Eneldo Loza Mencía

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Technische Universität Darmstadt

Revisiting Non-Specific Syndromic Surveillance

Infectious disease surveillance is of great importance for the preventio...
01/28/2021 ∙ by Moritz Kulessa, et al. ∙ 0 ∙ share

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Learning Structured Declarative Rule Sets – A Challenge for Deep Discrete Learning

Arguably the key reason for the success of deep neural networks is their...
12/08/2020 ∙ by Johannes Fürnkranz, et al. ∙ 0 ∙ share

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A Flexible Class of Dependence-aware Multi-Label Loss Functions

Multi-label classification is the task of assigning a subset of labels t...
11/02/2020 ∙ by Eyke Hüllermeier, et al. ∙ 0 ∙ share

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Conformal Rule-Based Multi-label Classification

We advocate the use of conformal prediction (CP) to enhance rule-based m...
07/16/2020 ∙ by Eyke Hüllermeier, et al. ∙ 0 ∙ share

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Learning Gradient Boosted Multi-label Classification Rules

In multi-label classification, where the evaluation of predictions is le...
06/23/2020 ∙ by Michael Rapp, et al. ∙ 0 ∙ share

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On Aggregation in Ensembles of Multilabel Classifiers

While a variety of ensemble methods for multilabel classification have b...
06/21/2020 ∙ by Vu-Linh Nguyen, et al. ∙ 0 ∙ share

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Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy

We analyze the trade-off between model complexity and accuracy for rando...
11/11/2019 ∙ by Michael Rapp, et al. ∙ 0 ∙ share

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Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning

Being able to model correlations between labels is considered crucial in...
08/19/2019 ∙ by Yannik Klein, et al. ∙ 0 ∙ share

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On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

Recently, several authors have advocated the use of rule learning algori...
08/08/2019 ∙ by Michael Rapp, et al. ∙ 4 ∙ share

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Improving Outbreak Detection with Stacking of Statistical Surveillance Methods

Epidemiologists use a variety of statistical algorithms for the early de...
07/17/2019 ∙ by Moritz Kulessa, et al. ∙ 0 ∙ share

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Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

Exploiting dependencies between labels is considered to be crucial for m...
12/14/2018 ∙ by Michael Rapp, et al. ∙ 0 ∙ share

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Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in whi...
11/30/2018 ∙ by Eneldo Loza Mencía, et al. ∙ 0 ∙ share

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Analysis and Optimization of Deep CounterfactualValue Networks

Recently a strong poker-playing algorithm called DeepStack was published...
07/02/2018 ∙ by Eneldo Loza Mencía, et al. ∙ 0 ∙ share

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Eneldo Loza Mencía

Featured Co-authors

Eyke Hüllermeier

Johannes Fürnkranz

Michael Rapp

Marcel Wever

Moritz Kulessa

Vu-Linh Nguyen

Patryk Hopner

Yannik Klein

Revisiting Non-Specific Syndromic Surveillance

Learning Structured Declarative Rule Sets – A Challenge for Deep Discrete Learning

A Flexible Class of Dependence-aware Multi-Label Loss Functions

Conformal Rule-Based Multi-label Classification

Learning Gradient Boosted Multi-label Classification Rules

On Aggregation in Ensembles of Multilabel Classifiers

Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy

Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning

On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

Improving Outbreak Detection with Stacking of Statistical Surveillance Methods

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

Learning Interpretable Rules for Multi-label Classification

Analysis and Optimization of Deep CounterfactualValue Networks