Fast and flexible AutoML with learning guarantees.

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on

Repository for collection of research papers on privacy.

[ICML2026] The first, fully verified, sorry-free, large-scale Lean 4 library for statistical learning theory, covering infrastructures for mordern statistics and learning theory.

Distributional Generalization in NLP. A roadmap.

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Model Zoos for Continual Learning (ICLR 22)

Formal Psychological Models of Categorization and Learning

Material for 'Mathematics of Deep Learning Workshop' (Invited Talk)

Scinis-learn is a package of non-OOP functions for Machine Learning developed by young Moroccan AI engineering students from scratch.

#UAI2020 Codes for PAC-Bayesian Contrastive Unsupervised Representation Learning

Code for paper "Efficient Sparse Coding using Hierarchical Riemannian Pursuit," in IEEE Transactions on Signal Processing, Y. Xue, V. K. N. Lau and S. Cai, doi: 10.1109/TSP.2021.3093769.

Lean4 kernel for synthetic formalization and discovery of statistical learning theory. First and complete formalization of the 5 way fundamental theorem. Typed premise + human-guided, AI-driven proof search across PAC, online, and Gold paradigms. The infrastructure forced by the types produced original mathematics.

Official code for Paper: "Can One Modality Model Synergize Training of Other Modality Models?" implemented in PyTorch

Solutions and Codes Example for Assignments of Machine Learning Foundation, Fall 2020, National Taiwan University

Official implementation of On-Demand Sampling: Learning Optimally from Multiple Distributions (Neurips 2022)

A Python implementation of the Neural Tangent Kernel (jacot et al, 2018)

Implementation of https://arxiv.org/abs/2106.03027

source code of NeurIPS 2021 paper: "Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound"