1.1k citations · 2k across the 52 of their papers we have counts for
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What Can AutoML Do For Continual Learning?
Mert Kilickaya, Joaquin Vanschoren
This position paper outlines the potential of AutoML for incremental (continual) learning to encourage more research in this direction. Incremental learning involves incorporating…
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Luis Oala, Manil Maskey, Lilith Bat-Leah +35
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…
Locality-Aware Hyperspectral Classification
Fangqin Zhou, Mert Kilickaya, Joaquin Vanschoren
Hyperspectral image classification is gaining popularity for high-precision vision tasks in remote sensing, thanks to their ability to capture visual information available in a wid…
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates
Murat Onur Yildirim, Elif Ceren Gok Yildirim, Ghada Sokar +2
Continual learning (CL) refers to the ability of an intelligent system to sequentially acquire and retain knowledge from a stream of data with as little computational overhead as p…
Advances and Challenges in Meta-Learning: A Technical Review
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren +2
Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides…
MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
Jiarong Pan, Stefan Falkner, Felix Berkenkamp +1
Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged a…
AdaCL:Adaptive Continual Learning
Elif Ceren Gok Yildirim, Murat Onur Yildirim, Mert Kilickaya +1
Class-Incremental Learning aims to update a deep classifier to learn new categories while maintaining or improving its accuracy on previously observed classes. Common methods to pr…
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
Hilde Weerts, Florian Pfisterer, Matthias Feurer +7
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducin…
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification
Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera +7
We introduce Meta-Album, an image classification meta-dataset designed to facilitate few-shot learning, transfer learning, meta-learning, among other tasks. It includes 40 open dat…
Are Labels Needed for Incremental Instance Learning?
Mert Kilickaya, Joaquin Vanschoren
In this paper, we learn to classify visual object instances, incrementally and via self-supervision (self-incremental). Our learner observes a single instance at a time, which is t…