b'Joeran Beel'

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Featured Co-authors

Bernd Bischl
93 publications
Bela Gipp
71 publications
Akiko Aizawa
44 publications
Barry Smyth
18 publications
Mark Collier
15 publications
Petr Knoth
14 publications
Andrew Collins
13 publications
Dominika Tkaczyk
11 publications
Lennart Schneider
10 publications
Holger Hoos
9 publications
Corinna Breitinger
9 publications

research

∙ 07/17/2023

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML

Automated machine learning (AutoML) systems commonly ensemble models pos...

0 Lennart Purucker, et al. ∙

research

∙ 07/01/2023

CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure

Many state-of-the-art automated machine learning (AutoML) systems use gr...

0 Lennart Purucker, et al. ∙

research

∙ 07/01/2023

Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML

Automated Machine Learning (AutoML) frameworks regularly use ensembles. ...

0 Lennart Purucker, et al. ∙

research

∙ 12/30/2020

Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering

Recommendation algorithms perform differently if the users, recommendati...

0 Andrew Collins, et al. ∙

research

∙ 09/10/2020

Finite Group Equivariant Neural Networks for Games

Games such as go, chess and checkers have multiple equivalent game state...

46 Oisín Carroll, et al. ∙

research

∙ 09/07/2020

Towards an Interoperable Data Protocol Aimed at Linking the Fashion Industry with AI Companies

The fashion industry is looking forward to use artificial intelligence t...

14 Mohammed Al-Rawi, et al. ∙

research

∙ 08/19/2020

Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization

We introduce Auto-Surprise, an Automated Recommender System library. Aut...

0 Rohan Anand, et al. ∙

research

∙ 06/22/2020

Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]

Automated per-instance algorithm selection often outperforms single lear...

19 Joeran Beel, et al. ∙

research

∙ 04/22/2020

Synthetic vs. Real Reference Strings for Citation Parsing, and the Importance of Re-training and Out-Of-Sample Data for Meaningful Evaluations: Experiments with GROBID, GIANT a

Citation parsing, particularly with deep neural networks, suffers from a...

2 Mark Grennan, et al. ∙

research

∙ 12/18/2019

Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems

The effectiveness of recommender system algorithms varies in different r...

0 Andrew Collins, et al. ∙

research

∙ 12/16/2019

Predicting the Outcome of Judicial Decisions made by the European Court of Human Rights

In this study, machine learning models were constructed to predict wheth...

0 Conor O'Sullivan, et al. ∙

research

∙ 12/16/2019

Multi-stream Data Analytics for Enhanced Performance Prediction in Fantasy Football

Fantasy Premier League (FPL) performance predictors tend to base their a...

0 Nicholas Bonello, et al. ∙

research

∙ 12/15/2019

NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts

Information about the contributions of individual authors to scientific ...

0 Dominika Tkaczyk, et al. ∙

research

∙ 09/18/2019

Memory-Augmented Neural Networks for Machine Translation

Memory-augmented neural networks (MANNs) have been shown to outperform o...

18 Mark Collier, et al. ∙

research

∙ 05/27/2019

Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems

Many recommendation algorithms are available to digital library recommen...

0 Andrew Collins, et al. ∙

research

∙ 11/26/2018

ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers

Bibliographic reference parsers extract machine-readable metadata such a...

0 Dominika Tkaczyk, et al. ∙

research

∙ 11/26/2018

The Architecture of Mr. DLib's Scientific Recommender-System API

Recommender systems in academia are not widely available. This may be in...

0 Joeran Beel, et al. ∙

research

∙ 09/27/2018

An Empirical Comparison of Syllabuses for Curriculum Learning

Syllabuses for curriculum learning have been developed on an ad-hoc, per...

0 Mark Collier, et al. ∙

research

∙ 08/27/2018

ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing

Bibliographic reference parsers extract metadata (e.g. author names, tit...

0 Dominika Tkaczyk, et al. ∙

research

∙ 07/23/2018

Implementing Neural Turing Machines

Neural Turing Machines (NTMs) are an instance of Memory Augmented Neural...

0 Mark Collier, et al. ∙

research

∙ 07/19/2018

Mr. DLib's Living Lab for Scholarly Recommendations

We introduce the first living lab for scholarly recommender systems. Thi...

0 Joeran Beel, et al. ∙

research

∙ 07/18/2018

RARD II: The 2nd Related-Article Recommendation Dataset

The main contribution of this paper is to introduce and describe a new r...

0 Joeran Beel, et al. ∙

research

∙ 05/30/2018

One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level

In this proposal we present the idea of a "macro recommender system", an...

0 Andrew Collins, et al. ∙

research

∙ 05/12/2018

Citation Data-set for Machine Learning Citation Styles and Entity Extraction from Citation Strings

Citation parsing is fundamental for search engines within academia and t...

0 Niall Martin Ryan, et al. ∙

research

∙ 02/19/2018

A Study of Position Bias in Digital Library Recommender Systems

"Position bias" describes the tendency of users to interact with items o...

0 Andrew Collins, et al. ∙

research

∙ 02/04/2018

A Method for Discovering and Extracting Author Contributions Information from Scientific Biomedical Publications

Creating scientific publications is a complex process, typically compose...

0 Dominika Tkaczyk, et al. ∙

research

∙ 02/04/2018

Machine Learning vs. Rules and Out-of-the-Box vs. Retrained: An Evaluation of Open-Source Bibliographic Reference and Citation Parsers

Bibliographic reference parsing refers to extracting machine-readable me...

0 Dominika Tkaczyk, et al. ∙

research

∙ 02/04/2018

Evaluation and Comparison of Open Source Bibliographic Reference Parsers: A Business Use Case

Bibliographic reference parsing refers to extracting machine-readable me...

0 Dominika Tkaczyk, et al. ∙

research

∙ 08/28/2017

It's Time to Consider "Time" when Evaluating Recommender-System Algorithms [Proposal]

In this position paper, we question the current practice of calculating ...

0 Joeran Beel, et al. ∙

research

∙ 03/27/2017

Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia

Only few digital libraries and reference managers offer recommender syst...

0 Joeran Beel, et al. ∙

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