惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

The GitHub Blog
The GitHub Blog
博客园 - 三生石上(FineUI控件)
V
V2EX
博客园 - 司徒正美
小众软件
小众软件
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
T
Tailwind CSS Blog
Last Week in AI
Last Week in AI
雷峰网
雷峰网
月光博客
月光博客
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Apple Machine Learning Research
Apple Machine Learning Research
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
S
SegmentFault 最新的问题
美团技术团队
Hugging Face - Blog
Hugging Face - Blog
WordPress大学
WordPress大学
宝玉的分享
宝玉的分享
爱范儿
爱范儿
博客园 - 聂微东
量子位
J
Java Code Geeks
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Vercel News
Vercel News

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for Primes in Short Intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Small-ball estimates for random walks on groups
Tom Hutchcroft · 2024-06-25 · via math.PR updates on arXiv.org

We prove a new inequality bounding the probability that the random walk on a group has small total displacement in terms of the spectral and isoperimetric profiles of the group. This inequality implies that if the random walk on the group is diffusive then Cheeger's inequality is sharp in the sense that the isoperimetric profile $Φ$ and spectral profile $Λ$ of the group are related by $Λ\simeq Φ^2$. Our inequality also yields substantial progress on a conjecture of Lyons, Peres, Sun, and Zheng (2017) stating that for any transient random walk on an infinite, finitely generated group, the expected occupation time of the ball of radius $r$ is $O(r^2)$: We prove that this conjecture holds for every group of superpolynomial growth whose spectral profile is slowly varying, which we conjecture is always the case. For groups of exponential or stretched-exponential growth satisfying a further mild regularity assumption on their spectral profile, our method yields the strong quantitative small-ball estimate \[-\log \mathbb{P}\bigl(d(X_0,X_n) \leq \varepsilon n^{1/2}\bigr) \succeq \frac{1}{\varepsilon^2} \wedge (-\log \mathbb{P}(X_n=X_0)),\] which is sharp for the lamplighter group. Finally, we prove that the regularity assumptions needed to apply the strongest versions our results are satisfied for several classical examples where the spectral profile is not known explicitly, including the first Grigorchuk group and Thompson's group $F$.