Federated optimization, an emerging paradigm which finds wide real-world...
Many approaches for optimizing decision making systems rely on gradient ...
Existing neural active learning algorithms have aimed to optimize the
pr...
We introduce Goat, a fine-tuned LLaMA model that significantly outperfor...
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...
Although the existing max-value entropy search (MES) is based on the wid...
As the use of machine learning (ML) models is becoming increasingly popu...
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...
Information-based Bayesian optimization (BO) algorithms have achieved
st...
Value-at-risk (VaR) is an established measure to assess risks in critica...
Deep Gaussian processes (DGPs), a hierarchical composition of GP models,...
This paper presents an information-theoretic framework for unifying acti...
This paper presents a novel approach to top-k ranking Bayesian optimizat...
The problem of inverse reinforcement learning (IRL) is relevant to a var...
This paper studies the problem of approximately unlearning a Bayesian mo...
This paper presents the private-outsourced-Gaussian process-upper confid...
Collaborative machine learning (ML) is an appealing paradigm to build
hi...
This paper presents a novel unifying framework of bilinear LSTMs that ca...