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.

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)

The first comprehensive Lean 4 formalization of statistical learning theory, featuring Gaussian Lipschitz concentration and Dudley's entropy integral-establishes a reusable foundation for formalizing ML theory.

Formal Psychological Models of Categorization and Learning

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

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

#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.

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)

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

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"

A program that learns your polynomial using just two queries