221 citations · 584 across the 36 of their papers we have counts for
44 papers
Optimizing Sparse Boolean Rule Networks using Mixed Integer Linear Programming
Thomas Pammer, Johannes Fürnkranz, Markus Sinnl
We study the problem of learning accurate and cognitively simple binary classifiers that are directly interpretable as Boolean rules. We propose mixed integer linear programming (M…
Data Quality Rule Generation with LLMs
Anna-Christina Glock, Thomas Hütter, Johannes Fürnkranz +3
The validation of data, such as customer and employee data, is an important task in many organizations. Errors in data can have severe consequences. For example, a wrong drug unit…
RePAIR: Predictive Self-Supervised Representation Learning in Chess
Christoph Koller, Johannes Fürnkranz, Timo Bertram
In this paper, we introduce Representation Prediction via Autoencoding using Iterative Refinement (RePAIR) - a novel self-supervised representation learning architecture that synth…
Contrastive Learning of Preferences with a Contextual InfoNCE Loss
Timo Bertram, Johannes Fürnkranz, Martin Müller
A common problem in contextual preference ranking is that a single preferred action is compared against several choices, thereby blowing up the complexity and skewing the preferenc…
Learning With Generalised Card Representations for "Magic: The Gathering"
Timo Bertram, Johannes Fürnkranz, Martin Müller
A defining feature of collectable card games is the deck building process prior to actual gameplay, in which players form their decks according to some restrictions. Learning to bu…
Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets
Timo Bertram, Johannes Fürnkranz, Martin Müller
In imperfect information games, the evaluation of a game state not only depends on the observable world but also relies on hidden parts of the environment. As accessing the obstruc…
Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess
Timo Bertram, Johannes Fürnkranz, Martin Müller
In imperfect information games, the game state is generally not fully observable to players. Therefore, good gameplay requires policies that deal with the different information tha…
Predictive change point detection for heterogeneous data
Anna-Christina Glock, Florian Sobieczky, Johannes Fürnkranz +2
A change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced and characterised in relation to other state-of-…
Efficient learning of large sets of locally optimal classification rules
Van Quoc Phuong Huynh, Johannes Fürnkranz, Florian Beck
Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in thi…
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess
Timo Bertram, Johannes Fürnkranz, Martin Müller
In this work, we adapt a training approach inspired by the original AlphaGo system to play the imperfect information game of Reconnaissance Blind Chess. Using only the observations…
Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability
Timo Bertram, Johannes Fürnkranz, Martin Müller
In this paper, we study learning in probabilistic domains where the learner may receive incorrect labels but can improve the reliability of labels by repeatedly sampling them. In s…
Unsupervised Alignment of Distributional Word Embeddings
Aissatou Diallo, Johannes Fürnkranz
Cross-domain alignment play a key roles in tasks ranging from machine translation to transfer learning. Recently, purely unsupervised methods operating on monolingual embeddings ha…
GausSetExpander: A Simple Approach for Entity Set Expansion
Aïssatou Diallo, Johannes Fürnkranz
Entity Set Expansion is an important NLP task that aims at expanding a small set of entities into a larger one with items from a large pool of candidates. In this paper, we propose…
Tree-Based Dynamic Classifier Chains
Eneldo Loza Mencía, Moritz Kulessa, Simon Bohlender +1
Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While…
Correlation-based Discovery of Disease Patterns for Syndromic Surveillance
Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía +1
Early outbreak detection is a key aspect in the containment of infectious diseases, as it enables the identification and isolation of infected individuals before the disease can sp…
A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems
Timo Bertram, Johannes Fürnkranz, Martin Müller
In this paper, we study the problem of evaluating the addition of elements to a set. This problem is difficult, because it can, in the general case, not be reduced to unconditional…
Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking
Timo Bertram, Johannes Fürnkranz, Martin Müller
Drafting, i.e., the selection of a subset of items from a larger candidate set, is a key element of many games and related problems. It encompasses team formation in sports or e-sp…
Gradient-based Label Binning in Multi-label Classification
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz +1
In multi-label classification, where a single example may be associated with several class labels at the same time, the ability to model dependencies between labels is considered c…
An Empirical Investigation into Deep and Shallow Rule Learning
Florian Beck, Johannes Fürnkranz
Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theo…
An Investigation into Mini-Batch Rule Learning
Florian Beck, Johannes Fürnkranz
We investigate whether it is possible to learn rule sets efficiently in a network structure with a single hidden layer using iterative refinements over mini-batches of examples. A…
Elliptical Ordinal Embedding
Aïssatou Diallo, Johannes Fürnkranz
Ordinal embedding aims at finding a low dimensional representation of objects from a set of constraints of the form "item is closer to item than item ". Typically, each…
Revisiting Non-Specific Syndromic Surveillance
Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz
Infectious disease surveillance is of great importance for the prevention of major outbreaks. Syndromic surveillance aims at developing algorithms which can detect outbreaks as ear…
Ordinal Monte Carlo Tree Search
Tobias Joppen, Johannes Fürnkranz
In many problem settings, most notably in game playing, an agent receives a possibly delayed reward for its actions. Often, those rewards are handcrafted and not naturally given. E…
Learning Structured Declarative Rule Sets -- A Challenge for Deep Discrete Learning
Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía +1
Arguably the key reason for the success of deep neural networks is their ability to autonomously form non-linear combinations of the input features, which can be used in subsequent…