Author name cluster

Javier Gonzalez

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

2 author rows

JMLR Journal 2021 Journal Article

GIBBON: General-purpose Information-Based Bayesian Optimisation

Henry B. Moss
David S. Leslie
Javier Gonzalez
Paul Rayson

This paper describes a general-purpose extension of max-value entropy search, a popular approach for Bayesian Optimisation (BO). A novel approximation is proposed for the information gain -- an information-theoretic quantity central to solving a range of BO problems, including noisy, multi-fidelity and batch optimisations across both continuous and highly-structured discrete spaces. Previously, these problems have been tackled separately within information-theoretic BO, each requiring a different sophisticated approximation scheme, except for batch BO, for which no computationally-lightweight information-theoretic approach has previously been proposed. GIBBON (General-purpose Information-Based Bayesian OptimisatioN) provides a single principled framework suitable for all the above, out-performing existing approaches whilst incurring substantially lower computational overheads. In addition, GIBBON does not require the problem's search space to be Euclidean and so is the first high-performance yet computationally light-weight acquisition function that supports batch BO over general highly structured input spaces like molecular search and gene design. Moreover, our principled derivation of GIBBON yields a natural interpretation of a popular batch BO heuristic based on determinantal point processes. Finally, we analyse GIBBON across a suite of synthetic benchmark tasks, a molecular search loop, and as part of a challenging batch multi-fidelity framework for problems with controllable experimental noise. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

PDF Details

UAI Conference 2019 Conference Paper

Active Multi-Information Source Bayesian Quadrature

Alexandra Gessner
Javier Gonzalez
Maren Mahsereci

Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far, active BQ learning schemes focus merely on the integrand itself as information source, and do not allow for information transfer from cheaper, related functions. Here, we set the scene for active learning in BQ when multiple related information sources of variable cost (in input and source) are accessible. This setting arises for example when evaluating the integrand requires a complex simulation to be run that can be approximated by simulating at lower levels of sophistication and at lesser expense. We construct meaningful cost-sensitive multi-source acquisition-rates as an extension to common utility functions from vanilla BQ (VBQ), and discuss pitfalls that arise from blindly generalizing. In proof-of-concept experiments we scrutinize the behavior of our generalized acquisition functions. On an epidemiological model, we demonstrate that active multi-source BQ (AMS-BQ) is more cost-efficient than VBQ in learning the integral to a good accuracy.

Details

NeurIPS Conference 2019 Conference Paper

Meta-Surrogate Benchmarking for Hyperparameter Optimization

Aaron Klein
Zhenwen Dai
Frank Hutter
Neil Lawrence
Javier Gonzalez

Despite the recent progress in hyperparameter optimization (HPO), available benchmarks that resemble real-world scenarios consist of a few and very large problem instances that are expensive to solve. This blocks researchers and practitioners no only from systematically running large-scale comparisons that are needed to draw statistically significant results but also from reproducing experiments that were conducted before. This work proposes a method to alleviate these issues by means of a meta-surrogate model for HPO tasks trained on off-line generated data. The model combines a probabilistic encoder with a multi-task model such that it can generate inexpensive and realistic tasks of the class of problems of interest. We demonstrate that benchmarking HPO methods on samples of the generative model allows us to draw more coherent and statistically significant conclusions that can be reached orders of magnitude faster than using the original tasks. We provide evidence of our findings for various HPO methods on a wide class of problems.

PDF Details

ICRA Conference 1998 Conference Paper

Hierarchical Graph Search for Mobile Robot Path Planning

Juan A. Fernandez
Javier Gonzalez

This paper focuses on the utilization of a highly structured model of the environment for mobile robot path planning, called hierarchical graph (or H-Graph). It can be seen as a set of different views of the same environment (hierarchical levels), each of them capturing different amounts of detail. We present a hierarchical graph search algorithm computationally less expensive than a conventional plain graph search algorithm that uses only the most detailed hierarchical level of the H-Graph. We also define the weights assigned to each arc of the H-Graph in such a way that they can represent the typical costs that a mobile robot has to assume when it navigates: energy consumption, time elapsed, etc. An example of the application of the hierarchical graph search algorithm to a portion of our research centre serves for demonstrating how the algorithm can guarantee the optimality of the paths found, and the important reduction in computational cost that it achieves with respect to a classical plain graph search.

Details

ICRA Conference 1997 Conference Paper

Mobile robot motion estimation from a range scan sequence

Javier Gonzalez
Rafael Gutiérrez

This paper presents a innovative algorithm to estimate the motion parameters of a mobile robot equipped with a radial laser rangefinder. Our method is based on the spatial and temporal linearization of the range function, which leads to a velocity constraint equation for the scanned points. The proposed formulation computes the motion vectors of the scanned points as they move from scan to scan in the sequence. This motion field can be very useful in a number of applications including detection and tracking of moving objects. Experimental results are presented, showing that good results are achieved with both real and synthetic data.

Details