Chen, Wenlin

23 publications

ICLR 2026 BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving Shu Liu, Wenlin Chen, Weihao Li, Zheng Wang, Lijin Yang, Jianing Huang, YipinZhang, Zhongzhan Huang, Ze Cheng, Hao Yang

ICML 2025 Progressive Tempering Sampler with Diffusion Severi Rissanen, Ruikang Ouyang, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato

ICLRW 2025 Towards Training One-Step Diffusion Models Without Distillation Mingtian Zhang, Jiajun He, Wenlin Chen, Zijing Ou, José Miguel Hernández-Lobato, Bernhard Schölkopf, David Barber

AISTATS 2025 Training Neural Samplers with Reverse Diffusive KL Divergence Jiajun He, Wenlin Chen, Mingtian Zhang, David Barber, José Miguel Hernández-Lobato

ICLRW 2025 Your Image Is Secretly the Last Frame of a Pseudo Video Wenlong Chen, Wenlin Chen, Lapo Rastrelli, Yingzhen Li

ICML 2024 Diffusive Gibbs Sampling Wenlin Chen, Mingtian Zhang, Brooks Paige, José Miguel Hernández-Lobato, David Barber

TMLR 2024 Leveraging Task Structures for Improved Identifiability in Neural Network Representations Wenlin Chen, Julien Horwood, Juyeon Heo, José Miguel Hernández-Lobato

NeurIPS 2024 Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks Wenlin Chen, Hong Ge

ICMLW 2024 ReLU Characteristic Activation Analysis Wenlin Chen, Hong Ge

ICML 2024 Wukong: Towards a Scaling Law for Large-Scale Recommendation Buyun Zhang, Liang Luo, Yuxin Chen, Jade Nie, Xi Liu, Shen Li, Yanli Zhao, Yuchen Hao, Yantao Yao, Ellie Dingqiao Wen, Jongsoo Park, Maxim Naumov, Wenlin Chen

ICLR 2023 Meta-Learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato

ICLRW 2022 An Evaluation Framework for the Objective Functions of De Novo Drug Design Benchmarks Austin Tripp, Wenlin Chen, José Miguel Hernández-Lobato

NeurIPSW 2022 Meta-Learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato

TMLR 2022 Optimal Client Sampling for Federated Learning Wenlin Chen, Samuel Horváth, Peter Richtárik

ECML-PKDD 2020 To Ensemble or Not Ensemble: When Does End-to-End Training Fail? Andrew M. Webb, Charles Reynolds, Wenlin Chen, Henry W. J. Reeve, Dan-Andrei Iliescu, Mikel Luján, Gavin Brown

ECML-PKDD 2016 Deep Metric Learning with Data Summarization Wenlin Wang, Changyou Chen, Wenlin Chen, Piyush Rai, Lawrence Carin

AAAI 2015 A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen

ICML 2015 Compressing Neural Networks with the Hashing Trick Wenlin Chen, James Wilson, Stephen Tyree, Kilian Weinberger, Yixin Chen

NeurIPS 2015 Fast Distributed K-Center Clustering with Outliers on Massive Data Gustavo Malkomes, Matt J Kusner, Wenlin Chen, Kilian Q. Weinberger, Benjamin Moseley

AISTATS 2015 Filtered Search for Submodular Maximization with Controllable Approximation Bounds Wenlin Chen, Yixin Chen, Kilian Q. Weinberger

AAAI 2014 Feature-Cost Sensitive Learning with Submodular Trees of Classifiers Matt J. Kusner, Wenlin Chen, Quan Zhou, Zhixiang Eddie Xu, Kilian Q. Weinberger, Yixin Chen

AAAI 2013 Goal-Oriented Euclidean Heuristics with Manifold Learning Wenlin Chen, Yixin Chen, Kilian Q. Weinberger, Qiang Lu, Xiaoping Chen

ICML 2013 Maximum Variance Correction with Application to A* Search Wenlin Chen, Kilian Weinberger, Yixin Chen