Eyke Hüllermeier

research

∙ 09/05/2023

Probabilistic Self-supervised Learning via Scoring Rules Minimization

In this paper, we propose a novel probabilistic self-supervised learning...

0 Amirhossein Vahidi, et al. ∙

research

∙ 08/28/2023

Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning

Ensembling a neural network is a widely recognized approach to enhance m...

0 Amirhossein Vahidi, et al. ∙

research

∙ 08/21/2023

Weighting by Tying: A New Approach to Weighted Rank Correlation

Measures of rank correlation are commonly used in statistics to capture ...

0 Sascha Henzgen, et al. ∙

research

∙ 07/13/2023

A Novel Bayes' Theorem for Upper Probabilities

In their seminal 1990 paper, Wasserman and Kadane establish an upper bou...

0 Michele Caprio, et al. ∙

research

∙ 06/16/2023

Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?

Adequate uncertainty representation and quantification have become imper...

0 Yusuf Sale, et al. ∙

research

∙ 06/13/2023

iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios

Post-hoc explanation techniques such as the well-established partial dep...

0 Maximilian Muschalik, et al. ∙

research

∙ 06/01/2023

Conformal Prediction with Partially Labeled Data

While the predictions produced by conformal prediction are set-valued, t...

0 alireza javanmardi, et al. ∙

research

∙ 06/01/2023

UNGOML: Automated Classification of unsafe Usages in Go

The Go programming language offers strong protection from memory corrupt...

0 Anna-Katharina Wickert, et al. ∙

research

∙ 05/25/2023

Koopman Kernel Regression

Many machine learning approaches for decision making, such as reinforcem...

0 Petar Bevanda, et al. ∙

research

∙ 05/23/2023

Mitigating Label Noise through Data Ambiguation

Label noise poses an important challenge in machine learning, especially...

0 Julian Lienen, et al. ∙

research

∙ 04/30/2023

Detecting Novelties with Empty Classes

For open world applications, deep neural networks (DNNs) need to be awar...

0 Svenja Uhlemeyer, et al. ∙

research

∙ 04/02/2023

Optimizing Data Shapley Interaction Calculation from O(2^n) to O(t n^2) for KNN models

With the rapid growth of data availability and usage, quantifying the ad...

0 Mohamed Karim Belaid, et al. ∙

research

∙ 03/02/2023

iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams

Explainable Artificial Intelligence (XAI) focuses mainly on batch learni...

0 Maximilian Muschalik, et al. ∙

research

∙ 03/02/2023

SHAP-IQ: Unified Approximation of any-order Shapley Interactions

Predominately in explainable artificial intelligence (XAI) research, the...

0 Fabian Fumagalli, et al. ∙

research

∙ 02/01/2023

Approximating the Shapley Value without Marginal Contributions

The Shapley value is arguably the most popular approach for assigning a ...

0 Patrick Kolpaczki, et al. ∙

research

∙ 02/01/2023

Iterative Deepening Hyperband

Hyperparameter optimization (HPO) is concerned with the automated search...

0 Jasmin Brandt, et al. ∙

research

∙ 01/30/2023

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification

It is well known that accurate probabilistic predictors can be trained t...

0 Viktor Bengs, et al. ∙

research

∙ 12/30/2022

Conformal Prediction Intervals for Remaining Useful Lifetime Estimation

The main objective of Prognostics and Health Management is to estimate t...

0 alireza javanmardi, et al. ∙

research

∙ 12/01/2022

AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration

We study the algorithm configuration (AC) problem, in which one seeks to...

0 Jasmin Brandt, et al. ∙

research

∙ 09/07/2022

Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?

This short note is a critical discussion of the quantification of aleato...

0 Eyke Hüllermeier, et al. ∙

research

∙ 09/05/2022

Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams

Explainable Artificial Intelligence (XAI) has mainly focused on static l...

0 Fabian Fumagalli, et al. ∙

research

∙ 06/11/2022

Memorization-Dilation: Modeling Neural Collapse Under Noise

The notion of neural collapse refers to several emergent phenomena that ...

27 Duc Anh Nguyen, et al. ∙

research

∙ 06/08/2022

Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark

In recent years, Explainable AI (xAI) attracted a lot of attention as va...

