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09/05/2023
Probabilistic Self-supervised Learning via Scoring Rules Minimization
In this paper, we propose a novel probabilistic self-supervised learning...
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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...
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08/21/2023
Weighting by Tying: A New Approach to Weighted Rank Correlation
Measures of rank correlation are commonly used in statistics to capture ...
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07/13/2023
A Novel Bayes' Theorem for Upper Probabilities
In their seminal 1990 paper, Wasserman and Kadane establish an upper bou...
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06/16/2023
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?
Adequate uncertainty representation and quantification have become imper...
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06/13/2023
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Post-hoc explanation techniques such as the well-established partial dep...
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06/01/2023
Conformal Prediction with Partially Labeled Data
While the predictions produced by conformal prediction are set-valued, t...
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06/01/2023
UNGOML: Automated Classification of unsafe Usages in Go
The Go programming language offers strong protection from memory corrupt...
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05/25/2023
Koopman Kernel Regression
Many machine learning approaches for decision making, such as reinforcem...
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05/23/2023
Mitigating Label Noise through Data Ambiguation
Label noise poses an important challenge in machine learning, especially...
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04/30/2023
Detecting Novelties with Empty Classes
For open world applications, deep neural networks (DNNs) need to be awar...
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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...
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03/02/2023
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams
Explainable Artificial Intelligence (XAI) focuses mainly on batch learni...
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03/02/2023
SHAP-IQ: Unified Approximation of any-order Shapley Interactions
Predominately in explainable artificial intelligence (XAI) research, the...
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02/01/2023
Approximating the Shapley Value without Marginal Contributions
The Shapley value is arguably the most popular approach for assigning a ...
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02/01/2023
Iterative Deepening Hyperband
Hyperparameter optimization (HPO) is concerned with the automated search...
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01/30/2023
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
It is well known that accurate probabilistic predictors can be trained t...
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12/30/2022
Conformal Prediction Intervals for Remaining Useful Lifetime Estimation
The main objective of Prognostics and Health Management is to estimate t...
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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...
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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...
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09/05/2022
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams
Explainable Artificial Intelligence (XAI) has mainly focused on static l...
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06/11/2022
Memorization-Dilation: Modeling Neural Collapse Under Noise
The notion of neural collapse refers to several emergent phenomena that ...
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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...
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05/30/2022
Conformal Credal Self-Supervised Learning
In semi-supervised learning, the paradigm of self-training refers to the...
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05/20/2022
On Calibration of Ensemble-Based Credal Predictors
In recent years, several classification methods that intend to quantify ...
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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...
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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 ...
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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 ...
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02/03/2022
A Survey of Methods for Automated Algorithm Configuration
Algorithm configuration (AC) is concerned with the automated search of t...
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02/02/2022
Non-Stationary Dueling Bandits
We study the non-stationary dueling bandits problem with K arms, where t...
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12/15/2021
Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities
Recent applications of machine learning (ML) reveal a noticeable shift f...
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11/10/2021
Towards Green Automated Machine Learning: Status Quo and Future Directions
Automated machine learning (AutoML) strives for the automatic configurat...
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09/13/2021
Machine Learning for Online Algorithm Selection under Censored Feedback
In online algorithm selection (OAS), instances of an algorithmic problem...
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09/10/2021
Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
The notion of bounded rationality originated from the insight that perfe...
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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...
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07/21/2021
Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inference
The idea to distinguish and quantify two important types of uncertainty,...
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07/20/2021
Algorithm Selection on a Meta Level
The problem of selecting an algorithm that appears most suitable for a s...
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06/22/2021
Credal Self-Supervised Learning
Self-training is an effective approach to semi-supervised learning. The ...
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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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05/15/2021
Annotation Uncertainty in the Context of Grammatical Change
This paper elaborates on the notion of uncertainty in the context of ann...
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04/18/2021
Ranking Structured Objects with Graph Neural Networks
Graph neural networks (GNNs) have been successfully applied in many stru...
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04/08/2021
Efficient time stepping for numerical integration using reinforcement learning
Many problems in science and engineering require the efficient numerical...
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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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11/17/2020
Towards Meta-Algorithm Selection
Instance-specific algorithm selection (AS) deals with the automatic sele...
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11/02/2020
Multi-Armed Bandits with Censored Consumption of Resources
We consider a resource-aware variant of the classical multi-armed bandit...
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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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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...
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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...
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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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07/14/2020