1.1k citations · 2k across the 52 of their papers we have counts for
11 papers · 1 filter
Automated Imbalanced Learning
Prabhant Singh, Joaquin Vanschoren
Automated Machine Learning has grown very successful in automating the time-consuming, iterative tasks of machine learning model development. However, current methods struggle when…
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport
Prabhant Singh, Joaquin Vanschoren
Automated machine learning has been widely researched and adopted in the field of supervised classification and regression, but progress in unsupervised settings has been limited.…
Evaluating Continual Test-Time Adaptation for Contextual and Semantic Domain Shifts
Tommie Kerssies, Mert Kılıçkaya, Joaquin Vanschoren
In this paper, our goal is to adapt a pre-trained convolutional neural network to domain shifts at test time. We do so continually with the incoming stream of test batches, without…
AMLB: an AutoML Benchmark
Pieter Gijsbers, Marcos L. P. Bueno, Stefan Coors +5
Comparing different AutoML frameworks is notoriously challenging and often done incorrectly. We introduce an open and extensible benchmark that follows best practices and avoids co…
DataPerf: Benchmarks for Data-Centric AI Development
Mark Mazumder, Colby Banbury, Xiaozhe Yao +42
Machine learning research has long focused on models rather than datasets, and prominent datasets are used for common ML tasks without regard to the breadth, difficulty, and faithf…
Open-Ended Learning Strategies for Learning Complex Locomotion Skills
Fangqin Zhou, Joaquin Vanschoren
Teaching robots to learn diverse locomotion skills under complex three-dimensional environmental settings via Reinforcement Learning (RL) is still challenging. It has been shown th…
EmProx: Neural Network Performance Estimation For Neural Architecture Search
G. G. H. Franken, P. Singh, J. Vanschoren
Common Neural Architecture Search methods generate large amounts of candidate architectures that need training in order to assess their performance and find an optimal architecture…
Warm-starting DARTS using meta-learning
Matej Grobelnik, Joaquin Vanschoren
Neural architecture search (NAS) has shown great promise in the field of automated machine learning (AutoML). NAS has outperformed hand-designed networks and made a significant ste…
Advances in MetaDL: AAAI 2021 challenge and workshop
Adrian El Baz, Isabelle Guyon, Zhengying Liu +3
To stimulate advances in metalearning using deep learning techniques (MetaDL), we organized in 2021 a challenge and an associated workshop. This paper presents the design of the ch…
Online AutoML: An adaptive AutoML framework for online learning
Bilge Celik, Prabhant Singh, Joaquin Vanschoren
Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribu…
Automated Reinforcement Learning: An Overview
Reza Refaei Afshar, Joaquin Vanschoren, Uzay Kaymak +4
Reinforcement Learning and, recently, Deep Reinforcement Learning are popular methods for solving sequential decision-making problems modeled as Markov Decision Processes. RL model…