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.

We analyze algorithms to learn Gaussian Bayesian networks with known structure up to a bounded error in total variation distance.

OSRL (Optimal Representation Learning in Multi-Task Bandits) comprises an algorithm that addresses the problem of sample complexity with fixed confidence in Multi-Task Bandit problems. Published at the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI23)

Python implementation of algorithms for Best Policy Identification in Markov Decision Processes

This package was the result of master thesis that is seen at link https://tede2.uepg.br/jspui/handle/prefix/152 and in the article https://doi.org/10.5335/rbca.2015.3727.

Python utilities to compute a lower bound of the expected sample complexity to identify the best arm in a bandit model