arXiv:2603.24771v1 Announce Type: 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-at-rando…
arXiv:2603.24833v1 Announce Type: cross
Abstract: We introduce a flexible framework for high-dimensional matrix estimation to incorporate side information for both rows and columns. Existing approaches, such as inductive matrix completion, often impos…
arXiv:2603.24025v1 Announce Type: new
Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures. It’s common that only a few features, called the influential features, m…
arXiv:2402.08151v4 Announce Type: replace-cross
Abstract: Importance sampling (IS) is an efficient stand-in for model refitting in performing (LOO) cross-validation (CV) on a Bayesian model. IS inverts the Bayesian update for a single observation by r…
arXiv:2603.24227v1 Announce Type: new
Abstract: In nonnegative matrix factorization (NMF), minimum-volume-constrained NMF is a widely used framework for identifying the solution of NMF by making basis vectors as similar as possible. This typically ind…
arXiv:2603.24392v1 Announce Type: cross
Abstract: Privacy and algorithmic fairness have become two central issues in modern machine learning. Although each has separately emerged as a rapidly growing research area, their joint effect remains comparati…
arXiv:2603.24041v1 Announce Type: cross
Abstract: Deep neural networks (DNNs) achieve remarkable predictive performance but remain difficult to interpret, largely due to overparameterization that obscures the minimal structure required for interpretat…
arXiv:2603.23688v1 Announce Type: cross
Abstract: Background: Determining an adequate sample size is essential for developing reliable and generalisable clinical prediction models, yet practical guidance on selecting appropriate methods remains limite…
arXiv:2603.23923v1 Announce Type: cross
Abstract: Predictive inference is a fundamental task in statistics, traditionally addressed using parametric assumptions about the data distribution and detailed analyses of how models learn from data. In recent…
arXiv:2603.24333v1 Announce Type: cross
Abstract: Recently, Forr\’e (arXiv:2104.11547, 2021) introduced transitional conditional independence, a notion of conditional independence that provides a unified framework for both random and non-stochastic va…