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Luke Lequn Wang
Luke Lequn Wang
Cornell University
Verified email at cornell.edu - Homepage
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Cited by
Cited by
Year
Resource aware person re-identification across multiple resolutions
Y Wang, L Wang, Y You, X Zou, V Chen, S Li, G Huang, B Hariharan, ...
Proceedings of the IEEE conference on computer vision and pattern …, 2018
2582018
Cab: Continuous adaptive blending for policy evaluation and learning
Y Su, L Wang, M Santacatterina, T Joachims
International Conference on Machine Learning, 6005-6014, 2019
542019
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets
L Wang, T Joachims
International Conference on Theory of Information Retrieval, 23-41, 2021
322021
Fairness of Exposure in Stochastic Bandits
L Wang, Y Bai, W Sun, T Joachims
International Conference on Machine Learning, 10686-10696, 2021
272021
Recommendations as treatments
T Joachims, B London, Y Su, A Swaminathan, L Wang
AI Magazine 42 (3), 19-30, 2021
92021
Batch learning from bandit feedback through bias corrected reward imputation
L Wang, Y Bai, A Bhalla, T Joachims
ICML Workshop on Real-World Sequential Decision Making, 2019
82019
Provably Improving Expert Predictions with Prediction Sets
E Straitouri, L Wang, N Okati, MG Rodriguez
arXiv preprint arXiv:2201.12006, 2022
7*2022
Fastfusionnet: New state-of-the-art for dawnbench squad
F Wu, B Li, L Wang, N Lao, J Blitzer, KQ Weinberger
arXiv preprint arXiv:1902.11291, 2019
62019
Improving Screening Processes via Calibrated Subset Selection
L Wang, T Joachims, MG Rodriguez
International Conference on Machine Learning, 2022
52022
Integrated triaging for fast reading comprehension
F Wu, B Li, L Wang, N Lao, J Blitzer, KQ Weinberger
arXiv preprint arXiv:1909.13128, 2019
52019
Uncertainty Quantification for Fairness in Two-Stage Recommender Systems
L Wang, T Joachims
ACM Conference on Web Search and Data Mining (WSDM), 2023
3*2023
Cost-sensitive learning via deep policy erm
L Wang, Q Xu, C De Sa, T Joachims
12018
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Articles 1–12