Thanh Nguyen-Tang | DeepAI

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∙ 02/24/2023

Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards

We propose a novel offline reinforcement learning (RL) algorithm, namely...

0 Thanh Nguyen-Tang, et al. ∙

∙ 11/23/2022

On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation

Sample-efficient offline reinforcement learning (RL) with linear functio...

0 Thanh Nguyen-Tang, et al. ∙

∙ 06/26/2022

Two-Stage Neural Contextual Bandits for Personalised News Recommendation

We consider the problem of personalised news recommendation where each u...

1 Mengyan Zhang, et al. ∙

∙ 03/03/2022

On Practical Reinforcement Learning: Provable Robustness, Scalability, and Statistical Efficiency

This thesis rigorously studies fundamental reinforcement learning (RL) m...

0 Thanh Nguyen-Tang, et al. ∙

∙ 11/27/2021

Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization

Offline policy learning (OPL) leverages existing data collected a priori...

1 Thanh Nguyen-Tang, et al. ∙

∙ 07/24/2021

Combining Online Learning and Offline Learning for Contextual Bandits with Deficient Support

We address policy learning with logged data in contextual bandits. Curre...

5 Hung Tran-The, et al. ∙

∙ 03/11/2021

On Finite-Sample Analysis of Offline Reinforcement Learning with Deep ReLU Networks

This paper studies the statistical theory of offline reinforcement learn...

21 Thanh Nguyen-Tang, et al. ∙

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