I am a final-year PhD student in Machine Learning at Imperial College London, supervised by Yingzhen Li. I obtained my MPhil degree in Machine Learning and Machine Intelligence from the University of Cambridge in 2020, supervised by José Miguel Hernández-Lobato. Before that, I graduated with a BSc (Hons) in Mathematics and Statistics from McMaster University.

During my PhD, I mainly work on uncertainty quantification, robustness in large-scale deep sequence models, and generative models. In general, I am also interested in probabilistic continual learning and meta-learning. I aim to develop reliable algorithms with improved calibration, generalization, and robustness.

Currently, I am working on continual learning as a research intern in the Approximate Bayesian Inference team at RIKEN AIP in Tokyo with Mohammad Emtiyaz Khan and Thomas Moellenhoff. From June to November 2023, I worked as a research intern in the Responsible AI team at ByteDance Research in London, where I researched on machine learning fairness.

Publications

An up-to-date list of publications can be found on my Google Scholar profile.

*equal contribution

• Wenlong Chen*, Wenlin Chen*, Lapo Rastrelli, Yingzhen Li. Your Image is Secretly the Last Frame of a Pseudo Video. Preprint 2024 [link]
• Wenlong Chen*, Yegor Klochkov*, Yang Liu. Post-hoc Bias Scoring Is Optimal For Fair Classification. ICLR 2024 (spotlight) [link]
• Wenlong Chen, Yingzhen Li. Calibrating Transformers via Sparse Gaussian Processes. ICLR 2023 [link]
• Andrew Campbell*, Wenlong Chen*, Vincent Stimper*, Jose Miguel Hernandez-Lobato, Yichuan Zhang. A Gradient Based Strategy for Hamiltonian Monte Carlo Hyperparameter Optimization. ICML 2021 [link]

Activities

Organizer

I have been involved in the organization of the workshop: 2nd UNCV at ICCV 2023

Reviewer

NeurIPS, ICML, AISTATS, TMLR, UNCV

Contact

Feel free to reach out to me at wenlong.chen21 (at) imperial.ac.uk