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05/11/2023
Predictive change point detection for heterogeneous data
A change point detection (CPD) framework assisted by a predictive machin...
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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...
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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...
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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...
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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...
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12/13/2021
Tree-Based Dynamic Classifier Chains
Classifier chains are an effective technique for modeling label dependen...
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10/18/2021
Correlation-based Discovery of Disease Patterns for Syndromic Surveillance
Early outbreak detection is a key aspect in the containment of infectiou...
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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...
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06/22/2021
Gradient-based Label Binning in Multi-label Classification
In multi-label classification, where a single example may be associated ...
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06/18/2021
An Empirical Investigation into Deep and Shallow Rule Learning
Inductive rule learning is arguably among the most traditional paradigms...
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06/18/2021
An Investigation into Mini-Batch Rule Learning
We investigate whether it is possible to learn rule sets efficiently in ...
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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...
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05/21/2021
Elliptical Ordinal Embedding
Ordinal embedding aims at finding a low dimensional representation of ob...
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01/28/2021
Revisiting Non-Specific Syndromic Surveillance
Infectious disease surveillance is of great importance for the preventio...
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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...
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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...
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07/16/2020
Conformal Rule-Based Multi-label Classification
We advocate the use of conformal prediction (CP) to enhance rule-based m...
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06/23/2020
Learning Gradient Boosted Multi-label Classification Rules
In multi-label classification, where the evaluation of predictions is le...
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06/21/2020
On Aggregation in Ensembles of Multilabel Classifiers
While a variety of ensemble methods for multilabel classification have b...
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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...
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11/08/2019
Advances in Machine Learning for the Behavioral Sciences
The areas of machine learning and knowledge discovery in databases have ...
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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...
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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...
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07/17/2019
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods
Epidemiologists use a variety of statistical algorithms for the early de...
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05/31/2019
Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation
In this paper, we present a simple and cheap ordinal bucketing algorithm...
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05/06/2019
Deep Ordinal Reinforcement Learning
Reinforcement learning usually makes use of numerical rewards, which hav...
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01/14/2019
Ordinal Monte Carlo Tree Search
In many problem settings, most notably in game playing, an agent receive...
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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...
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11/30/2018
Learning Interpretable Rules for Multi-label Classification
Multi-label classification (MLC) is a supervised learning problem in whi...
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11/27/2018
Beta Distribution Drift Detection for Adaptive Classifiers
With today's abundant streams of data, the only constant we can rely on ...
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07/17/2018
Preference-Based Monte Carlo Tree Search
Monte Carlo tree search (MCTS) is a popular choice for solving sequentia...
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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 ...
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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...
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12/22/2014