arXiv:2603.23736v1 Announce Type: cross
Abstract: Many scientific systems, such as cellular populations or economic cohorts, are naturally described by probability distributions that evolve over time. Predicting how such a system would have evolved un…
arXiv:2603.23835v1 Announce Type: cross
Abstract: There remain theoretical gaps in deep neural network estimators for the nonparametric Cox proportional hazards model. In particular, it is unclear how gradient-based optimization error propagates to po…
arXiv:2507.00629v4 Announce Type: replace-cross
Abstract: Understanding the generalization properties of neural networks on simple input-output distributions is key to explaining their performance on real datasets. The classical teacher-student settin…
arXiv:2512.09295v3 Announce Type: replace-cross
Abstract: Consider the additive Gaussian model $Y = X + \sigma Z$, where $X \sim P$ is an unknown signal, $Z \sim N(0,1)$ is independent of $X$, and $\sigma > 0$ is known. Let $Q$ denote the law of $Y$. …
arXiv:2508.01517v3 Announce Type: replace-cross
Abstract: We develop a central limit theorem (CLT) for a non-parametric estimator of the transition matrices in controlled Markov chains (CMCs) with finite state-action spaces. Our results establish prec…
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…
arXiv:2603.24495v1 Announce Type: cross
Abstract: While the mathematical foundations of score-based generative models are increasingly well understood for unconstrained Euclidean spaces, many practical applications involve data restricted to bounded d…
arXiv:2602.12023v2 Announce Type: replace-cross
Abstract: Applied work under interference typically models outcomes as functions of own treatment and a low-dimensional exposure mapping of others’ treatments, even when that mapping may be misspecified….
arXiv:2603.24545v1 Announce Type: cross
Abstract: We study the problem of detecting local geometry in random graphs. We introduce a model $\mathcal{G}(n, p, d, k)$, where a hidden community of average size $k$ has edges drawn as a random geometric gra…