Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
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Updated
Feb 18, 2023
Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning
Fast and flexible AutoML with learning guarantees.
🔬 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.
AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics
Model Zoos for Continual Learning (ICLR 22)
Material for 'Mathematics of Deep Learning Workshop' (Invited Talk)
source code of NeurIPS 2021 paper: "Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound"
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.
Formal Psychological Models of Categorization and Learning
Distributional Generalization in NLP. A roadmap.
PAC-Bayesian Binary Activated Deep Neural Networks
Implementation of https://arxiv.org/abs/2106.03027
A program that learns your polynomial using just two queries
Python code for the post "Binary Search on Graphs"
Learning ReLU networks to high uniform accuracy is intractable (ICLR 2023)
Solutions and Codes Example for Assignments of Machine Learning Foundation, Fall 2020, National Taiwan University
Scinis-learn is a package of non-OOP functions for Machine Learning developed by young Moroccan AI engineering students from scratch.
A Python implementation of the Neural Tangent Kernel (jacot et al, 2018)
Official implementation of On-Demand Sampling: Learning Optimally from Multiple Distributions (Neurips 2022)
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