The article discusses unsupervised machine learning, noting it differs from supervised and reinforcement learning by being a heterogeneous field with multiple objectives. It argues that defining a single goal for unsupervised learning is futile and identifies four distinct goals: estimating the distribution, generating new data points, extracting features for downstream tasks, and understanding the data.
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.
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...
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.
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.
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.
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.
arXiv:2409. 18624v4 Announce Type: replace Abstract: Unsupervised learning methods have a soft inspiration in cognition models.
arXiv:2608. 16700v1 Announce Type: cross Abstract: Various machine unlearning techniques have been developed in response to privacy legislation requirements, enabling individuals to exercise their legal right to have their data $D_f$ removed from a machine learning model.
A primer about Semi-Supervised Learning, the approaches taken with different algorithms and the limitations of using unlabelled data. The post Introduction to Semi-Supervised Learning appeared first on Towards Data Science .
arXiv:2608. 11746v1 Announce Type: new Abstract: Modern systems are increasingly expected to transfer across tasks not specified during training.
arXiv:2408.16189v4 Announce Type: replace-cross Abstract: Transfer learning, in regression or classification, concerns problems where one aims to use imperfect data from a source distribution P to im...
arXiv:2607. 00392v1 Announce Type: cross Abstract: Unsupervised Reinforcement Learning (URL) aims to pre-train scalable, skill-conditioned policies without extrinsic rewards, serving as a foundation for downstream control tasks.