650 citations · 1.3k across the 33 of their papers we have counts for
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Global Convergence of Direct Policy Search for State-Feedback Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential
Xingang Guo, Bin Hu
Direct policy search has been widely applied in modern reinforcement learning and continuous control. However, the theoretical properties of direct policy search on nonsmooth robus…
Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies
Bin Hu, Kaiqing Zhang, Na Li +3
Gradient-based methods have been widely used for system design and optimization in diverse application domains. Recently, there has been a renewed interest in studying theoretical…
Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition
Huayu Chen, Huanhuan He, Jing Zhu +6
Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yie…
Provable Acceleration of Heavy Ball beyond Quadratics for a Class of Polyak-Łojasiewicz Functions when the Non-Convexity is Averaged-Out
Jun-Kun Wang, Chi-Heng Lin, Andre Wibisono +1
Heavy Ball (HB) nowadays is one of the most popular momentum methods in non-convex optimization. It has been widely observed that incorporating the Heavy Ball dynamic in gradient-b…
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks
Binyan Hu, Yu Sun, A. K. Qin
Deep neural networks (DNNs) often rely on massive labelled data for training, which is inaccessible in many applications. Data augmentation (DA) tackles data scarcity by creating n…
Exact Formulas for Finite-Time Estimation Errors of Decentralized Temporal Difference Learning with Linear Function Approximation
Xingang Guo, Bin Hu
In this paper, we consider the policy evaluation problem in multi-agent reinforcement learning (MARL) and derive exact closed-form formulas for the finite-time mean-squared estimat…