Hoeffding's lemma for Markov Chains and its applications to statistical learning

TL;DR

This paper extends Hoeffding's lemma to general-state-space, non-reversible Markov chains, providing a sub-Gaussian bound for sums of bounded functions and demonstrating its applications in statistics and machine learning.

Contribution

It introduces a Hoeffding-type inequality for a broad class of Markov chains, including non-reversible and time-dependent cases, with explicit spectral gap dependence.

Findings

Derived a sub-Gaussian bound with spectral gap dependence

Unified Hoeffding's inequality for non-reversible Markov chains

Applied results to six problems in statistics and machine learning

Abstract

We extend Hoeffding's lemma to general-state-space and not necessarily reversible Markov chains. Let $\{X_i\}_{i \ge 1}$ be a stationary Markov chain with invariant measure $\pi$ and absolute spectral gap $1-\lambda$, where $\lambda$ is defined as the operator norm of the transition kernel acting on mean zero and square-integrable functions with respect to $\pi$. Then, for any bounded functions $f_i: x \mapsto [a_i,b_i]$, the sum of $f_i(X_i)$ is sub-Gaussian with variance proxy $\frac{1+\lambda}{1-\lambda} \cdot \sum_i \frac{(b_i-a_i)^2}{4}$. This result differs from the classical Hoeffding's lemma by a multiplicative coefficient of $(1+\lambda)/(1-\lambda)$, and simplifies to the latter when $\lambda = 0$. The counterpart of Hoeffding's inequality for Markov chains immediately follows. Our results assume none of countable state space, reversibility and time-homogeneity of Markov chains and cover time-dependent functions with various ranges. We illustrate the utility of these results by applying them to six problems in statistics and machine learning.