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 limitations of …

Kernelized bandits, also known as Bayesian optimization (BO), has been a prevalent
method for optimizing complicated black-box reward functions. Various BO algorithms have …

Large language models (LLMs) have shown remarkable instruction-following capabilities
and achieved impressive performances in various applications. However, the performances …

Many real-world experimental design problems (a) evaluate multiple experimental
conditions in parallel and (b) replicate each condition multiple times due to large and …

Existing neural active learning algorithms have aimed to optimize the predictive
performance of neural networks (NNs) by selecting data for labelling. However, other than a …

Physics-Informed Neural Networks (PINNs), which incorporate PDEs as soft constraints,
train with a composite loss function that contains multiple training point types: different types …

Federated optimization, an emerging paradigm which finds wide real-world applications
such as federated learning, enables multiple clients (eg, edge devices) to collaboratively …

Federated Reinforcement Learning (FedRL) encourages distributed agents to learn
collectively from each other's experience to improve their performance without exchanging …

Federated learning (FL) has demonstrated great potential in revolutionizing distributed
machine learning, and tremendous efforts have been made to extend it beyond the original …

The Nash equilibrium (NE) is a classic solution concept for normal-form games that is stable
under potential unilateral deviations by self-interested agents. Bayesian optimization (BO) …