arXiv:2509.25599v1 Announce Type: new Abstract: We consider inferring the causal effect of a treatment (intervention) on an outcome of interest in situations where there is potentially an unobserved confounder influencing both the treatment and the outcome. This is achievable by…
arXiv:2509.25588v1 Announce Type: new Abstract: In binary classification applications, conservative decision-making that allows for abstention can be advantageous. To this end, we introduce a novel approach that determines the optimal cutoff interval for risk scores, which can be directly available…
arXiv:2509.25507v1 Announce Type: new Abstract: How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applications in both modern machine learning and classical statistics, including image post-processing in…
arXiv:2509.25802v1 Announce Type: new Abstract: We introduce a novel framework for graph signal processing (GSP) that models signals as graph distribution-valued signals (GDSs), which are probability distributions in the Wasserstein space. This approach overcomes key limitations of classical vector-based GSP,…
arXiv:2509.23946v2 Announce Type: replace-cross Abstract: Chain-of-Thought (CoT) and its variants have markedly advanced the reasoning abilities of Large Language Models (LLMs), yet their monolithic and auto-regressive architecture inherently conflates high-level strategic planning with low-level step-by-step execution, leading to computational inefficiency,…
arXiv:2509.26005v1 Announce Type: new Abstract: We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields — a key task in oceanography, marine science, and ocean engineering — using a physics-informed spatio-temporal…
arXiv:2509.26371v1 Announce Type: cross Abstract: Recently, there has been growing interest in characterizing the function spaces underlying neural networks. While shallow and deep scalar-valued neural networks have been linked to scalar-valued reproducing kernel Banach spaces (RKBS), $R^d$-valued neural networks and…
arXiv:2509.26149v1 Announce Type: new Abstract: A central challenge in understanding generalization is to obtain non-vacuous guarantees that go beyond worst-case complexity over data or weight space. Among existing approaches, PAC-Bayes bounds stand out as they can provide tight, data-dependent guarantees…
arXiv:2502.04575v2 Announce Type: replace Abstract: Given an unnormalized probability density $piproptomathrm{e}^{-V}$, estimating its normalizing constant $Z=int_{mathbb{R}^d}mathrm{e}^{-V(x)}mathrm{d}x$ or free energy $F=-log Z$ is a crucial problem in Bayesian statistics, statistical mechanics, and machine learning. It is challenging especially in high dimensions…
arXiv:2509.26175v1 Announce Type: new Abstract: The Metropolis-within-Gibbs (MwG) algorithm is a widely used Markov Chain Monte Carlo method for sampling from high-dimensional distributions when exact conditional sampling is intractable. We study MwG with Random Walk Metropolis (RWM) updates, using proposal…
