9 papers
Only Linear Constraints Survive Coarse-Graining: Evaluating Physics-Constrained Neural Operators on Stochastically Forced Turbulence
Michael Groom, Rafael Oliveira
A physics-informed loss adds a discretised PDE residual to the data term, but is only strictly valid if the equations being enforced are closed on the fields being fitted. This fai…
Sequential operator learning under dependent data
Rafael Oliveira
Learning operators from sequentially collected data arises in adaptive experimental design, Bayesian optimization, and dynamical-system modelling, where observations may be depende…
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…
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…
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…
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…
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…
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…
Bayesian Adaptive Calibration and Optimal Design
Rafael Oliveira, Dino Sejdinovic, David Howard +1
The process of calibrating computer models of natural phenomena is essential for applications in the physical sciences, where plenty of domain knowledge can be embedded into simula…