Short bio

I am currently finishing my PhD at EPFL in the LIONS lab with Prof. Volkan Cevher, where I’ve broadly been interested in optimization for machine learning with a focus on stable training of deep learning models. During my studies, I interned with Amazon and ETH Zürich.

Selected publications

See publications for other publications and Google Scholar for the most up to date version.

Training Deep Learning Models with Norm-Constrained LMOs
Thomas Pethick, Wanyun Xie, Kimon Antonakopoulos, Zhenyu Zhu, Antonio Silveti-Falls and Volkan Cevher
International Conference on Machine Learning (ICML) 2025 (spotlight)
paper code

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Efficient Interpolation between Extragradient and Proximal Methods for Weak MVIs
Thomas Pethick, Ioannis Mavrothalassitis and Volkan Cevher
International Conference on Learning Representations (ICLR) 2025
paper

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SAMPa: Sharpness-aware Minimization Parallelized
Wanyun Xie, Thomas Pethick and Volkan Cevher
Neural Information Processing Systems (NeurIPS) 2024
paper code

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Stable nonconvex-nonconcave training via linear interpolation
Thomas Pethick, Wanyun Xie, Volkan Cevher
Neural Information Processing Systems (NeurIPS) 2023 (spotlight)
paper code

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Solving stochastic weak Minty variational inequalities without increasing batch size
Thomas Pethick, Olivier Fercoq, Puya Latafat, Panagiotis Patrinos and Volkan Cevher
International Conference on Learning Representations (ICLR) 2023
paper code

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Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
Thomas Pethick, Puya Latafat, Panagiotis Patrinos, Olivier Fercoq, Volkan Cevher
The International Conference on Learning Representations (ICLR) 2022 (spotlight)
paper code

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Content

A geometric view on optimization

• Polyak stepsize through a hyperplane projection interpretation

Online learning

• Follow the regularized leader

• Hedge and bandits

• Gaussian processes and Hedge for infinite armed bandits

Talks

• Provably beneficial artificial intelligence by Stuart Russell

• From causal inference to autoencoders and gene regulation by Caroline Uhler

Tidbits

• Various ways of writing Nesterov’s acceleration

• Acceleration convergence using a potential function

• Gradient boosting

• Bayesian logistic Regression

• The I/O model

All the posts can also be found in chronological order in the archive.

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Open source

Some of the projects I worked on prior to the PhD:

• Scalable Gaussian Processes for Economic Models. This codebase can be used to run high-dimensional scalable Gaussian Processes on Economic Models on a High Performance Computing cluster.

• Ensembled Deep Network for Global Optimization. This project explores the behavior of an ensembled variant of the architecture proposed by (Snoek et al 2015) on various Bayesian Optimization benchmark problems.

• Prolog code generation from Isabelle’s inner syntax. This project compiles a theorem prover written and proven with Isabelle and compiles it into Prolog. It does so in Haskell through several catamorphism that changes the Isabelle AST into a Prolog AST.

• CampusNet Sync. A Dropbox like inspired app to sync your computer with the filesystem used at the Technical University of Denmark.

• Anki Onenote importer. Allows one to import .mht files exported from OneNote into Anki.

… and more on Github including this site which was originally build by Hakyll with some added \(\text{\LaTeX}\) goods. I have since moved to the Executable Book Project for a well-maintained codebase with many of the same features.