Bayesian optimization is a sample-efficient black-box optimization procedure that is typically
applied to a small number of independent objectives. However, in practice we often wish to …

We propose a new causal inference framework to learn causal effects from multiple,
decentralized data sources in a federated setting. We introduce an adaptive transfer …

Abstract Multi-output Gaussian processes (MOGPs) leverage the flexibility and
interpretability of GPs while capturing structure across outputs, which is desirable, for …

Many applications, such as in physical simulation and engineering design, demand we
estimate functions with high-dimensional outputs. The training examples can be collected …

Multiway data-related learning tasks pose a huge challenge to the traditional regression
analysis techniques due to the existence of multidirectional relatedness. Simply vectorizing …

Multi-fidelity modeling and learning is important in physical simulation related applications. It
can leverage both low-fidelity and high-fidelity examples for training so as to reduce the cost …

We propose a Bayesian framework for estimating causal effects from federated
observational data sources. Bayesian causal inference is an important approach to learning …

Kernel machines are one of the most studied family of methods in machine learning. In the
exact setting, training requires to instantiate the kernel matrix, thereby prohibiting their …

Multi-fidelity fusion has become an important surrogate technique, which provides insights
into expensive computer simulations and effectively improves decision-making, eg …

Abstract Fourier Neural Operator (FNO) is a popular operator learning framework. It not only
achieves the state-of-the-art performance in many tasks, but also is efficient in training and …