Johannes Fürnkranz

research

∙ 05/11/2023

Predictive change point detection for heterogeneous data

A change point detection (CPD) framework assisted by a predictive machin...

0 Anna-Christina Glock, et al. ∙

research

∙ 01/24/2023

Efficient learning of large sets of locally optimal classification rules

Conventional rule learning algorithms aim at finding a set of simple rul...

0 Van Quoc Phuong Huynh, et al. ∙

research

∙ 08/03/2022

Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess

In this work, we adapt a training approach inspired by the original Alph...

0 Timo Bertram, et al. ∙

research

∙ 04/20/2022

Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability

In this paper, we study learning in probabilistic domains where the lear...

0 Timo Bertram, et al. ∙

research

∙ 02/28/2022

GausSetExpander: A Simple Approach for Entity Set Expansion

Entity Set Expansion is an important NLP task that aims at expanding a s...

0 Aissatou Diallo, et al. ∙

research

∙ 12/13/2021

Tree-Based Dynamic Classifier Chains

Classifier chains are an effective technique for modeling label dependen...

12 Eneldo Loza Mencía, et al. ∙

research

∙ 10/18/2021

Correlation-based Discovery of Disease Patterns for Syndromic Surveillance

Early outbreak detection is a key aspect in the containment of infectiou...

0 Michael Rapp, et al. ∙

research

∙ 07/09/2021

A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems

In this paper, we study the problem of evaluating the addition of elemen...

0 Timo Bertram, et al. ∙

research

∙ 06/22/2021

Gradient-based Label Binning in Multi-label Classification

In multi-label classification, where a single example may be associated ...

0 Michael Rapp, et al. ∙

research

∙ 06/18/2021

An Empirical Investigation into Deep and Shallow Rule Learning

Inductive rule learning is arguably among the most traditional paradigms...

0 Florian Beck, et al. ∙

research

∙ 06/18/2021

An Investigation into Mini-Batch Rule Learning

We investigate whether it is possible to learn rule sets efficiently in ...

0 Florian Beck, et al. ∙

research

∙ 05/25/2021

Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking

Drafting, i.e., the selection of a subset of items from a larger candida...

0 Timo Bertram, et al. ∙

research

∙ 05/21/2021

Elliptical Ordinal Embedding

Ordinal embedding aims at finding a low dimensional representation of ob...

42 Aissatou Diallo, et al. ∙

research

∙ 01/28/2021

Revisiting Non-Specific Syndromic Surveillance

Infectious disease surveillance is of great importance for the preventio...

0 Moritz Kulessa, et al. ∙

research

∙ 12/08/2020

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

Arguably the key reason for the success of deep neural networks is their...

0 Johannes Fürnkranz, et al. ∙

research

∙ 11/02/2020

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

Multi-label classification is the task of assigning a subset of labels t...

0 Eyke Hüllermeier, et al. ∙

research

∙ 07/16/2020

Conformal Rule-Based Multi-label Classification

We advocate the use of conformal prediction (CP) to enhance rule-based m...

0 Eyke Hüllermeier, et al. ∙

research

∙ 06/23/2020

Learning Gradient Boosted Multi-label Classification Rules

In multi-label classification, where the evaluation of predictions is le...

0 Michael Rapp, et al. ∙

research

∙ 06/21/2020

On Aggregation in Ensembles of Multilabel Classifiers

While a variety of ensemble methods for multilabel classification have b...

0 Vu-Linh Nguyen, et al. ∙

research

∙ 11/11/2019

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

We analyze the trade-off between model complexity and accuracy for rando...

0 Michael Rapp, et al. ∙

research

∙ 11/08/2019

Advances in Machine Learning for the Behavioral Sciences

The areas of machine learning and knowledge discovery in databases have ...

0 Tomas Kliegr, et al. ∙

research

∙ 08/19/2019

Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data

Deep neural networks have been successfully applied in learning the boar...

7 Johannes Czech, et al. ∙

research

∙ 08/08/2019

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...

4 Michael Rapp, et al. ∙

research

∙ 07/17/2019

Improving Outbreak Detection with Stacking of Statistical Surveillance Methods

Epidemiologists use a variety of statistical algorithms for the early de...

0 Moritz Kulessa, et al. ∙

research

∙ 05/31/2019

Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation

In this paper, we present a simple and cheap ordinal bucketing algorithm...

0 Tobias Joppen, et al. ∙

research

∙ 05/06/2019

Deep Ordinal Reinforcement Learning

Reinforcement learning usually makes use of numerical rewards, which hav...

0 Alexander Zap, et al. ∙

research

∙ 01/14/2019

Ordinal Monte Carlo Tree Search

In many problem settings, most notably in game playing, an agent receive...

0 Tobias Joppen, et al. ∙

research

∙ 12/14/2018

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

Exploiting dependencies between labels is considered to be crucial for m...

0 Michael Rapp, et al. ∙

research

∙ 11/30/2018

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in whi...

0 Eneldo Loza Mencía, et al. ∙

research

∙ 11/27/2018

Beta Distribution Drift Detection for Adaptive Classifiers

With today's abundant streams of data, the only constant we can rely on ...

0 Lukas Fleckenstein, et al. ∙

research

∙ 07/17/2018

Preference-Based Monte Carlo Tree Search

Monte Carlo tree search (MCTS) is a popular choice for solving sequentia...

0 Tobias Joppen, et al. ∙

research

∙ 04/09/2018

A review of possible effects of cognitive biases on interpretation of rule-based machine learning models

This paper investigates to what extent do cognitive biases affect human ...

0 Tomas Kliegr, et al. ∙

research

∙ 03/04/2018

On Cognitive Preferences and the Interpretability of Rule-based Models

It is conventional wisdom in machine learning and data mining that logic...

0 Johannes Fürnkranz, et al. ∙

research

∙ 12/22/2014

On Learning Vector Representations in Hierarchical Label Spaces

An important problem in multi-label classification is to capture label p...

0 Jinseok Nam, et al. ∙

Johannes Fürnkranz

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