Papers
1–3 of 3Research Paper·Jan 30, 2026
Machine Unlearning in Low-Dimensional Feature Subspace
Machine Unlearning (MU) aims at removing the influence of specific data from a pretrained model while preserving performance on the remaining data. In this work, a novel perspective for MU is presente...
7.0 viability
Research Paper·Feb 4, 2026·B2B
Quality Model for Machine Learning Components
Despite increased adoption and advances in machine learning (ML), there are studies showing that many ML prototypes do not reach the production stage and that testing is still largely limited to testi...
6.0 viability
Research Paper·Feb 19, 2026
FAMOSE: A ReAct Approach to Automated Feature Discovery
Feature engineering remains a critical yet challenging bottleneck in machine learning, particularly for tabular data, as identifying optimal features from an exponentially large feature space traditio...
6.0 viability