Federated optimization, an emerging paradigm which finds wide real-world...
Existing neural active learning algorithms have aimed to optimize the
pr...
Federated Reinforcement Learning (FedRL) encourages distributed agents t...
Bayesian optimization (BO), which uses a Gaussian process (GP) as a surr...
Bayesian optimization (BO) is a widely-used sequential method for
zeroth...
Bayesian optimization (BO) has become popular for sequential optimizatio...
Recent works on neural contextual bandit have achieved compelling
perfor...
The expected improvement (EI) is one of the most popular acquisition
fun...
Neural architecture search (NAS) has gained immense popularity owing to ...
Bayesian optimization (BO) has recently been extended to the federated
l...
The growing literature of Federated Learning (FL) has recently inspired
...
Recently, Neural Architecture Search (NAS) has been widely applied to
au...
Recent years have witnessed a surging interest in Neural Architecture Se...
Value-at-risk (VaR) is an established measure to assess risks in critica...
This paper presents the private-outsourced-Gaussian process-upper confid...
Bayesian optimization (BO) is a prominent approach to optimizing
expensi...
This paper presents a recursive reasoning formalism of Bayesian optimiza...
A multi-layer deep Gaussian process (DGP) model is a hierarchical compos...
This paper presents novel mixed-type Bayesian optimization (BO) algorith...