no code implementations • 18 Mar 2024 • Haque Ishfaq, Thanh Nguyen-Tang, Songtao Feng, Raman Arora, Mengdi Wang, Ming Yin, Doina Precup
We study offline multitask representation learning in reinforcement learning (RL), where a learner is provided with an offline dataset from different tasks that share a common representation and is asked to learn the shared representation.
no code implementations • 6 Jan 2024 • Thanh Nguyen-Tang, Raman Arora
This result is surprising, given that the prior work suggested an unfavorable sample complexity of the RO-based algorithm compared to the VS-based algorithm, whereas posterior sampling is rarely considered in offline RL due to its explorative nature.
1 code implementation • 20 Oct 2023 • Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan Tran, Jaesik Choi
To mitigate such difficulties, we introduce SigFormer, a novel deep learning model that combines the power of path signatures and transformers to handle sequential data, particularly in cases with irregularities.
1 code implementation • 23 Jun 2023 • Nguyen Ngoc-Hieu, Nguyen Hung-Quang, The-Anh Ta, Thanh Nguyen-Tang, Khoa D Doan, Hoang Thanh-Tung
The ability to detect OOD data is a crucial aspect of practical machine learning applications.
1 code implementation • 24 Feb 2023 • Thanh Nguyen-Tang, Raman Arora
We corroborate the statistical and computational efficiency of VIPeR with an empirical evaluation on a wide set of synthetic and real-world datasets.
1 code implementation • CVPR 2023 • A. Tuan Nguyen, Thanh Nguyen-Tang, Ser-Nam Lim, Philip H.S. Torr
Test Time Adaptation offers a means to combat this problem, as it allows the model to adapt during test time to the new data distribution, using only unlabeled test data batches.
no code implementations • 23 Nov 2022 • Thanh Nguyen-Tang, Ming Yin, Sunil Gupta, Svetha Venkatesh, Raman Arora
To the best of our knowledge, these are the first $\tilde{\mathcal{O}}(\frac{1}{K})$ bound and absolute zero sub-optimality bound respectively for offline RL with linear function approximation from adaptive data with partial coverage.
no code implementations • 26 Jun 2022 • Mengyan Zhang, Thanh Nguyen-Tang, Fangzhao Wu, Zhenyu He, Xing Xie, Cheng Soon Ong
We consider the problem of personalised news recommendation where each user consumes news in a sequential fashion.
1 code implementation • 3 Mar 2022 • Thanh Nguyen-Tang
This thesis rigorously studies fundamental reinforcement learning (RL) methods in modern practical considerations, including robust RL, distributional RL, and offline RL with neural function approximation.
1 code implementation • ICLR 2022 • Thanh Nguyen-Tang, Sunil Gupta, A. Tuan Nguyen, Svetha Venkatesh
Moreover, we show that our method is more computationally efficient and has a better dependence on the effective dimension of the neural network than an online counterpart.
no code implementations • 24 Jul 2021 • Hung Tran-The, Sunil Gupta, Thanh Nguyen-Tang, Santu Rana, Svetha Venkatesh
We propose a novel approach that uses a hybrid of offline learning with online exploration.
no code implementations • 11 Mar 2021 • Thanh Nguyen-Tang, Sunil Gupta, Hung Tran-The, Svetha Venkatesh
To the best of our knowledge, this is the first theoretical characterization of the sample complexity of offline RL with deep neural network function approximation under the general Besov regularity condition that goes beyond {the linearity regime} in the traditional Reproducing Hilbert kernel spaces and Neural Tangent Kernels.