Riashat Islam
Riashat Islam
PhD Student, McGill University
Verified email at mail.mcgill.ca - Homepage
TitleCited byYear
Deep reinforcement learning that matters
P Henderson*, R Islam*, P Bachman, J Pineau, D Precup, D Meger
Proceedings of 32nd AAAI Conference on Artificial Intelligence (AAAI-18), 2017
Deep Bayesian Active Learning with Image Data
Y Gal, R Islam, Z Ghahramani
Proceedings of the 34th International Conference on Machine Learning (ICML-17), 2017
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
R Islam, P Henderson, M Gomrokchi, D Precup
Reproducibility in Machine Learning Workshop, ICML 2017, 2017
Bayesian Hypernetworks
D Krueger, CW Huang, R Islam, R Turner, A Lacoste, A Courville
arXiv preprint arXiv:1710.04759, 2017
Active Learning for High Dimensional Inputs using Bayesian Convolutional Neural Networks
R Islam, Y Gal, Z Ghahramani
University of Cambridge, Masters Thesis, 2016
Bayesian Policy Gradients via Alpha Divergence Dropout Inference
P Henderson, T Doan, R Islam, D Meger
Bayesian Deep Learning Workshop, NIPS 2017, 2017
RE-EVALUATE: Reproducibility in Evaluating Reinforcement Learning Algorithms
K Khetarpal, Z Ahmed, A Cianflone, R Islam, J Pineau
An Introduction to Deep Reinforcement Learning
V Francois-Lavet, P Henderson, R Islam, MG Bellemare, J Pineau
arXiv preprint arXiv:1811.12560, 2018
Prioritizing Starting States for Reinforcement Learning
A Tavakoli, V Levdik, R Islam, P Kormushev
arXiv preprint arXiv:1811.11298, 2018
VFunc: a Deep Generative Model for Functions
P Bachman, R Islam, A Sordoni, Z Ahmed
Prediction and Generative Modeling in Reinforcement Learning workshop, ICML 2018, 2018
Alpha-Divergences in Variational Dropout
B Mazoure, R Islam
arXiv preprint:1711.04345v1, 2017
Comparing Convergence of Policy Gradient Methods in Reinforcement Learning
R Islam, G Lever, J Shawe-Taylor
University College London (UCL), Undergraduate Thesis, 2015
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Articles 1–12