Unsupervised feature selection (FS) is essential for high-dimensional learning tasks where labels are not available. It helps reduce noise, improve generalization, and enhance interpretability. However, most existing unsupervised FS methods evaluate features in isolation, even though informative signals often emerge from groups of related features. For example, adjacent pixels, functionally connected brain regions, or correlated financial indicators tend to act together, making independent evaluation suboptimal. Although some methods attempt to capture group structure, they typically rely on predefined partitions or label supervision, limiting their applicability. We propose GroupFS, an end-to-end, fully differentiable framework that jointly discovers latent feature groups and selects the most informative groups among them, without relying on fixed a priori groups or label supervision. GroupFS enforces Laplacian smoothness on both feature and sample graphs and applies a group sparsity regularizer to learn a compact, structured representation. Across nine benchmarks spanning images, tabular data, and biological datasets, GroupFS consistently outperforms state-of-the-art unsupervised FS in clustering and selects groups of features that align with meaningful patterns.

Feature selection is a crucial tool in machine learning and is widely applied across various scientific disciplines. Traditional supervised methods generally identify a universal set of informative features for the entire population. However, feature relevance often varies with context, while the context itself may not directly affect the outcome variable. Here, we propose a novel architecture for contextual feature selection where the subset of selected features is conditioned on the value of *context variables*. Our new approach, Conditional Stochastic Gates (c-STG), models the importance of features using conditional Bernoulli variables whose parameters are predicted based on contextual variables. We introduce a hypernetwork that maps context variables to feature selection parameters to learn the context-dependent gates along with a prediction model. We further present a theoretical analysis of our model, indicating that it can improve performance and flexibility over population-level methods in complex feature selection settings. Finally, we conduct an extensive benchmark using simulated and real-world datasets across multiple domains demonstrating that c-STG can lead to improved feature selection capabilities while enhancing prediction accuracy and interpretability.

Many voice conversion systems require parallel training sets of the source and target speakers. Non-parallel training is more complicated as it involves evaluation of source-target correspondence along with the conversion function itself. INCA is a recently proposed method for non-parallel training, based on iterative estimation of alignment and conversion function. The alignment is evaluated using a simple nearest-neighbor search, which often leads to phonetic miss-matched source-target pairs. We propose here a generalized approach, denoted as Temporal-Context INCA (TC-INCA), based on matching temporal context vectors. We formulate the training stage as a minimization problem of a joint cost, considering both context-based alignment and conversion function. We show that TC-INCA reduces the joint cost and prove its convergence. Experimental results indicate that TC-INCA significantly improves the alignment accuracy, compared to INCA. Moreover, subjective evaluations show that TC-INCA leads to improved quality of the synthesized output signals, when small training sets are used.

The goal of voice conversion is to transform a sentence said by one speaker, to sound as if another speaker had said it. The classical conversion based on a Gaussian Mixture Model and several other schemes suggested since, produce muffled sounding outputs, due to excessive smoothing of the spectral envelopes. To reduce the muffling effect, enhancement of the Global Variance (GV) of the spectral features was recently suggested. We propose a different approach for GV enhancement, based on the classical conversion formalized as a GV-constrained minimization. Listening tests show that an improvement in quality is achieved by the proposed approach.