5 citations · 12 across the 6 of their papers we have counts for
7 papers
Sample-Then-Optimize Batch Neural Thompson Sampling
Zhongxiang Dai, Yao Shu, Bryan Kian Hsiang Low +1
Bayesian optimization (BO), which uses a Gaussian process (GP) as a surrogate to model its objective function, is popular for black-box optimization. However, due to the limitation…
Optimal Information Provision for Strategic Hybrid Workers
Sohil Shah, Saurabh Amin, Patrick Jaillet
We study the problem of information provision by a strategic central planner who can publicly signal about an uncertain infectious risk parameter. Signalling leads to an updated pu…
Rectified Max-Value Entropy Search for Bayesian Optimization
Quoc Phong Nguyen, Bryan Kian Hsiang Low, Patrick Jaillet
Although the existing max-value entropy search (MES) is based on the widely celebrated notion of mutual information, its empirical performance can suffer due to two misconceptions…
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…