5 citations · 37 across the 41 of their papers we have counts for
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Robust Entropy-regularized Markov Decision Processes
Tien Mai, Patrick Jaillet
Stochastic and soft optimal policies resulting from entropy-regularized Markov decision processes (ER-MDP) are desirable for exploration and imitation learning applications. Motiva…
Differentially Private Federated Bayesian Optimization with Distributed Exploration
Zhongxiang Dai, Bryan Kian Hsiang Low, Patrick Jaillet
Bayesian optimization (BO) has recently been extended to the federated learning (FL) setting by the federated Thompson sampling (FTS) algorithm, which has promising applications su…
Trusted-Maximizers Entropy Search for Efficient Bayesian Optimization
Quoc Phong Nguyen, Zhaoxuan Wu, Bryan Kian Hsiang Low +1
Information-based Bayesian optimization (BO) algorithms have achieved state-of-the-art performance in optimizing a black-box objective function. However, they usually require sever…
Convolutional Normalizing Flows for Deep Gaussian Processes
Haibin Yu, Dapeng Liu, Yizhou Chen +2
Deep Gaussian processes (DGPs), a hierarchical composition of GP models, have successfully boosted the expressive power of their single-layer counterpart. However, it is impossible…
Value-at-Risk Optimization with Gaussian Processes
Quoc Phong Nguyen, Zhongxiang Dai, Bryan Kian Hsiang Low +1
Value-at-risk (VaR) is an established measure to assess risks in critical real-world applications with random environmental factors. This paper presents a novel VaR upper confidenc…