arXiv subjects

Rui-Ray Zhang

Publications and source records attributed to Rui-Ray Zhang.

8 recordsLinked to original sources

arXiv2025

Finding a Nash equilibrium of a random win-lose game in expected polynomial time

A long-standing open problem in algorithmic game theory asks whether or not there is a polynomial time algorithm to compute a Nash equilibrium in a random bimatrix game. We study random win-lose games, where the entries of the $n\times n$ payoff matrices are independent and identically distributed (i.i.d.) Bernoulli random variables with parameter $p=p(n)$. We prove that, for nearly all values of the parameter $p=p(n)$, there is an expected polynomial-time algorithm to find a Nash equilibrium in a random win-lose game. More precisely, if $p\sim cn^{-a}$ for some parameters $a,c\ge 0$, then there is an expected polynomial-time algorithm whenever $a\not\in \{1/2, 1\}$. In addition, if $a = 1/2$ there is an efficient algorithm if either $c \le e^{-52} 2^{-8} $ or $c\ge 0.977$. If $a=1$, then there is an expected polynomial-time algorithm if either $c\le 0.3849$ or $c\ge \log^9 n$.

cs.GT↗

arXiv2025

Multivariate Poisson approximation of joint subgraph counts in random graphs via size-biased couplings

Using Chen-Stein method in combination with size-biased couplings, we obtain the multivariate Poisson approximation in terms of the Wasserstein distance. As applications, we study the multivariate Poisson approximation of the distribution of joint subgraph counts in an Erd\H{o}s-R\'enyi random graph and the multivariate hypergeometric distribution giving explicit convergence rates.

math.PR↗

arXiv2024

Correlation between residual entropy and spanning tree entropy of ice-type models on graphs

The logarithm of the number of Eulerian orientations, normalised by the number of vertices, is known as the residual entropy in studies of ice-type models on graphs. The spanning tree entropy depends similarly on the number of spanning trees. We demonstrate and investigate a remarkably strong, though non-deterministic, correlation between these two entropies. This leads us to propose a new heuristic estimate for the residual entropy of regular graphs that performs much better than previous heuristics. We also study the expansion properties and residual entropy of random graphs with given degrees.

math.CO↗

arXiv2023

Cumulant expansion for counting Eulerian orientations

An Eulerian orientation is an orientation of the edges of a graph such that every vertex is balanced: its in-degree equals its out-degree. Counting Eulerian orientations corresponds to the crucial partition function in so-called ``ice-type models'' in statistical physics and is known to be hard for general graphs. For all graphs with good expansion properties and degrees larger than $\log^{8} n$, we derive an asymptotic expansion for this count that approximates it to precision $O(n^{-c})$ for arbitrary large $c$, where $n$ is the number of vertices. The proof relies on a new tail bound for the cumulant expansion of the Laplace transform, which is of independent interest.

math.CO↗

arXiv2022

Asymptotic linearity of binomial random hypergraphs via cluster expansion under graph-dependence

Let integer $n \ge 3$ and integer $r = r(n) \ge 3$. Define the binomial random $r$-uniform hypergraph $H_r(n, p)$ to be the $r$-uniform graph on the vertex set $[n]$ such that each $r$-set is an edge independently with probability $p$. A hypergraph is linear if every pair of hyperedges intersects in at most one vertex. We study the probability of linearity of random hypergraphs $H_r(n, p)$ via cluster expansion and give more precise asymptotics of the probability in question, improving the asymptotic probability of linearity obtained by McKay and Tian, in particular, when $r=3$ and $p = o(n^{-7/5})$.

math.CO↗

arXiv2022

Generalization bounds for learning under graph-dependence: A survey

Traditional statistical learning theory relies on the assumption that data are identically and independently distributed (i.i.d.). However, this assumption often does not hold in many real-life applications. In this survey, we explore learning scenarios where examples are dependent and their dependence relationship is described by a dependency graph, a commonly utilized model in probability and combinatorics. We collect various graph-dependent concentration bounds, which are then used to derive Rademacher complexity and stability generalization bounds for learning from graph-dependent data. We illustrate this paradigm through practical learning tasks and provide some research directions for future work. To our knowledge, this survey is the first of this kind on this subject.

cs.LG↗

arXiv2021

When Janson meets McDiarmid: Bounded difference inequalities under graph-dependence

We establish concentration inequalities for Lipschitz functions of dependent random variables, whose dependencies are specified by forests. We also give concentration results for decomposable functions, improving Janson's Hoeffding-type inequality for the summation of graph-dependent bounded variables. These results extend McDiarmid's bounded difference inequality to the dependent cases.

math.PR↗

arXiv2021

Extremal independence in discrete random systems

Let $\mathbf{X}(n) \in \mathbb{R}^d$ be a sequence of random vectors, where $n\in\mathbb{N}$ and $d = d(n)$. Under certain weakly dependence conditions, we prove that the distribution of the maximal component of $\mathbf{X}$ and the distribution of the maximum of their independent copies are asymptotically equivalent. Our result on extremal independence relies on new lower and upper bounds for the probability that none of a given finite set of events occurs. As applications, we obtain the distribution of various extremal characteristics of random discrete structures such as maximum codegree in binomial random hypergraphs and the maximum number of cliques sharing a given vertex in binomial random graphs. We also generalise Berman-type conditions for a sequence of Gaussian random vectors to possess the extremal independence property.

math.PR↗