Uncertain data in learning: challenges and opportunities
S Destercke - Conformal and Probabilistic Prediction with …, 2022 - proceedings.mlr.press
Dealing with uncertain data in statistical estimation problems or in machine learning is not
really a new issue. However, such uncertainty has so far mostly been modelled either as …
really a new issue. However, such uncertainty has so far mostly been modelled either as …
Rough set-based feature selection for weakly labeled data
Supervised learning is an important branch of machine learning (ML), which requires a
complete annotation (labeling) of the involved training data. This assumption is relaxed in …
complete annotation (labeling) of the involved training data. This assumption is relaxed in …
Reciprocal learning
J Rodemann, C Jansen… - Advances in Neural …, 2025 - proceedings.neurips.cc
We demonstrate that numerous machine learning algorithms are specific instances of one
single paradigm: reciprocal learning. These instances range from active learning over multi …
single paradigm: reciprocal learning. These instances range from active learning over multi …
In all likelihoods: Robust selection of pseudo-labeled data
Self-training is a simple yet effective method within semi-supervised learning. Self-training's
rationale is to iteratively enhance training data by adding pseudo-labeled data. Its …
rationale is to iteratively enhance training data by adding pseudo-labeled data. Its …
Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches
The aim of this article is to study the relationship between two popular Cautious Learning
approaches, namely: Three-way decision (TWD) and conformal prediction (CP). Based on …
approaches, namely: Three-way decision (TWD) and conformal prediction (CP). Based on …
Learning from fuzzy labels: Theoretical issues and algorithmic solutions
A Campagner - International Journal of Approximate Reasoning, 2024 - Elsevier
In this article we study the problem of learning from fuzzy labels (LFL), a form of weakly
supervised learning in which the supervision target is not precisely specified but is instead …
supervised learning in which the supervision target is not precisely specified but is instead …
[HTML][HTML] Synergies between machine learning and reasoning-An introduction by the Kay R. Amel group
This paper proposes a tentative and original survey of meeting points between Knowledge
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …
Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have …
Attribute reduction using self-information uncertainty measures in optimistic neighborhood extreme-granulation rough set
K Qu, P Gao, Q Dai, Y Sun, X Hua - Information Sciences, 2025 - Elsevier
The pessimistic and optimistic neighborhood multi-granulation rough sets (PNMRS and
ONMRS) have been applied to attribute reduction. Nonetheless, the setting of the …
ONMRS) have been applied to attribute reduction. Nonetheless, the setting of the …
Uncertainty-wise software anti-patterns detection: a possibilistic evolutionary machine learning approach
Context: Code smells (aka anti-patterns) are manifestations of poor design solutions that can
deteriorate software maintainability and evolution. Research gap: Existing works did not take …
deteriorate software maintainability and evolution. Research gap: Existing works did not take …
Bayesian Data Selection
J Rodemann - arxiv preprint arxiv:2406.12560, 2024 - arxiv.org
A wide range of machine learning algorithms iteratively add data to the training sample.
Examples include semi-supervised learning, active learning, multi-armed bandits, and …
Examples include semi-supervised learning, active learning, multi-armed bandits, and …