Search Results for author:
Found 32 papers, 6 papers with code
SAFFRON: tranSfer leArning For Food-disease RelatiOn extractioN
The accelerating growth of big data in the biomedical domain, with an endless amount of electronic health records and more than 30 million citations and abstracts in PubMed, introduces the need for automatic structuring of textual biomedical data.
TransOpt: Transformer-based Representation Learning for Optimization Problem Classification
We propose a representation of optimization problem instances using a transformer-based neural network architecture trained for the task of problem classification of the 24 problem classes from the Black-box Optimization Benchmarking (BBOB) benchmark.
PS-AAS: Portfolio Selection for Automated Algorithm Selection in Black-Box Optimization
Our proposed method creates algorithm behavior meta-representations, constructs a graph from a set of algorithms based on their meta-representation similarity, and applies a graph algorithm to select a final portfolio of diverse, representative, and non-redundant algorithms.
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS).
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features.
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
In black-box optimization, it is essential to understand why an algorithm instance works on a set of problem instances while failing on others and provide explanations of its behavior.
Assessing the Generalizability of a Performance Predictive Model
A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model.
Sensitivity Analysis of RF+clust for Leave-one-problem-out Performance Prediction
That is, instead of considering cosine distance in the feature space, we consider a weighted distance measure, with weights depending on the relevance of the feature for the regression model.
Predefined domain specific embeddings of food concepts and recipes: A case study on heterogeneous recipe datasets
From the list of ingredients, domain-specific embeddings are created using the same embedding space for all recipes - one ingredient dataset is generated.
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology.
RF+clust for Leave-One-Problem-Out Performance Prediction
Per-instance automated algorithm configuration and selection are gaining significant moments in evolutionary computation in recent years.
OPTION: OPTImization Algorithm Benchmarking ONtology
Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research.
Explainable Model-specific Algorithm Selection for Multi-Label Classification
To address this algorithm selection problem, we investigate in this work the quality of an automated approach that uses characteristics of the datasets - so-called features - and a trained algorithm selector to choose which algorithm to apply for a given task.
Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration
Algorithm selection wizards are effective and versatile tools that automatically select an optimization algorithm given high-level information about the problem and available computational resources, such as number and type of decision variables, maximal number of evaluations, possibility to parallelize evaluations, etc.
SELECTOR: Selecting a Representative Benchmark Suite for Reproducible Statistical Comparison
Fair algorithm evaluation is conditioned on the existence of high-quality benchmark datasets that are non-redundant and are representative of typical optimization scenarios.
Per-run Algorithm Selection with Warm-starting using Trajectory-based Features
In contrast to other recent work on online per-run algorithm selection, we warm-start the second optimizer using information accumulated during the first optimization phase.
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
In addition, we have shown that by using classifiers that take the features relevance on the model accuracy, we are able to predict the status of individual modules in the CMA-ES configurations.
Trajectory-based Algorithm Selection with Warm-starting
We study the quality and accuracy of performance regression and algorithm selection models in the scenario of predicting different algorithm performances after a fixed budget of function evaluations.
Explainable Landscape Analysis in Automated Algorithm Performance Prediction
Predicting the performance of an optimization algorithm on a new problem instance is crucial in order to select the most appropriate algorithm for solving that problem instance.
Explainable Landscape-Aware Optimization Performance Prediction
In this study, we are investigating explainable landscape-aware regression models where the contribution of each landscape feature to the prediction of the optimization algorithm performance is estimated on a global and local level.
FoodChem: A food-chemical relation extraction model
In this paper, we present FoodChem, a new Relation Extraction (RE) model for identifying chemicals present in the composition of food entities, based on textual information provided in biomedical peer-reviewed scientific literature.
Less is more: Selecting the right benchmarking set of data for time series classification
By observing discrepancy between the empirical results of the bootstrap evaluation and recently adapted practices in TSC literature when introducing novel methods we warn on the potentially harmful effects of tuning the methods on certain parts of the landscape (unless this is an explicit and desired goal of the study).
A Complementarity Analysis of the COCO Benchmark Problems and Artificially Generated Problems
When designing a benchmark problem set, it is important to create a set of benchmark problems that are a good generalization of the set of all possible problems.
OPTION: OPTImization Algorithm Benchmarking ONtology
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research.
Personalizing Performance Regression Models to Black-Box Optimization Problems
Accurately predicting the performance of different optimization algorithms for previously unseen problem instances is crucial for high-performing algorithm selection and configuration techniques.
The Impact of Hyper-Parameter Tuning for Landscape-Aware Performance Regression and Algorithm Selection
By comparing a total number of 30 different models, each coupled with 2 complementary regression strategies, we derive guidelines for the tuning of the regression models and provide general recommendations for a more systematic use of classical machine learning models in landscape-aware algorithm selection.
Towards Feature-Based Performance Regression Using Trajectory Data
The evaluation of these points is costly, and the benefit of an ELA-based algorithm selection over a default algorithm must therefore be significant in order to pay off.
On Statistical Analysis of MOEAs with Multiple Performance Indicators
Assessing the empirical performance of Multi-Objective Evolutionary Algorithms (MOEAs) is vital when we extensively test a set of MOEAs and aim to determine a proper ranking thereof.
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscapes
Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning, and TSP.
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics
Representation learning methods that transform encoded data (e. g., diagnosis and drug codes) into continuous vector spaces (i. e., vector embeddings) are critical for the application of deep learning in healthcare.
A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic
Statistical learning methods that use data from multiple clinical centers across the US to detect opioid over-prescribing trends and predict possible opioid misuse are required.
