lê, Jingchen Feng, Ya Le, Sriraj Badam, Ed H. Chi, Minmin Chen

CoRR, 2022

Learning to Augment for Casual User Recommendation.

Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen

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

Off-Policy Actor-critic for Recommender Systems.

Minmin Chen, Can Xu, Vince Gatto, Devanshu Jain, Aviral Kumar, Ed H. Chi

Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

Surrogate for Long-Term User Experience in Recommender Systems.

Yuyan Wang, Mohit Sharma, Can Xu, Sriraj Badam, Qian Sun, Lee Richardson, Lisa Chung, Ed H. Chi, Minmin Chen

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

2021

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

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

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

User Response Models to Improve a REINFORCE Recommender System.

Minmin Chen, Bo Chang, Can Xu, Ed H. Chi

Proceedings of the WSDM '21, 2021

Values of User Exploration in Recommender Systems.

Minmin Chen, Yuyan Wang, Can Xu, Ya Le, Mohit Sharma, Lee Richardson, Su-Lin Wu, Ed H. Chi

Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

Exploration in Recommender Systems.

Minmin Chen

Proceedings of the RecSys '21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021, 2021

Batch Reinforcement Learning Through Continuation Method.

Yijie Guo, Shengyu Feng, Nicolas Le Roux, Ed H. Chi, Honglak Lee, Minmin Chen

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Deconfounding User Satisfaction Estimation from Response Rate Bias.

Konstantina Christakopoulou, Madeleine Traverse, Trevor Potter, Emma Marriott, Daniel Li, Chris Haulk, Ed H. Chi, Minmin Chen

Proceedings of the RecSys 2020: Fourteenth ACM Conference on Recommender Systems, 2020

2019

Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs.

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

CoRR, 2019

Towards Neural Mixture Recommender for Long Range Dependent User Sequences.

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

Proceedings of the World Wide Web Conference, 2019

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

Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed H. Chi

Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2019

EstImAgg: A Learning Framework for Groupwise Aggregated Data.

Avradeep Bhowmik, Minmin Chen, Zhengming Xing, Suju Rajan

Proceedings of the 2019 SIAM International Conference on Data Mining, 2019

Surrogate Objectives for Batch Policy Optimization in One-step Decision Making.

Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

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

Francois Belletti, Minmin Chen, Ed H. Chi

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

AntisymmetricRNN: A Dynamical System View on Recurrent Neural Networks.

Bo Chang, Minmin Chen, Eldad Haber, Ed H. Chi

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

2018

Categorical-attributes-based item classification for recommender systems.

Qian Zhao, Jilin Chen, Minmin Chen, Sagar Jain, Alex Beutel, Francois Belletti, Ed H. Chi

Proceedings of the 12th ACM Conference on Recommender Systems, 2018

Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks.

Minmin Chen, Jeffrey Pennington, Samuel S. Schoenholz

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

MinimalRNN: Toward More Interpretable and Trainable Recurrent Neural Networks.

Minmin Chen

CoRR, 2017

Efficient Vector Representation for Documents through Corruption.

Minmin Chen

Proceedings of the 5th International Conference on Learning Representations, 2017

2015

Marginalizing stacked linear denoising autoencoders.

Minmin Chen, Kilian Q. Weinberger, Zhixiang Eddie Xu, Fei Sha

J. Mach. Learn. Res., 2015

Marginalized Denoising for Link Prediction and Multi-Label Learning.

Zheng Chen, Minmin Chen, Kilian Q. Weinberger, Weixiong Zhang

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Classifier cascades and trees for minimizing feature evaluation cost.

Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen, Olivier Chapelle

J. Mach. Learn. Res., 2014

Marginalizing Corrupted Features.

Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Q. Weinberger

CoRR, 2014

Marginalized Denoising Auto-encoders for Nonlinear Representations.

Minmin Chen, Kilian Q. Weinberger, Fei Sha, Yoshua Bengio

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

2013

An alternative text representation to TF-IDF and Bag-of-Words

Zhixiang Eddie Xu, Minmin Chen, Kilian Q. Weinberger, Fei Sha

CoRR, 2013

Cost-Sensitive Tree of Classifiers.

Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Minmin Chen

Proceedings of the 30th International Conference on Machine Learning, 2013

Learning with Marginalized Corrupted Features.

Laurens van der Maaten, Minmin Chen, Stephen Tyree, Kilian Q. Weinberger

Proceedings of the 30th International Conference on Machine Learning, 2013

Fast Image Tagging.

Minmin Chen, Alice X. Zheng, Kilian Q. Weinberger

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Classifier Cascade for Minimizing Feature Evaluation Cost.

Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Olivier Chapelle, Dor Kedem

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Marginalized Denoising Autoencoders for Domain Adaptation.

Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, Fei Sha

Proceedings of the 29th International Conference on Machine Learning, 2012

From sBoW to dCoT marginalized encoders for text representation.

Zhixiang Eddie Xu, Minmin Chen, Kilian Q. Weinberger, Fei Sha

Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

2011

Early Deterioration Warning for Hospitalized Patients by Mining Clinical Data.

Yi Mao, Yixin Chen, Gregory Hackmann, Minmin Chen, Chenyang Lu, Marin Kollef, Thomas C. Bailey

Int. J. Knowl. Discov. Bioinform., 2011

Co-Training for Domain Adaptation.

Minmin Chen, Kilian Q. Weinberger, John Blitzer

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Automatic Feature Decomposition for Single View Co-training.

Minmin Chen, Kilian Q. Weinberger, Yixin Chen

Proceedings of the 28th International Conference on Machine Learning, 2011

Medical Data Mining for Early Deterioration Warning in General Hospital Wards.

Yi Mao, Yixin Chen, Gregory Hackmann, Minmin Chen, Chenyang Lu, Marin Kollef, Thomas C. Bailey

Proceedings of the Data Mining Workshops (ICDMW), 2011

Improving context-aware query classification via adaptive self-training.

Minmin Chen, Jian-Tao Sun, Xiaochuan Ni, Yixin Chen

Proceedings of the 20th ACM Conference on Information and Knowledge Management, 2011

2010

Extended duality for nonlinear programming.

Yixin Chen, Minmin Chen

Comput. Optim. Appl., 2010

2009

Constrained optimization for validation-guided conditional random field learning.

Minmin Chen, Yixin Chen, Michael R. Brent, Aaron E. Tenney

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

Gradient-Based Feature Selection for Conditional Random Fields and its Applications in Computational Genetics.

Minmin Chen, Yixin Chen, Michael R. Brent, Aaron E. Tenney

Proceedings of the ICTAI 2009, 2009

2008

CRF-OPT: An Efficient High-Quality Conditional Random Field Solver.

Minmin Chen, Yixin Chen, Michael R. Brent

Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, 2008