About Me

I am a PhD student in Machine Learning at University of Cambridge (Machine Learning Group, Computational and Biological Learning Lab) and Max Planck Institute for Intelligent Systems (Department of Empirical Inference), under the Cambridge-Tübingen PhD Fellowship in Machine Learning. My supervisors are Professor José Miguel Hernández-Lobato, Professor Bernhard Schölkopf, and Dr Hong Ge.

I am keen on basic research in machine learning and its scientific applications. My research interest lies at the intersection of probabilistic methods, deep learning, and causal inference. I aim to develop efficient machine learning methods for robust prediction and realistic data generation.

Interests
  • Representation Learning
  • Generative Modeling
  • Multi-task/Meta Learning
  • Probabilistic Modeling
  • Causal Modeling
  • Optimization
  • AI for Science
Education

  • PhD in Machine Learning

    University of Cambridge and Max Planck Institute for Intelligent Systems

  • MPhil in Machine Learning and Machine Intelligence, Distinction, 2021

    University of Cambridge

  • BSc (Hons) in Mathematics, First Class, 2020

    University of Manchester

Publications

See also my Google Scholar profile or search on my publication page.
Wen Wu*, Wenlin Chen*, Chao Zhang, Philip C. Woodland (2023). It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation. arXiv preprint 2310.00486.

PDF Cite Code arXiv

Wenlin Chen*, Julien Horwood*, Juyeon Heo, José Miguel Hernández-Lobato (2023). Leveraging Task Structures for Improved Identifiability in Neural Network Representations. ICML 2023 SCIS Workshop.

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Wenlin Chen, Hong Ge (2023). Neural Characteristic Activation Value Analysis for Improved ReLU Network Feature Learning. arXiv preprint 2305.15912.

PDF Cite Code arXiv

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

PDF Cite Code Slides OpenReview arXiv

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

PDF Cite Code Video OpenReview arXiv

Austin Tripp, Wenlin Chen, José Miguel Hernández-Lobato (2022). An Evaluation Framework for the Objective Functions of De Novo Drug Design Benchmarks. ICLR 2022 MLDD Workshop.

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Wenlin Chen (2021). Causal Representation Learning for Latent Space Optimization. MPhil Thesis, University of Cambridge.

PDF Cite Cambridge

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

PDF Cite Code Video Springer Link arXiv

Contact

  • wc337@cam.ac.uk
  • St Edmund’s College, Cambridge CB3 0BN, United Kingdom,