Recent years have witnessed a surge of interest in developing trustworthy methods to
evaluate the value of data in many real-world applications (eg, collaborative machine learning, …

This paper studies the problem of approximately unlearning a Bayesian model from a small
subset of the training data to be erased. We frame this problem as one of minimizing the …

Bayesian optimization (BO) is a prominent approach to optimizing expensive-to-evaluate
black-box functions. The massive computational capability of edge devices such as mobile …

In collaborative machine learning (CML), multiple agents pool their resources (eg, data)
together for a common learning task. In realistic CML settings where the agents are self-…

The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement
Learning (FRL) to encourage multiple agents to federatively build a better decision-…

Collaborative machine learning (ML) is an appealing paradigm to build high-quality ML
models by training on the aggregated data from many parties. However, these parties are only …

Data valuation arises as a non-trivial challenge in real-world use cases such as collaborative
machine learning, federated learning, trusted data sharing, data marketplaces. The value …

This paper presents a novel unifying framework of anytime sparse Gaussian process regression
(SGPR) models that can produce good predictive performance fast and improve their …

Data valuation in machine learning (ML) is an emerging research area that studies the worth
of data in ML. Data valuation is used in collaborative ML to determine a fair compensation …

Recent years have witnessed a surging interest in Neural Architecture Search (NAS). Various
algorithms have been proposed to improve the search efficiency and effectiveness of NAS, …