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Found 16 papers, 6 papers with code
Some strategies for the improvement of a Spanish WordNet
Although there are currently several versions of Princeton WordNet for different languages, the lack of development of some of these versions does not make it possible to use them in different Natural Language Processing applications.
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires
Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens.
Invariant Priors for Bayesian Quadrature
Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task at hand.
Emulation of physical processes with Emukit
Decision making in uncertain scenarios is an ubiquitous challenge in real world systems.
RKHS-SHAP: Shapley Values for Kernel Methods
Feature attribution for kernel methods is often heuristic and not individualised for each prediction.
GIBBON: General-purpose Information-Based Bayesian OptimisatioN
This paper describes a general-purpose extension of max-value entropy search, a popular approach for Bayesian Optimisation (BO).
Good practices for Bayesian Optimization of high dimensional structured spaces
The increasing availability of structured but high dimensional data has opened new opportunities for optimization.
Polarization calibration techniques for new-generation VLBI
The calibration and analysis of polarization observations in Very Long Baseline Interferometry (VLBI) requires the use of specific algorithms that suffer from several limitations, closely related to assumptions in the data properties that may not hold in observations taken with new-generation VLBI equipment.
Instrumentation and Methods for Astrophysics
BOSS: Bayesian Optimization over String Spaces
This article develops a Bayesian optimization (BO) method which acts directly over raw strings, proposing the first uses of string kernels and genetic algorithms within BO loops.
Preferential Batch Bayesian Optimization
This direction has been mainly driven by the use of BO in machine learning hyper-parameter configuration problems.
BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design
Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings.
Meta-Surrogate Benchmarking for Hyperparameter Optimization
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.
Automatic Discovery of Privacy-Utility Pareto Fronts
Differential privacy is a mathematical framework for privacy-preserving data analysis.
Active Multi-Information Source Bayesian Quadrature
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.
Structured Variationally Auto-encoded Optimization
We tackle the problem of optimizing a black-box objective function defined over a highly-structured input space.
Preferential Bayesian Optimization
Bayesian optimization (BO) has emerged during the last few years as an effective approach to optimizing black-box functions where direct queries of the objective are expensive.
