Every story across every category, newest first. Each card links to the original publisher; daily-brief posts open as editorial pages.
RT PoIiMathThis is one of those moments where I feel most disconnected from my normie friends I'm like "do you realize what this means?" And they…
vulkan: Apply bias before softmax in FA, to avoid overflow (#24909) macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiAI enabled) DISABLED macOS Intel (x64)…
arXiv:2507.11768v3 Announce Type: replace Abstract: Bayesian accounts of in-context learning face a direct objection: exact posterior predictives for exchangeable data are invariant to task-preserving order, yet…
arXiv:2601.21860v3 Announce Type: replace-cross Abstract: The reconstruction and inference of stochastic dynamical systems from data is a fundamental task in inverse problems and statistical learning. While…
arXiv:2410.14843v4 Announce Type: replace Abstract: Vanilla variational inference finds an optimal approximation to the Bayesian posterior distribution, but even the exact Bayesian posterior is often not…
arXiv:2501.07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction. In this context, we study a content exploration task, which we…
arXiv:2307.13127v3 Announce Type: replace Abstract: Data used to train predictive models via empirical risk minimization (ERM) often contain sensitive personal information. While differential privacy (DP) provides…
arXiv:2410.11116v4 Announce Type: replace-cross Abstract: In this paper, we establish a novel connection between the metric entropy growth and the embeddability of function spaces into reproducing…
arXiv:2606.24465v1 Announce Type: cross Abstract: We study the problem of estimating the arrival times of vertices in a uniform random recursive tree from its unlabeled structure.…
arXiv:2606.18729v2 Announce Type: replace Abstract: Data valuation quantifies the intrinsic quality of individual samples to enable principled data curation, quality control, and robust learning. For time…
arXiv:2606.24427v1 Announce Type: cross Abstract: Nonlinear mixed-effects models are widely used to analyze longitudinal data, but existing open-source software often supports only a limited subset of…
arXiv:2606.24715v1 Announce Type: new Abstract: We consider the problem of model selection between probabilistic models on data sets of time series. Chosen a proper scoring rule,…
arXiv:2606.24418v1 Announce Type: cross Abstract: Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems. Given a group acting on the…
arXiv:2605.19208v2 Announce Type: replace-cross Abstract: Physical activity (PA) plays an important role in maintaining and improving health. Daily steps have been a key PA measure that…
arXiv:2606.23871v1 Announce Type: cross Abstract: Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a…
arXiv:2606.23944v1 Announce Type: cross Abstract: State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture…
arXiv:2606.24244v1 Announce Type: cross Abstract: AI-assisted interviews promise to reduce respondent burden in surveys by allowing respondents to describe experiences naturally while an AI system noisily…
arXiv:2606.24168v1 Announce Type: cross Abstract: Automated diabetic retinopathy (DR) grading from colour fundus photographs can achieve strong predictive performance, but clinical interpretation requires more than an…
arXiv:2606.18074v2 Announce Type: replace Abstract: Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal…
arXiv:2606.24348v1 Announce Type: cross Abstract: The growing reliance on machine learning for decisions across sectors underscores the importance of model transparency and interpretability. Existing post hoc…
arXiv:2401.14483v4 Announce Type: replace-cross Abstract: In the current practices of machine learning, the evaluation of forecasts has become a cornerstone of scientific progress. A multitude of…
arXiv:2602.16568v2 Announce Type: replace-cross Abstract: Sparse recovery is among the most well-studied problems in learning theory and high-dimensional statistics. In this work, we investigate the statistical…
arXiv:2503.17300v5 Announce Type: replace-cross Abstract: We propose a variational tail bound for norms of random vectors and matrices under moment assumptions on their one-dimensional marginals. A…
arXiv:2605.02701v2 Announce Type: replace-cross Abstract: We propose a robust gradient estimator based on per-sample gradient clipping and analyze its properties both theoretically and empirically. We show…