arXiv research

Novel Hilbert space Gaussian process improves sequential design accuracy and efficiency.

Advanced kernels improve Gaussian process accuracy by incorporating domain knowledge.

New algorithm converts data into sub-gaussian designs efficiently.

Designs an MLP from LDA for multi-Gaussian class classification.

Adaptive batching improves Gaussian process surrogates for noisy level set estimation.

The chapter compares Gaussian process models for stochastic simulators with varying noise.

The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.

Two methods improve Gaussian process predictive distributions' calibration.

We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization. We adapt…

The paper optimizes designs for distinguishing between Gaussian process models.

Improves Bayesian optimisation for engineering design problems with many variables.

Develops active learning for Jump Gaussian Process models.

The paper examines Gaussian process means under misspecified likelihoods and smoothness.

The Lasso method is analyzed for high-dimensional regression with Gaussian designs, leading to new insights on its performance.

We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets. Bayesian optimization guides the choice of experiments during materials design and discovery to find good material designs in as few experiments as p…

Bayesian surrogate models reduce uncertainty in high-dimensional design optimisation problems.

Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification. Adaptive sampling for multi-fidelity Gaussian process is a changing task due to the fact that not only we seek to estimate the next samplin…

Design of experiments improves validation of biomolecular networks.

We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The truncated variables are assumed latent and integrated out to induce a marginal m…

This paper reviews Gaussian process-based multi-fidelity techniques for different fidelity relationships.

The paper optimizes interpolation schedules in generative models to improve sampling accuracy.

Bayesian methods improve drug discovery experiment design.

Enhances topology optimization with multiclass microstructures using latent variable Gaussian process.

New mechanisms improve differential privacy for scalar queries.

Gradient-free framework for Bayesian experimental design in complex systems.

Bayesian sOED uses PG reinforcement learning for efficient experiment design.

New method for mixed-variable GSA improves material design efficiency.

Gaussian process priors are commonly used in aerospace design for performing Bayesian optimization. Nonetheless, Gaussian processes suffer two significant drawbacks: outliers are a priori assumed unlikely, and the posterior variance conditioned on observed data depends only on the locations of those data, not the assoc…

Stochastic gradient descent improves Gaussian process regression.

Paper proposes efficient algorithms for designing SLOPE penalty sequences.

Performing statistical inference in high-dimension is an outstanding challenge. A major source of difficulty is the absence of precise information on the distribution of high-dimensional estimators. Here, we consider linear regression in the high-dimensional regime $p\gg n$. In this context, we would like to perform in…

Quasi-experimental research designs, such as regression discontinuity and interrupted time series, allow for causal inference in the absence of a randomized controlled trial, at the cost of additional assumptions. In this paper, we provide a framework for discontinuity-based designs using Bayesian model comparison and …

New method designs joint initial noises for diffusion models to improve diversity and alignment.

Adaptive calibration improves model accuracy with fewer simulations.

LaMBO optimizes biological sequences using autoencoders and Bayesian optimization.

Fast approximate inference for non-Gaussian data.

We consider a class of misspecified dynamical models where the governing term is only approximately known. Under the assumption that observations of the system's evolution are accessible for various initial conditions, our goal is to infer a non-parametric correction to the misspecified driving term such as to faithful…

New algorithm recovers sparse signals robustly against Gaussian noise and adaptive adversaries.

DeepICMGP surrogate models multiple outputs efficiently.

CRPS improves GP-based sequential design for chemical space.

New method uses diffusion models for Bayesian inverse problems.

Designs efficient algorithms to maximize the expectation of Gaussian random variables.

We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new me…

TAD efficiently finds optimal settings for advanced manufacturing.

Gaussian process regression helps approximate Bayesian inverse problems efficiently.

Materials design can be cast as an optimization problem with the goal of achieving desired properties, by varying material composition, microstructure morphology, and processing conditions. Existence of both qualitative and quantitative material design variables leads to disjointed regions in property space, making the…

Scalable GP model tackles big data, categorical factors, and multiple responses.

A new Gaussian process framework uses neural feature maps for scalable, accurate inference.