Joaquin Vanschoren

research

∙ 09/04/2023

Locality-Aware Hyperspectral Classification

Hyperspectral image classification is gaining popularity for high-precis...

0 Fangqin Zhou, et al. ∙

research

∙ 08/28/2023

Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates

Continual learning (CL) refers to the ability of an intelligent system t...

0 Murat Onur Yildirim, et al. ∙

research

∙ 07/10/2023

Advances and Challenges in Meta-Learning: A Technical Review

Meta-learning empowers learning systems with the ability to acquire know...

0 Anna Vettoruzzo, et al. ∙

research

∙ 07/07/2023

MALIBO: Meta-learning for Likelihood-free Bayesian Optimization

Bayesian optimization (BO) is a popular method to optimize costly black-...

0 Jiarong Pan, et al. ∙

research

∙ 03/23/2023

Adaptive Regularization for Class-Incremental Learning

Class-Incremental Learning updates a deep classifier with new categories...

1 Elif Ceren Gok Yildirim, et al. ∙

research

∙ 03/15/2023

Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML

The field of automated machine learning (AutoML) introduces techniques t...

19 Hilde Weerts, et al. ∙

research

∙ 02/16/2023

Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification

We introduce Meta-Album, an image classification meta-dataset designed t...

23 Ihsan Ullah, et al. ∙

research

∙ 01/26/2023

Are Labels Needed for Incremental Instance Learning?

In this paper, we learn to classify visual object instances, incremental...

7 Mert Kilickaya, et al. ∙

research

∙ 11/01/2022

Automated Imbalanced Learning

Automated Machine Learning has grown very successful in automating the t...

0 Prabhant Singh, et al. ∙

research

∙ 11/01/2022

Meta-Learning for Unsupervised Outlier Detection with Optimal Transport

Automated machine learning has been widely researched and adopted in the...

0 Prabhant Singh, et al. ∙

research

∙ 08/18/2022

Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts

In this paper, our goal is to adapt a pre-trained Convolutional Neural N...

9 Tommie Kerssies, et al. ∙

research

∙ 07/25/2022

AMLB: an AutoML Benchmark

Comparing different AutoML frameworks is notoriously challenging and oft...

12 Pieter Gijsbers, et al. ∙

research

∙ 07/20/2022

DataPerf: Benchmarks for Data-Centric AI Development

Machine learning (ML) research has generally focused on models, while th...

17 Mark Mazumder, et al. ∙

research

∙ 06/14/2022

Open-Ended Learning Strategies for Learning Complex Locomotion Skills

Teaching robots to learn diverse locomotion skills under complex three-d...

0 Fangqin Zhou, et al. ∙

research

∙ 05/12/2022

Warm-starting DARTS using meta-learning

Neural architecture search (NAS) has shown great promise in the field of...

0 Matej Grobelnik, et al. ∙

research

∙ 02/01/2022

Advances in MetaDL: AAAI 2021 challenge and workshop

To stimulate advances in metalearning using deep learning techniques (Me...

6 Adrian El Baz, et al. ∙

research

∙ 01/24/2022

Online AutoML: An adaptive AutoML framework for online learning

Automated Machine Learning (AutoML) has been used successfully in settin...

0 Bilge Celik, et al. ∙

research

∙ 01/13/2022

Automated Reinforcement Learning: An Overview

Reinforcement Learning and recently Deep Reinforcement Learning are popu...

21 Reza Refaei Afshar, et al. ∙

research

∙ 11/05/2021

Frugal Machine Learning

Machine learning, already at the core of increasingly many systems and a...

10 Mikhail Evchenko, et al. ∙

research

∙ 11/02/2021

From Strings to Data Science: a Practical Framework for Automated String Handling

Many machine learning libraries require that string features be converte...

0 John W. van Lith, et al. ∙

research

∙ 07/13/2021

Cats, not CAT scans: a study of dataset similarity in transfer learning for 2D medical image classification

Transfer learning is a commonly used strategy for medical image classifi...

0 Irma van den Brandt, et al. ∙

research

∙ 06/10/2021

Meta-Learning for Symbolic Hyperparameter Defaults

Hyperparameter optimization in machine learning (ML) deals with the prob...

