A field on ReadSearch
Mathematics & Statistics
New mathematics and statistics is posted on arXiv, often long before it appears in a journal. This page shows the newest papers ReadSearch has read, from algebra and geometry to analysis, probability and applied mathematics.
The newest 1,000 papers, from 13 October 2026 to 5 November 2026: 968 arXiv preprints and 32 OpenAlex records.
Newest papers, page 37
Minimax Optimality of the NPMLE for Gaussian Mixtures in Hellinger Distance
We analyze the problem of estimating univariate Gaussian mixture densities using the nonparametric maximum likelihood estimator (NPMLE) based on $n$ independent and identically distributed samples.Self-normalized Bernstein Inequalities for Regenerative Processes and Applications to Learning Without Independence
We prove a self-normalized Bernstein inequality for bounded functions of a regenerative process with independent, identically distributed marked cycles of finite mean length.A Quasipolynomial Algorithm for $\ell_\infty$ Contractions
We give an algorithm for computing an $\eps$-approximate fixed point of any $d$-dimensional nonexpansion with respect to the $\ell_\infty$ norm in time $\log(1/\eps)^{O(\log^2 d)}$.Temporal Exploration and Rendezvous via Weisfeiler-Leman Colorings
Temporal graphs are graphs whose edge relation changes in discrete time steps.Differentially Private Synthetic Data via Generalized Moment Matching
We study $(\varepsilon,δ)$-differentially private synthetic data for arbitrary databases of $n$ records in $[-1,1]^d$ under a specified integral probability metric (IPM).When Reconstruction Is Not Enough: Doubly Robust Counterfactual and Interventional Mediation Analysis with Learned Multimodal Representations
Learned representations of high-dimensional multimodal health records are increasingly used to adjust for confounding, yet they are usually trained to reconstruct the data rather than preserve the…Generalizing Metropolis-Hastings for DAG Sampling
A fundamental challenge in Bayesian network structure learning lies in efficiently approximating the posterior distribution of directed acyclic graphs (DAGs).A Transformer-based Approach for Real-time Multi-agent Path Planning with Varying Initial/Final Conditions
We address the problem of real-time multi-agent path planning under prescribed initial and final configurations, formulated as a high-dimensional optimal control problem subject to inter-agent…Polynomial pathwidth for graphs vertex-coverable by few shortest paths and algorithms for quasi-shortest path covers
We study how covering a graph by a small number of shortest or approximately shortest paths/walks constrains its treewidth and pathwidth.LLM2Cert: LLM Synthesis of Certificates with SMT/Lean Verification
Lyapunov functions and barrier certificates give state-space guarantees of stability and safety, but synthesizing and verifying them---especially for nonlinear, high-dimensional systems---remains an…Dynamics of the Liquidity Profile in Concentrated Liquidity Provision: An SPIDE Approach
We model the dynamics of the liquidity profile in Uniswap~V3 and other concentrated-liquidity automated market makers with a stochastic partial integro-differential equation (SPIDE).BCC optimality for the three-dimensional Coulomb lattice energy
We prove that the body-centered cubic lattice uniquely minimizes the renormalized Coulomb energy among three-dimensional Bravais lattices of fixed covolume, up to orthogonal transformations.A Bayesian machine learning approach to direct and spillover effects in cluster-randomized trials with noncompliance
We develop a Bayesian machine learning approach for estimating direct and spillover effects in cluster-randomized trials with treatment noncompliance.Beyond Scalar Leaderboards: Adaptive Sampling for Pareto Frontier Identification in Multi-Objective LLM Arenas
The evaluation of large language models (LLMs) is inherently multi-objective, as different models may offer different tradeoffs across performance dimensions such as math, code, and writing.Soliton Profiles in 1D: Enhanced Neural-Network Solvers and Constrained Deep Ritz Methods
We develop and compare enhanced neural-network solvers for one-dimensional ground-state profiles, which are solutions of $$ Q^{\prime\prime}=bQ-γQ^{p},\quad Q>0,\quad p>1, \quad b, γ>0, $$ the…On the Accuracy of Approximate Leave-One-Out with Blockwise Nonsmooth Regularization in High-dimensions
Approximate leave-one-out (ALO) refers to methods that provide computationally efficient and statistically accurate approximations to leave-one-out risk estimation.Active Pilot Sampling for Multi-fidelity Estimation in Stochastic Optimization
Stochastic optimization with high-fidelity nonlinear models is often computationally prohibitive because each objective evaluation requires sampling-based uncertainty quantification inside an outer…Bayesian Neural Networks via Dirichlet Process Priors on the Data-Generating Distribution for Randomized Value Functions in Deep Reinforcement Learning
Bayesian neural networks (BNNs) offer a principled framework for quantifying epistemic uncertainty.Divide-and-Conquer Neural Bayes Estimation for Spatial and Spatiotemporal Point Process Models
Spatial and spatiotemporal point process models are widely used to analyze event data across many scientific disciplines, but likelihood-based Bayesian inference is often computationally prohibitive…Minimum-Time Escape from the Conic Field of View of a Reeds-Shepp Pursuer: Classification and Game Value for a Slower or Equally Fast Evader
We study minimum-time escape of an omnidirectional evader from the forward conic field of view of a Reeds-Shepp pursuer with minimum turning radius $r$.
Get the few papers worth your time in Mathematics & Statistics every morning.
Choose your topics and ReadSearch reads the new papers in them each day, then brings you the few worth reading, each with its key finding in a sentence.