arXiv:2603.27072v1 Announce Type: cross
Abstract: Weight decay is ubiquitous in training deep neural network architectures. Its empirical success is often attributed to capacity control; nonetheless, our theoretical understanding of its effect on the …
arXiv:2410.06326v2 Announce Type: replace-cross
Abstract: Gaussian graphical models (GGMs) are widely used to recover the conditional independence structure among random variables. Recent work has sought to incorporate auxiliary covariates to improve …
arXiv:2603.25860v1 Announce Type: new
Abstract: Transformer architectures have achieved remarkable empirical success in modeling contextual relationships in natural language, yet a precise mathematical characterization of their expressive power remain…
arXiv:2603.26478v1 Announce Type: cross
Abstract: Motifs often recur in musical works in altered forms, preserving aspects of their identity while undergoing local variation. This paper investigates how such motivic transformations occur within their …
arXiv:2603.25796v1 Announce Type: new
Abstract: We provide explicit, finite-sample guarantees for learning causal representations from data with a sublinear number of environments. Causal representation learning seeks to provide a rigourous foundation…
arXiv:2603.24771v2 Announce Type: replace-cross
Abstract: Missing data is a ubiquitous challenge in data analysis, often leading to biased and inaccurate results. Traditional imputation methods usually assume that the missingness mechanism is missing-…
arXiv:2603.25869v1 Announce Type: cross
Abstract: Self-supervised image denoising methods have traditionally relied on either architectural constraints or specialized loss functions that require prior knowledge of the noise distribution to avoid the t…
arXiv:2603.25776v1 Announce Type: new
Abstract: We propose SAHMM-VAE, a source-wise adaptive Hidden Markov prior variational autoencoder for unsupervised blind source separation. Instead of treating the latent prior as a single generic regularizer, th…
arXiv:2603.24763v2 Announce Type: replace-cross
Abstract: Conditional independence is central to modern statistics, but beyond special parametric families it rarely admits an exact covariance characterization. We introduce the binary expansion group i…
arXiv:2603.26502v1 Announce Type: cross
Abstract: In recent years, there has been growing interest in causal machine learning estimators for quantifying subject-specific effects of a binary treatment on time-to-event outcomes. Estimation approaches ha…