arXiv:2608. 12057v1 Announce Type: new Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of evaluation techniques to measure the quality of a specific method.
By Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek
arXiv:2608.12057v4 Announce Type: replace
Abstract: Feature selection is one of the most important and fundamental tasks in data mining, tackled by a family of methods with an established set of eval...
By Hafiz Saud Arshad, Muhammad Rajabinasab, Arthur Zimek
The paper argues that unlabeled data does not mean there is no human supervision in visual learning. It highlights that different data curation and training objectives embed distinct human priors, making the blanket term "unsupervised" misleading. The authors call for explicit disclosure of these priors to improve clarity, comparison, and methodological diversity in the field.
By Dong Lao
The paper highlights that current deep machine unlearning research focuses on overall effectiveness, neglecting how difficult it is to unlearn individual training samples. It presents a detailed instance-level performance analysis across various algorithms and datasets, identifying four factors that make unlearning a data point hard. The study shows these factors are independent of the unlearning method but depend on the target model and its training data, urging future research to consider instance-level difficulty.
By Hammad Rizwan, Mahtab Sarvmaili, Hassan Sajjad, Ga Wu
arXiv:2601. 19810v2 Announce Type: replace-cross Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks.
By Octavio Pappalardo
This post describes four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning. In addition to describing our work, this post will tell you a bit more about generative models: what they are, why they are important, and where they might be going.