Probability & Statistics · Mathematics & Statistics
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298 papers, from 31 August 2026 to 5 November 2026: 270 arXiv preprints and 28 OpenAlex records.
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Newest papers, page 11
Algorithmic trading and stochastic integration
We study simple predictable processes whose coefficients are represented by neural networks.Boolean threshold functions, neuron capacity, and memory retrieval
How much information can a single neuron remember?On the SoS Certifiability of Log-Concave Distributions
For an arbitrary isotropic log-concave distribution $P$ on $\mathbb{R}^d$, we prove that the polynomial $(Cm)^m\|v\|_2^m - \mathbb{E}_{X\sim P}\langle X,v\rangle^m$ is a sum of squares for every…Stability of Change-of-Numéraire Reweighting: An Exact Wasserstein Boundary
Reweighting a probability law by a positive numéraire and pushing forward the payoff-to-numéraire ratio yields the change-of-numéraire reweighting: the swap-rate law under the annuity measure, the…Potential Hessian Ascent IV: Sampling the Sherrington-Kirkpatrick model at $β< 1$
We give a polynomial-time algorithm to sample from the Gibbs measure of the Sherrington-Kirkpatrick (SK) model with $o_n(1)$ error in total-variation distance (TVD) at any inverse-temperature $β< 1$.Sampling Line-Graph Colorings with Constant Extra Colors
Let $G$ be the line graph of a finite simple graph, with $n\geq1$ vertices and maximum degree $Δ$.Distributed Stochastic Approximation Algorithms and Heavy-Tailed Age of Information
Algorithms in multi-agent systems such as federated learning, mobile robotic swarming, and consensus control can be designed and analyzed as distributed stochastic approximation algorithms.Robustness of Diffusion Models under Distribution Shift
Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution, yet existing theoretical guarantees largely…Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler
Local geometric mixing localizes geometric mixing by requiring geometric convergence to equilibrium in total variation only over finitely many transitions.Global Convergence of Third-Order Langevin Dynamics for Non-Convex Optimization via Simulated Annealing
We study global convergence guarantees of third-order Langevin dynamics for non-convex optimization via simulated annealing with fixed friction and decreasing noise.Eigenvalue and Eigenvector Approximation for Random Matrices Using Low-Degree Polynomials
We initiate the study of approximating the top eigenvalue and eigenvector of a random symmetric matrix $ A \in \mathbb{R}^{n\times n} $ using $ q(A)b $ where $q$ is a degree-$d$ polynomial and $b$…Adaptive Random Matrices in Gaussian Bandits: Spectral Universality and Selection-Induced Outliers
Adaptive arm selection changes the distribution of the observations collected by a bandit algorithm, but it need not change their limiting empirical spectrum.Weighted universal Value-at-Risk Superadditivity for discrete distributions
The concept of weighted universal Value-at-Risk superadditivity (WUVS) was recently introduced by Chen et al. (2026) as a generalization of the question whether for some infinite mean distributions…Quantum-Enhanced Sampling of Schrödinger Bridges
We consider the dynamic Schrödinger bridge problem on a finite state space and exploit its Markov structure to decompose the problem into an endpoint coupling and a collection of conditional Markov…Local Weak Limits for Equilibrium and Risk in Economic Networks
We study equilibrium and risk evaluation in large sparse economic networks with heterogeneous responses, shocks, and bilateral exposures.The Critical Line Algorithm and the Constrained LASSO: One Curve, Two Literatures
Many statistical procedures compute an entire solution path rather than a single estimator.High-Dimensional Ultra-Log-Concave Distributions
Ultra-log-concave distributions are ubiquitous in probability, combinatorics, and statistical mechanics and have been studied extensively.On Deterministically Computing Total Variation Distance via Zonotope Compression
We study deterministic relative approximation of the total variation distance between high-dimensional distributions given by succinct descriptions.Affine Volterra covariance processes and application to commodity markets
We study affine stochastic Volterra equations on the cone of symmetric positive semidefinite matrices.Penalized Nonreversible Langevin for Constrained Sampling
We propose penalized nonreversible Langevin algorithms for sampling from $π(x)\propto e^{-f(x)}\mathbf 1_{\mathcal C}(x)$, where $\mathcal C\subset\mathbb R^d$ is a compact convex set.