Search Results for author: Joaquin Vanschoren

Found 32 papers, 15 papers with code

Warm-starting DARTS using meta-learning

no code implementations12 May 2022 Matej Grobelnik, Joaquin Vanschoren

Neural architecture search (NAS) has shown great promise in the field of automated machine learning (AutoML).

Meta-Learning Neural Architecture Search

Advances in MetaDL: AAAI 2021 challenge and workshop

no code implementations1 Feb 2022 Adrian El Baz, Isabelle Guyon, Zhengying Liu, Jan van Rijn, Sebastien Treguer, Joaquin Vanschoren

Winning methods featured various classifiers trained on top of the second last layer of popular CNN backbones, fined-tuned on the meta-training data (not necessarily in an episodic manner), then trained on the labeled support and tested on the unlabeled query sets of the meta-test data.

Few-Shot Learning

Online AutoML: An adaptive AutoML framework for online learning

no code implementations24 Jan 2022 Bilge Celik, Prabhant Singh, Joaquin Vanschoren

For this purpose, we design an adaptive Online Automated Machine Learning (OAML) system, searching the complete pipeline configuration space of online learners, including preprocessing algorithms and ensembling techniques.

AutoML online learning

Automated Reinforcement Learning: An Overview

no code implementations13 Jan 2022 Reza Refaei Afshar, Yingqian Zhang, Joaquin Vanschoren, Uzay Kaymak

Automated RL provides a framework in which different components of RL including MDP modeling, algorithm selection and hyper-parameter optimization are modeled and defined automatically.

Decision Making reinforcement-learning

Frugal Machine Learning

no code implementations5 Nov 2021 Mikhail Evchenko, Joaquin Vanschoren, Holger H. Hoos, Marc Schoenauer, Michèle Sebag

Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things.

Activity Recognition

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

no code implementations2 Nov 2021 John W. van Lith, Joaquin Vanschoren

Many machine learning libraries require that string features be converted to a numerical representation for the models to work as intended.

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

1 code implementation13 Jul 2021 Irma van den Brandt, Floris Fok, Bas Mulders, Joaquin Vanschoren, Veronika Cheplygina

There is currently no consensus on how to choose appropriate source data, and in the literature we can find both evidence of favoring large natural image datasets such as ImageNet, and evidence of favoring more specialized medical datasets.

Image Classification Transfer Learning

Meta-Learning for Symbolic Hyperparameter Defaults

1 code implementation10 Jun 2021 Pieter Gijsbers, Florian Pfisterer, Jan N. van Rijn, Bernd Bischl, Joaquin Vanschoren

Hyperparameter optimization in machine learning (ML) deals with the problem of empirically learning an optimal algorithm configuration from data, usually formulated as a black-box optimization problem.

Hyperparameter Optimization Meta-Learning

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

1 code implementation3 Feb 2021 Rishabh Goyal, Joaquin Vanschoren, Victor van Acht, Stephan Nijssen

One drawback however is the high computational complexity and high memory consumption of CNNs which makes them unfeasible for execution on embedded platforms which are constrained on physical resources needed to support CNNs.

Image Classification Quantization

Theory-based Habit Modeling for Enhancing Behavior Prediction

no code implementations5 Jan 2021 Chao Zhang, Joaquin Vanschoren, Arlette van Wissen, Daniel Lakens, Boris de Ruyter, Wijnand A. IJsselsteijn

Psychological theories of habit posit that when a strong habit is formed through behavioral repetition, it can trigger behavior automatically in the same environment.

Aerial Imagery Pixel-level Segmentation

1 code implementation3 Dec 2020 Michael R. Heffels, Joaquin Vanschoren

Hence, we also propose a new benchmark on the DroneDeploy test set using the best performing DeepLabv3+ Xception65 architecture, with a mIOU score of 52. 5%.

Data Augmentation Semantic Segmentation

Importance of Tuning Hyperparameters of Machine Learning Algorithms

no code implementations15 Jul 2020 Hilde J. P. Weerts, Andreas C. Mueller, Joaquin Vanschoren

The performance of many machine learning algorithms depends on their hyperparameter settings.

