1.7k citations · 2.1k across the 113 of their papers we have counts for
91 papers · 1 filter
Uncertainty Quantification in Pairwise Difference Learning for Classification
Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier
Instead of learning a mapping from instances to outcomes in the standard way, the key idea of pairwise difference learning (PDL) is to learn a function that takes two instances as…
Challenges and Solutions for Bandits in the Wild: Warm-Started Mixture Bandits for Cross-Cohort Slate Recommendation
Serafima Lebedeva, Sumantrak Mukherjee, Ali Arshad Sadal +8
Many recommender services repeatedly encounter cold-start cohorts, where new users arrive with little or no interaction history. This creates two challenges: learning user preferen…
Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier +1
Current evaluation of epistemic uncertainty relies on tasks such as out-ofdistribution detection and active learning. However, the Bayes-optimal decision strategies for these tasks…
OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators
Joshua Stiller, Santo M. A. R. Thies, Felix Czaja +1
Understanding model predictions is essential for physical applications, where outputs often inform safety-critical decisions, such as structural load assessment, weather warnings,…
Uncertainty quantification via conformal prediction in data assimilation
Catherine George, Alireza Javanmardi, Tijana Janjić +1
Quantifying the evolution of uncertainty is critical to both probabilistic forecasting and data assimilation in numerical weather prediction. In this study, we investigate the appl…
Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score
Stefan Haas, Luca Killmaier, Alireza Javanmardi +1
Ordinal classification (OC) arises in high-stakes domains such as medicine and finance, where uncertainty quantification must account for the severity of ordinal errors. Conformal…
What Uncertainties Do We Need for Dynamical Systems?
Yusuf Sale, Christopher Bülte, Felix Czaja +2
The distinction between aleatoric and epistemic uncertainty has received considerable attention in machine learning research, mainly in the context of supervised learning but also…
Calibrated Preference Learning: The Case of Label Ranking
Santo M. A. R. Thies, Viktor Bengs, Timo Kaufmann +2
Calibration, the alignment of predicted probabilities with true outcome frequencies, is essential for reliable decision-making. While extensively studied for classification and reg…
CLANE: Continual Learning of Actions on Neuromorphic Hardware from Event Cameras
Elvin Hajizada, Michael Neumeier, Edward Paxon Frady +4
Recognizing and continuously learning novel human actions without forgetting prior classes is a requirement for emerging AR/VR and robotics applications. For these applications, bo…
Unification and Optimization of Robust Supervised Learning
Jonas Hanselle, Valentin Margraf, Clemens Damke +1
Robust learning methods such as distributionally robust optimization, vicinal risk minimization, Mixup, and label smoothing address different sources of uncertainty, including dist…
Proxy-Based Approximation of Shapley and Banzhaf Interactions
Santo M. A. R. Thies, Hubert Baniecki, R. Teal Witter +3
Shapley and Banzhaf interactions capture the complex dynamics inherent in modern machine learning applications. However, current estimators for these higher-order interactions trad…
Efficient Credal Prediction through Decalibration
Paul Hofman, Timo Löhr, Maximilian Muschalik +2
A reliable representation of uncertainty is essential for the application of modern machine learning methods in safety-critical settings. In this regard, the use of credal sets (i.…
Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models
Kanta Yamaoka, Sumantrak Mukherjee, Thomas Gärtner +5
Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reasoning---estimating effect sizes…
ResponseRank: Data-Efficient Reward Modeling through Preference Strength Learning
Timo Kaufmann, Yannick Metz, Daniel Keim +1
Binary choices, as often used for reinforcement learning from human feedback (RLHF), convey only the direction of a preference. A person may choose apples over oranges and bananas…
Distribution Matching for Graph Quantification Under Structural Covariate Shift
Clemens Damke, Eyke Hüllermeier
Graphs are commonly used in machine learning to model relationships between instances. Consider the task of predicting the political preferences of users in a social network; to so…
Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits
Sumantrak Mukherjee, Serafima Lebedeva, Valentin Margraf +6
We propose a novel Bayesian framework for efficient exploration in contextual multi-task multi-armed bandit settings, where the context is only observed partially and dependencies…
Uncertainty Quantification for Machine Learning: One Size Does Not Fit All
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
Proper quantification of predictive uncertainty is essential for the use of machine learning in safety-critical applications. Various uncertainty measures have been proposed for th…
Online Continual Learning on Intel Loihi 2 via a Co-designed Spiking Neural Network
Elvin Hajizada, Danielle Rager, Timothy Shea +5
Edge AI must adapt to new classes and shifting data within a tight power budget, but online continual learning (learning from each sample before the next arrives) is where conventi…
Efficient Online Learning with Predictive Coding Networks: Exploiting Temporal Correlations
Darius Masoum Zadeh-Jousdani, Elvin Hajizada, Eyke Hüllermeier
Robotic systems operating at the edge require efficient online learning algorithms that can continuously adapt to changing environments while processing streaming sensory data. Tra…
Fine-Grained Uncertainty Decomposition in Large Language Models: A Spectral Approach
Nassim Walha, Sebastian G. Gruber, Thomas Decker +4
As Large Language Models (LLMs) are increasingly integrated in diverse applications, obtaining reliable measures of their predictive uncertainty has become critically important. A…
Aleatoric and Epistemic Uncertainty Measures for Ordinal Classification through Binary Reduction
Stefan Haas, Eyke Hüllermeier
Ordinal classification problems, where labels exhibit a natural order, are prevalent in high-stakes fields such as medicine and finance. Accurate uncertainty quantification, includ…
Uncertainty Quantification with Proper Scoring Rules: Adjusting Measures to Prediction Tasks
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
We address the problem of uncertainty quantification and propose measures of total, aleatoric, and epistemic uncertainty based on a known decomposition of (strictly) proper scoring…
Antithetic Sampling for Top-k Shapley Identification
Patrick Kolpaczki, Tim Nielen, Eyke Hüllermeier
Additive feature explanations rely primarily on game-theoretic notions such as the Shapley value by viewing features as cooperating players. The Shapley value's popularity in and o…
Adjusted Count Quantification Learning on Graphs
Clemens Damke, Eyke Hüllermeier
Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances a…