7 papers
Generative Bayesian Optimization: Generative Models as Acquisition Functions
Rafael Oliveira, Daniel M. Steinberg, Edwin V. Bonilla
We present a general strategy for turning generative models into candidate solution samplers for batch Bayesian optimization (BO). The use of generative models for BO enables large…
Multi-Scale Wavelet Transformers for Operator Learning of Dynamical Systems
Xuesong Wang, Michael Groom, Rafael Oliveira +3
Recent years have seen a surge in data-driven surrogates for dynamical systems that can be orders of magnitude faster than numerical solvers. However, many machine learning-based m…
Variational Transdimensional Inference
Laurence Davies, Dan Mackinlay, Rafael Oliveira +1
The expressiveness of flow-based models combined with stochastic variational inference (SVI) has expanded the application of optimization-based Bayesian inference to highly complex…
Thompson Sampling in Function Spaces via Neural Operators
Rafael Oliveira, Xuesong Wang, Kian Ming A. Chai +1
We propose an extension of Thompson sampling to optimization problems over function spaces where the objective is a known functional of an unknown operator's output. We assume that…
Amortized Active Generation of Pareto Sets
Daniel M. Steinberg, Asiri Wijesinghe, Rafael Oliveira +3
We introduce active generation of Pareto sets (A-GPS), a new framework for online discrete black-box multi-objective optimization (MOO). A-GPS learns a generative model of the Pare…
Variational Search Distributions
Daniel M. Steinberg, Rafael Oliveira, Cheng Soon Ong +1
We develop VSD, a method for conditioning a generative model of discrete, combinatorial designs on a rare desired class by efficiently evaluating a black-box (e.g. experiment, simu…