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20202023
most citedMulti-Fidelity Bayesian Optimization via Deep Neural Networks

22 citations · 40 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.LG2023★ 2 cited

Multi-Resolution Active Learning of Fourier Neural Operators

Shibo Li, Xin Yu, Wei Xing +3

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 p…

cs.LG2022★ 2 cited

Batch Multi-Fidelity Active Learning with Budget Constraints

Shibo Li, Jeff M. Phillips, Xin Yu +2

Learning functions with high-dimensional outputs is critical in many applications, such as physical simulation and engineering design. However, collecting training examples for the…

cs.LG2022★ 1 cited

Nonparametric Embeddings of Sparse High-Order Interaction Events

Zheng Wang, Yiming Xu, Conor Tillinghast +3

High-order interaction events are common in real-world applications. Learning embeddings that encode the complex relationships of the participants from these events is of great imp…

cs.LG2020★ 7 cited

Deep Multi-Fidelity Active Learning of High-dimensional Outputs

Shibo Li, Robert M. Kirby, Shandian Zhe

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

cs.LG2020★ 22 cited

Multi-Fidelity Bayesian Optimization via Deep Neural Networks

Shibo Li, Wei Xing, Mike Kirby +1

Bayesian optimization (BO) is a popular framework to optimize black-box functions. In many applications, the objective function can be evaluated at multiple fidelities to enable a…