0 Mohamed Karim Belaid, et al. ∙

research

∙ 05/30/2022

Conformal Credal Self-Supervised Learning

In semi-supervised learning, the paradigm of self-training refers to the...

19 Julian Lienen, et al. ∙

research

∙ 05/20/2022

On Calibration of Ensemble-Based Credal Predictors

In recent years, several classification methods that intend to quantify ...

0 Thomas Mortier, et al. ∙

research

∙ 03/13/2022

Set-valued prediction in hierarchical classification with constrained representation complexity

Set-valued prediction is a well-known concept in multi-class classificat...

0 Thomas Mortier, et al. ∙

research

∙ 03/11/2022

On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation

Uncertainty quantification has received increasing attention in machine ...

0 Viktor Bengs, et al. ∙

research

∙ 02/09/2022

Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget

We consider the combinatorial bandits problem with semi-bandit feedback ...

0 Jasmin Brandt, et al. ∙

research

∙ 02/03/2022

A Survey of Methods for Automated Algorithm Configuration

Algorithm configuration (AC) is concerned with the automated search of t...

2 Elias Schede, et al. ∙

research

∙ 02/02/2022

Non-Stationary Dueling Bandits

We study the non-stationary dueling bandits problem with K arms, where t...

0 Patrick Kolpaczki, et al. ∙

research

∙ 12/15/2021

Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities

Recent applications of machine learning (ML) reveal a noticeable shift f...

0 Eyke Hüllermeier, et al. ∙

research

∙ 11/10/2021

Towards Green Automated Machine Learning: Status Quo and Future Directions

Automated machine learning (AutoML) strives for the automatic configurat...

0 Tanja Tornede, et al. ∙

research

∙ 09/13/2021

Machine Learning for Online Algorithm Selection under Censored Feedback

In online algorithm selection (OAS), instances of an algorithmic problem...

0 Alexander Tornede, et al. ∙

research

∙ 09/10/2021

Automated Machine Learning, Bounded Rationality, and Rational Metareasoning

The notion of bounded rationality originated from the insight that perfe...

0 Eyke Hüllermeier, et al. ∙

research

∙ 08/03/2021

iART: A Search Engine for Art-Historical Images to Support Research in the Humanities

In this paper, we introduce iART: an open Web platform for art-historica...

0 Matthias Springstein, et al. ∙

research

∙ 07/21/2021

Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inference

The idea to distinguish and quantify two important types of uncertainty,...

0 Mohammad Hossein Shaker, et al. ∙

research

∙ 07/20/2021

Algorithm Selection on a Meta Level

The problem of selecting an algorithm that appears most suitable for a s...

0 Alexander Tornede, et al. ∙

research

∙ 06/22/2021

Credal Self-Supervised Learning

Self-training is an effective approach to semi-supervised learning. The ...

6 Julian Lienen, 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

∙ 05/15/2021

Annotation Uncertainty in the Context of Grammatical Change

This paper elaborates on the notion of uncertainty in the context of ann...

0 Marie-Luis Merten, et al. ∙

research

∙ 04/18/2021

Ranking Structured Objects with Graph Neural Networks

Graph neural networks (GNNs) have been successfully applied in many stru...

0 Clemens Damke, et al. ∙

research

∙ 04/08/2021

Efficient time stepping for numerical integration using reinforcement learning

Many problems in science and engineering require the efficient numerical...

0 Michael Dellnitz, 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

∙ 11/17/2020

Towards Meta-Algorithm Selection

Instance-specific algorithm selection (AS) deals with the automatic sele...

0 Alexander Tornede, et al. ∙

research

∙ 11/02/2020

Multi-Armed Bandits with Censored Consumption of Resources

We consider a resource-aware variant of the classical multi-armed bandit...

0 Viktor Bengs, 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

∙ 10/25/2020

Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model

In many real-world applications, the relative depth of objects in an ima...

3 Julian Lienen, et al. ∙

research

∙ 08/04/2020

Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction

Syntactic annotation of corpora in the form of part-of-speech (POS) tags...

0 Stefan Heid, 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

∙ 07/14/2020

Learning Choice Functions via Pareto-Embeddings

We consider the problem of learning to choose from a given set of object...

0 Karlson Pfannschmidt, et al. ∙

Eyke Hüllermeier

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