Riashat Islam

According to our database1, Riashat Islam authored at least 29 papers between 2017 and 2023.

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Bibliography

2023
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models.
Trans. Mach. Learn. Res., 2023

PcLast: Discovering Plannable Continuous Latent States.
CoRR, 2023

Ignorance is Bliss: Robust Control via Information Gating.
CoRR, 2023

Understanding and Addressing the Pitfalls of Bisimulation-based Representations in Offline Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Ignorance is Bliss: Robust Control via Information Gating.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Principled Offline RL in the Presence of Rich Exogenous Information.
Proceedings of the International Conference on Machine Learning, 2023

Behavior Prior Representation learning for Offline Reinforcement Learning.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Representation Learning in Deep RL via Discrete Information Bottleneck.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Offline Policy Optimization in RL with Variance Regularizaton.
CoRR, 2022

Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning.
CoRR, 2022

Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information.
CoRR, 2022

Guaranteed Discovery of Controllable Latent States with Multi-Step Inverse Models.
CoRR, 2022

Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning.
CoRR, 2021

2019
Marginalized State Distribution Entropy Regularization in Policy Optimization.
CoRR, 2019

Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning.
CoRR, 2019

Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods.
CoRR, 2019

Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift.
CoRR, 2019

Transfer Learning by Modeling a Distribution over Policies.
CoRR, 2019

InfoBot: Transfer and Exploration via the Information Bottleneck.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
An Introduction to Deep Reinforcement Learning.
Found. Trends Mach. Learn., 2018

Prioritizing Starting States for Reinforcement Learning.
CoRR, 2018

VFunc: a Deep Generative Model for Functions.
CoRR, 2018

Deep Reinforcement Learning That Matters.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Bayesian Policy Gradients via Alpha Divergence Dropout Inference.
CoRR, 2017

Alpha-Divergences in Variational Dropout.
CoRR, 2017

Bayesian Hypernetworks.
CoRR, 2017

Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control.
CoRR, 2017

Deep Bayesian Active Learning with Image Data.
Proceedings of the 34th International Conference on Machine Learning, 2017


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