13 citations · 15 across the 5 of their papers we have counts for
9 papers
Compact set-valued deep ensembling in multi-class classification
Kim-Dung Tran, Dang-Man Nguyen, Vu-Linh Nguyen +3
This paper tackles visible challenges in deep ensemble learning, where deep neural networks serve as ensemble members: training and storage burdens, and robustness of cautious (set…
Selectivity Estimation for Semantic Filters on Image Data
Matthias Urban, Vu Huy Nguyen, Gabriele Sanmartino +2
Semantic data systems integrate Large Language Models (LLMs) and Vision-Language Models (VLMs) directly into database query execution, enabling expressive queries on multi-modal da…
Probabilistic Multi-Dimensional Classification
Vu-Linh Nguyen, Yang Yang, Cassio de Campos
Multi-dimensional classification (MDC) can be employed in a range of applications where one needs to predict multiple class variables for each given instance. Many existing MDC met…
Skeptical inferences in multi-label ranking with sets of probabilities
Yonatan Carlos Carranza Alarcón, Vu-Linh Nguyen
In this paper, we consider the problem of making skeptical inferences for the multi-label ranking problem. We assume that our uncertainty is described by a convex set of probabilit…
Confident Sinkhorn Allocation for Pseudo-Labeling
Vu Nguyen, Hisham Husain, Sachin Farfade +1
Semi-supervised learning is a critical tool in reducing machine learning's dependence on labeled data. It has been successfully applied to structured data, such as images and natur…
Learning Gradient Boosted Multi-label Classification Rules
Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz +2
In multi-label classification, where the evaluation of predictions is less straightforward than in single-label classification, various meaningful, though different, loss functions…
On Aggregation in Ensembles of Multilabel Classifiers
Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp +2
While a variety of ensemble methods for multilabel classification have been proposed in the literature, the question of how to aggregate the predictions of the individual members o…
Epistemic Uncertainty Sampling
Vu-Linh Nguyen, Sébastien Destercke, Eyke Hüllermeier
Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learne…
Reliable Multi-label Classification: Prediction with Partial Abstention
Vu-Linh Nguyen, Eyke Hüllermeier
In contrast to conventional (single-label) classification, the setting of multilabel classification (MLC) allows an instance to belong to several classes simultaneously. Thus, inst…