arXiv:2502.20123v2 Announce Type: replace-cross Abstract: Given a collection of observed signals corrupted with Gaussian noise, how can we learn to optimally denoise them? This fundamental problem arises in both empirical Bayes and generative modeling. In empirical Bayes, the predominant approach…
arXiv:2509.19559v1 Announce Type: new Abstract: We introduce the Stochastic Correlated Obstacle Scene (SCOS) problem, a navigation setting with spatially correlated obstacles of uncertain blockage status, realistically constrained sensors that provide noisy readings and costly disambiguation. Modeling the spatial correlation with…
arXiv:2509.19367v1 Announce Type: cross Abstract: We present a sensor-fusion framework for rapid, non-destructive classification and quality control of organic substances, built on a standard Arduino Mega 2560 microcontroller platform equipped with three commercial environmental and gas sensors. All data used…
arXiv:2509.19901v1 Announce Type: cross Abstract: We study pure exploration in structured stochastic multi-armed bandits, aiming to efficiently identify the correct hypothesis from a finite set of alternatives. For a broad class of tasks, asymptotic analyses reduce to a maximin optimization…
arXiv:2509.19962v1 Announce Type: cross Abstract: Discrete diffusion models (DDMs) have shown powerful generation ability for discrete data modalities like text and molecules. However, their practical application is hindered by inefficient sampling, requiring a large number of sampling steps. Accelerating DDMs…
arXiv:2509.20323v1 Announce Type: cross Abstract: We prove the first guarantees of sparse recovery for ReLU neural networks, where the sparse network weights constitute the signal to be recovered. Specifically, we study structural properties of the sparse network weights for two-layer,…
arXiv:2509.19387v1 Announce Type: cross Abstract: Spike-and-wave discharges (SWDs) are the electroencephalographic hallmark of absence epilepsy, yet their manual identification in multi-day recordings remains labour-intensive and error-prone. We present a lightweight hybrid pipeline that couples analytical features with a shallow artificial…
arXiv:2210.06140v4 Announce Type: replace Abstract: Differentially private (DP) mechanisms protect individual-level information by introducing randomness into the statistical analysis procedure. Despite the availability of numerous DP tools, there remains a lack of general techniques for conducting statistical inference under DP.…
arXiv:2509.19490v1 Announce Type: cross Abstract: In regression and causal inference, controlled subgroup selection aims to identify, with inferential guarantees, a subgroup (defined as a subset of the covariate space) on which the average response or treatment effect is above a…
arXiv:2502.13570v3 Announce Type: replace Abstract: Two-sample hypothesis testing-determining whether two sets of data are drawn from the same distribution-is a fundamental problem in statistics and machine learning with broad scientific applications. In the context of nonparametric testing, maximum mean discrepancy…
