Felix Biggs

Find me on twitter, github and linkedin or contact me at [email protected].

I recently finished my PhD in Machine Learning (FAI CDT) at University College London advised by Benjamin Guedj. Previously I completed the UCL Machine Learning MSc and the Physics Tripos at Cambridge. My research has focused on understanding how and when machine learning methods work, with the motivation of making them safer and more predictable.

I am actively seeking a next role, ideally one with robust research component. If you would like to collaborate or discuss further, don’t hesitate to get in touch!

Papers and Preprints [Google Scholar]

MMD-FUSE: Learning and Combining Kernels for Two-Sample Testing Without Data Splitting.
Felix Biggs†, Antonin Schrab†, Arthur Gretton.
Neural Information Processing Systems (NeurIPS) 2023. Spotlight * [arXiv:2306.08777]

Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty.
Felix Biggs, Benjamin Guedj.
Artificial Intelligence and Statistics (AISTATS), 2023. [arXiv:2210.11289]

On Margins and Generalisation for Voting Classifiers.
Felix Biggs, Valentina Zantedeschi, Benjamin Guedj.
Neural Information Processing Systems (NeurIPS) 2022. [arXiv:2206.04607]

Non-Vacuous Generalisation Bounds for Shallow Networks.
Felix Biggs, Benjamin Guedj.
International Conf. Machine Learning (ICML) 2022. [arXiv:2202.01627]

On Margins and Derandomisation in PAC-Bayes.
Felix Biggs, Benjamin Guedj.
Artificial Intelligence and Statistics (AISTATS), 2022. [arXiv:2107.03955]

A Note on the Efficient Evaluation of PAC-Bayes Bounds.
Felix Biggs.
Preprint. [arXiv:2209.05188]

Differentiable PAC–Bayes Objectives with Partially Aggregated Neural Networks.
Felix Biggs, Benjamin Guedj.
Entropy 2021, 23, 1280. [doi:10.3390/e23101280]

† = equal contribution. * = about 3% of NeurIPS submissions receive a spotlight.