0 Pieter Gijsbers, et al. ∙

research

∙ 02/03/2021

Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms

Convolutional Neural Networks (CNNs) have proven to be a powerful state-...

9 Rishabh Goyal, et al. ∙

research

∙ 01/06/2021

Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate models

Bayesian Optimization is a popular tool for tuning algorithms in automat...

35 Jeroen van Hoof, et al. ∙

research

∙ 01/05/2021

Theory-based Habit Modeling for Enhancing Behavior Prediction

Psychological theories of habit posit that when a strong habit is formed...

0 Chao Zhang, et al. ∙

research

∙ 12/03/2020

Aerial Imagery Pixel-level Segmentation

Aerial imagery can be used for important work on a global scale. Neverth...

0 Michael R. Heffels, et al. ∙

research

∙ 07/15/2020

Importance of Tuning Hyperparameters of Machine Learning Algorithms

The performance of many machine learning algorithms depends on their hyp...

0 Hilde J. P. Weerts, et al. ∙

research

∙ 07/09/2020

GAMA: a General Automated Machine learning Assistant

The General Automated Machine learning Assistant (GAMA) is a modular Aut...

0 Pieter Gijsbers, et al. ∙

research

∙ 06/09/2020

Adaptation Strategies for Automated Machine Learning on Evolving Data

Automated Machine Learning (AutoML) systems have been shown to efficient...

0 Bilge Celik, et al. ∙

research

∙ 11/06/2019

OpenML-Python: an extensible Python API for OpenML

OpenML is an online platform for open science collaboration in machine l...

0 Matthias Feurer, et al. ∙

research

∙ 07/01/2019

An Open Source AutoML Benchmark

In recent years, an active field of research has developed around automa...

0 Pieter Gijsbers, et al. ∙

research

∙ 06/04/2019

A meta-learning recommender system for hyperparameter tuning: predicting when tuning improves SVM classifiers

For many machine learning algorithms, predictive performance is critical...

0 Rafael Gomes Mantovani, et al. ∙

research

∙ 03/29/2019

SysML: The New Frontier of Machine Learning Systems

Machine learning (ML) techniques are enjoying rapidly increasing adoptio...

0 Alexander Ratner, et al. ∙

research

∙ 12/05/2018

An empirical study on hyperparameter tuning of decision trees

Machine learning algorithms often contain many hyperparameters whose val...

18 Rafael Gomes Mantovani, et al. ∙

research

∙ 11/08/2018

Transformative Machine Learning

The key to success in machine learning (ML) is the use of effective data...

0 Ivan Olier, et al. ∙

research

∙ 10/08/2018

Meta-Learning: A Survey

Meta-learning, or learning to learn, is the science of systematically ob...

0 Joaquin Vanschoren, et al. ∙

research

∙ 08/30/2018

Towards Reproducible Empirical Research in Meta-Learning

Meta-learning is increasingly used to support the recommendation of mach...

0 Adriano Rivolli, et al. ∙

research

∙ 07/14/2018

ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies

The ML-Schema, proposed by the W3C Machine Learning Schema Community Gro...

0 Gustavo Correa Publio, et al. ∙

research

∙ 01/18/2018

Layered TPOT: Speeding up Tree-based Pipeline Optimization

With the demand for machine learning increasing, so does the demand for ...

0 Pieter Gijsbers, et al. ∙

research

∙ 09/12/2017

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

We investigate the learning of quantitative structure activity relations...

0 Ivan Olier, et al. ∙

research

∙ 08/11/2017

OpenML Benchmarking Suites and the OpenML100

We advocate the use of curated, comprehensive benchmark suites of machin...

0 Bernd Bischl, et al. ∙

research

∙ 01/05/2017

OpenML: An R Package to Connect to the Machine Learning Platform OpenML

OpenML is an online machine learning platform where researchers can easi...

0 Giuseppe Casalicchio, et al. ∙

research

∙ 06/08/2015

ASlib: A Benchmark Library for Algorithm Selection

The task of algorithm selection involves choosing an algorithm from a se...

0 Bernd Bischl, et al. ∙

Joaquin Vanschoren

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