GAMA: a General Automated Machine learning Assistant

3 code implementations9 Jul 2020 Pieter Gijsbers, Joaquin Vanschoren

The General Automated Machine learning Assistant (GAMA) is a modular AutoML system developed to empower users to track and control how AutoML algorithms search for optimal machine learning pipelines, and facilitate AutoML research itself.

AutoML

Adaptation Strategies for Automated Machine Learning on Evolving Data

1 code implementation9 Jun 2020 Bilge Celik, Joaquin Vanschoren

To that end, we propose 6 concept drift adaptation strategies and evaluate their effectiveness on different AutoML approaches.

AutoML

OpenML-Python: an extensible Python API for OpenML

1 code implementation6 Nov 2019 Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter

It also provides functionality to conduct machine learning experiments, upload the results to OpenML, and reproduce results which are stored on OpenML.

An Open Source AutoML Benchmark

no code implementations1 Jul 2019 Pieter Gijsbers, Erin LeDell, Janek Thomas, Sébastien Poirier, Bernd Bischl, Joaquin Vanschoren

In recent years, an active field of research has developed around automated machine learning (AutoML).

AutoML

An empirical study on hyperparameter tuning of decision trees

no code implementations5 Dec 2018 Rafael Gomes Mantovani, Tomáš Horváth, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, André Carlos Ponce de Leon Ferreira de Carvalho

Experimental results indicate that hyperparameter tuning provides statistically significant improvements for C4. 5 and CTree in only one-third of the datasets, and in most of the datasets for CART.

General Classification

Transformative Machine Learning

no code implementations8 Nov 2018 Ivan Olier, Oghenejokpeme I. Orhobor, Joaquin Vanschoren, Ross D. King

In all three problems, transformative machine learning significantly outperforms the best intrinsic representation.

Explainable Models Meta-Learning +2

Meta-Learning: A Survey

no code implementations8 Oct 2018 Joaquin Vanschoren

In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.

Meta-Learning

Characterizing classification datasets: a study of meta-features for meta-learning

1 code implementation30 Aug 2018 Adriano Rivolli, Luís P. F. Garcia, Carlos Soares, Joaquin Vanschoren, André C. P. L. F. de Carvalho

These characterizations, also called meta-features, describe properties of the data which are predictive for the performance of machine learning algorithms trained on them.

General Classification Meta-Learning

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

no code implementations14 Jul 2018 Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren, Hamid Zafar

The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments.

Layered TPOT: Speeding up Tree-based Pipeline Optimization

1 code implementation18 Jan 2018 Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson

With the demand for machine learning increasing, so does the demand for tools which make it easier to use.

Automated Feature Engineering Hyperparameter Optimization

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

no code implementations12 Sep 2017 Ivan Olier, Noureddin Sadawi, G. Richard Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa Soldatova, Ross D. King

We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 6 molecular representations, applied to more than 2, 700 QSAR problems.

Meta-Learning

OpenML Benchmarking Suites

4 code implementations11 Aug 2017 Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang, Rafael G. Mantovani, Jan N. van Rijn, Joaquin Vanschoren

Machine learning research depends on objectively interpretable, comparable, and reproducible algorithm benchmarks.

General Classification

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

1 code implementation5 Jan 2017 Giuseppe Casalicchio, Jakob Bossek, Michel Lang, Dominik Kirchhoff, Pascal Kerschke, Benjamin Hofner, Heidi Seibold, Joaquin Vanschoren, Bernd Bischl

We show how the OpenML package allows R users to easily search, download and upload data sets and machine learning tasks.

ASlib: A Benchmark Library for Algorithm Selection

2 code implementations8 Jun 2015 Bernd Bischl, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Frechette, Holger Hoos, Frank Hutter, Kevin Leyton-Brown, Kevin Tierney, Joaquin Vanschoren

To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature.

OpenML: networked science in machine learning

1 code implementation29 Jul 2014 Joaquin Vanschoren, Jan N. van Rijn, Bernd Bischl, Luis Torgo

Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals.

Open science in machine learning

no code implementations24 Feb 2014 Joaquin Vanschoren, Mikio L. Braun, Cheng Soon Ong

We present OpenML and mldata, open science platforms that provides easy access to machine learning data, software and results to encourage further study and application.

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