AI for Science

Bridging Deep Learning and Probabilistic Inference: Towards Data Efficiency, Identifiability, and Sampling Scalability
Wenlin Chen
PhD Thesis
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Progressive Tempering Sampler with Diffusion
Severi Rissanen*, RuiKang OuYang*, Jiajun He, Wenlin Chen, Markus Heinonen, Arno Solin, José Miguel Hernández-Lobato
ICML 2025
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Training Neural Samplers with Reverse Diffusive KL Divergence
Jiajun He*, Wenlin Chen*, Mingtian Zhang*, David Barber, José Miguel Hernández-Lobato
AISTATS 2025
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Leveraging Task Structures for Improved Identifiability in Neural Network Representations
Wenlin Chen*, Julien Horwood*, Juyeon Heo, José Miguel Hernández-Lobato
TMLR 2024
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Diffusive Gibbs Sampling
Wenlin Chen*, Mingtian Zhang*, Brooks Paige, José Miguel Hernández-Lobato, David Barber
ICML 2024
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Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
Wenlin Chen, Austin Tripp, José Miguel Hernández-Lobato
ICLR 2023
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An Evaluation Framework for the Objective Functions of De Novo Drug Design Benchmarks
Austin Tripp, Wenlin Chen, José Miguel Hernández-Lobato
MLDD Workshop @ ICLR 2022
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Causal Representation Learning for Latent Space Optimization
Wenlin Chen
MPhil Thesis
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