arXiv Statistics ML

Simultaneous Pointwise Majorization for Mixed Tail Processes with Applications in Gaussian Chaos and Ergodic Diffusions

The paper introduces a new simultaneous pointwise majorization framework for Banach‑valued stochastic processes that possess finite‑metric mixed‑tail increments. By assuming an anchored process satisfies a tail bound involving multiple pseudo‑metrics and orders, the authors derive a high‑probability envelope that holds uniformly over the index set, with terms expressed through integrals of log‑covering numbers and distance functions. This result generalizes single‑metric sub‑Weibull bounds and, in the Gaussian case, improves existing pointwise upper bounds by removing extraneous logarithmic factors.

arXiv Machine Learning
Jul 21

Scaling Limits of Constant-Stepsize SGD at Flat Minima

arXiv:2607. 16384v1 Announce Type: new Abstract: For stochastic gradient descent (SGD) with a constant stepsize $\alpha$, the invariant law of the iterates, centered at a minimizer, describes the behavior of the algorithm over long time horizons.

By Jingyi Zhang, Cheng Mao, Debankur Mukherjee
arXiv Statistics ML
Oct 16

Poincar\'e's Inequality on Sliding-Window Occupation Count Kernels and the Optimal Normalized Spectral-Gap Constant

The paper studies the spectral gap of a projected count kernel τP_n derived from an irreducible reversible Markov kernel P on an m‑state space. It establishes bounds on the optimal normalized comparison constant c_m^* = inf nGap( τP_n)/Gap(P), showing 1/(1080m) ≤ c_m^* ≤ q_{m-2} where q_{r+1}=q_r(1-q_r) and q_0=1/4. The authors prove that q_{m-2}=(m+log m+O(1))^{-1}, implying c_m^*=Θ(m^{-1}) and that the reciprocal optimal comparison cost C_m^{opt} grows linearly with m; exact values are given for the two‑state case.

By Yanjin Xiang, Zhihua Zhang
arXiv Machine Learning
Sep 14

Poisson-Corrector Complexity Bounds for Moreau--Yosida Unadjusted Langevin Sampling

arXiv:2609. 12594v1 Announce Type: new Abstract: We study the classical Moreau--Yosida unadjusted Langevin algorithm (MYULA) for $\pi(\,\mathrm{d} x)\propto e^{-f(x)-g(x)}\,\mathrm{d} x$, where $f\in C^2(\mathbb{R}^d)$ is $m$-strongly convex with $L_f$-Lipschitz gradient and $g:\mathbb{R}^d\to\mathbb{R}$ is convex and globally $G$-Lipschitz.

By Yuchen Xin, Zhihua Zhang