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

T
Tailwind CSS Blog
人人都是产品经理
人人都是产品经理
博客园 - 叶小钗
大猫的无限游戏
大猫的无限游戏
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
博客园 - 【当耐特】
The Cloudflare Blog
博客园 - 聂微东
博客园 - 司徒正美
量子位
博客园 - 三生石上(FineUI控件)
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
G
Google Developers Blog
Apple Machine Learning Research
Apple Machine Learning Research
罗磊的独立博客
酷 壳 – CoolShell
酷 壳 – CoolShell
Y
Y Combinator Blog
S
SegmentFault 最新的问题
T
The Blog of Author Tim Ferriss
P
Proofpoint News Feed
Google DeepMind News
Google DeepMind News
Blog — PlanetScale
Blog — PlanetScale
有赞技术团队
有赞技术团队
A
About on SuperTechFans

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
Introduction to Quantum Groups and Yang-Baxter Equation F...
Jeffrey Kuan · 2025-12-05 · via math.PR updates on arXiv.org

These are a set of lecture notes for a mini-course I gave at The University of Warwick from October 30th to November 1st, 2024. Recordings of the lectures are available on Oleg Zaboronski's webpage at https://warwick.ac.uk/fac/sci/maths/people/staff/oleg_zaboronski/jeffrey_kuan_visit/ . The main body of the notes covers the content of the lectures, and provides an introduction to Drinfel'd-Jimbo quantum groups and the Yang-Baxter equation, with a probabilist as the target audience. The appendix contains several topics, requested by colleagues during my visit to the United Kingdom, which all depend on the main set of notes. The notes begin by defining what it means for the asymmetric simple exclusion process (ASEP) to be integrable, in the sense of satisfying the Yang-Baxter equation. It then provides the algebraic background for the Yang-Baxter equation, by defining Drinfel'd-Jimbo groups as a quasi-triangular Hopf algebra. The algebraic background motivates generalizations of ASEP to stochastic vertex models and "fused" models. Each section corresponds to approximately an hour of lecture time. The appendix covers the F.R.T. construction, Hecke algebras, the matrix product ansatz, and orthogonal polynomial vertex weights. The topics in the appendix can be read independently of each other. Accessibility Statement: This PDF meets the technical standards of WCAG2.1AA, which complies with Ohio Administrative Policy IT-09 , Texas Administrative Code 206.70 and Title II of the Americans with Disabilities Act (effective April 24, 2026) . A webpage version of this PDF, typeset in MathML, is also available at https://go.osu.edu/QuantumKuan . To block web crawlers, the webpage is password protected. The password is TaySwift13. As an additional benefit, the webpage will have space for public comments and a list of updated errata, without the need to update the arXiv version.