Homepage of Lequn (Luke) Wang
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Lequn (Luke) Wang |
About
I am a research scientist at Netflix, conducting applied research on machine learning and recommender systems. Before joining Netflix, I obtained my Ph.D. in Computer Science from Cornell University, advised by Prof. Thorsten Joachims. Prior to that, I received a B.S. in Computer Science from Shanghai Jiao Tong University. I also interned/visited Microsoft Research (Asia, Redmond, New York), Amazon Music, MPI-SWS, MBZUAI, and TU Dortmund.
News
Paper Off-Policy Evaluation for Large Action Spaces via Policy Convolution accepted at the Web Conference 2024.
Paper Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits accepted at AISTATS 2024.
Paper Improving Expert Predictions with Conformal Prediction accepted at ICML 2023.
Paper Uncertainty Quantification for Fairness in Two-Stage Recommender Systems accepted at WSDM 2023.
Research Interests
Machine learning algorithms and theory
Learning from human behavioral data and implicit feedback in search and recommender systems
Biases, inequities, uncertainties, and long-term dynamics in algorithmic decision-making systems
Trustworthy, responsible, human-centered machine learning
Batch and online multi-armed bandits, reinforcement learning, and ranking
Selected Preprints and Publications
Off-Policy Evaluation for Large Action Spaces via Policy Convolution
Noveen Sachdeva, Lequn Wang, Dawen Liang, Nathan Kallus, Julian McAuley
The Web Conference, 2024
Oracle-Efficient Pessimism: Offline Policy Optimization in Contextual Bandits
Lequn Wang, Akshay Krishnamurthy, Aleksandrs Slivkins
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
Improving Expert Predictions with Conformal Prediction
Eleni Straitouri, Lequn Wang, Nastaran Okati, Manuel Gomez Rodriguez
International Conference on Machine Learning (ICML), 2023
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems
Lequn Wang, Thorsten Joachims
International Conference on Web Search and Data Mining (WSDM), 2023 [code]
Improving Screening Processes via Calibrated Subset Selection
Lequn Wang, Thorsten Joachims, Manuel Gomez Rodriguez
International Conference on Machine Learning (ICML), 2022 [code]
Recommendations as Treatments
Thorsten Joachims, Ben London, Yi Su, Adith Swaminathan, Lequn Wang
AI Magazine 42(3), 19-30, 2021
Fairness of Exposure in Stochastic Bandits
Lequn Wang, Yiwei Bai, Wen Sun, Thorsten Joachims
International Conference on Machine Learning (ICML), 2021
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets
Lequn Wang, Thorsten Joachims
International Conference on the Theory of Information Retrieval (ICTIR), 2021
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
Yi Su*, Lequn Wang*, Michele Santacatterina, Thorsten Joachims
International Conference on Machine Learning (ICML), 2019
Resource Aware Person Re-identification across Multiple Resolutions
Yan Wang*, Lequn Wang*, Yurong You*, Xu Zou, Vincent Chen, Serena Li, Gao Huang, Bharath Hariharan, Kilian Q. Weinberger
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
* indicates equal contribution.
Talks
Towards Fair Interactive Systems despite Biases, Bytedance AI Lab, Virtual, January 2023.
Fairness of Exposure in Stochastic Bandits, INFORMS, Indianapolis (USA), October 2022.
Improving Screening Processes via Calibrated Subset Selection, ICML, Baltimore (USA), July 2022.
Improving Screening Processes via Calibrated Subset Selection, MBZUAI, Abu Dhabi (UAE), March 2022.
Fairness of Exposure in Stochastic Bandits, ICML, Virtual, July 2021.
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets, ICTIR, Virtual, July 2021.
Professional Services
Program Committee Reviewer for
International Conference on Machine Learning (ICML) 2019-2023
Neural Information Processing Systems (NeurIPS) 2019-2023
Conference on Artiļ¬cial Intelligence (AAAI) 2020-2022
International Conference on Learning Representations (ICLR) 2021
Conference on Knowledge Discovery and Data Mining (KDD) 2021-2023
International Conference on Artificial Intelligence and Statistics (AISTATS) 2022-2023
International Conference on Web Search and Data Mining (WSDM) 2023