arXiv subjects

Yanzhi Li

Publications and source records attributed to Yanzhi Li.

4 recordsLinked to original sources

arXiv2026

Diversity-Fair Online Selection

Online selection problems arise in applications such as crowdsourcing and recruitment, where decision makers may seek representation across multiple, potentially overlapping demographic or skill dimensions. We study diversity-fair online selection under adversarial arrivals. A recruiter must immediately and irrevocably decide whether to accept each candidate while selecting at most $K$ candidates. Before arrivals begin, the recruiter observes aggregate marginal information: the total number of candidates contributing to each of the $d$ diversity dimensions. When the candidate pool is large, this information may be estimated from demographic statistics of the applicant population. We evaluate the expected utilities across dimensions using the generalized mean $M_p=(d^{-1}\sum_{k=1}^d U_k^p)^{1/p}, -\infty\le p\le 1,$ where $U_k$ denotes the expected utility of dimension $k$. We first study max-min fairness, corresponding to $p=-\infty$. We prove that no online policy can achieve a competitive ratio better than $O(1/\sqrt d)$ and develop a policy with a competitive ratio $1/[4(2+\sqrt2)\sqrt d]$, establishing the optimal dependence on $d$ up to a constant factor. Without exact marginal information, the optimal worst-case rate falls to $Θ(1/d)$, demonstrating the value of this information. We also extend the max-min analysis to nonbinary attributes and characterize the optimal dependence on their value range. Finally, we study generalized-mean objectives. For $0\le p\le1$, we establish an optimal competitive ratio of $Θ(1/\log d)$. For each fixed finite negative mean $p=-q$, where $q>0$, our policy achieves $d^{-q/(2q+1)}$ up to polylogarithmic factors, matching the exponent of the corresponding impossibility bound.

econ.TH↗

arXiv2026

AWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty

Millimeter-wave (mmWave) links are increasingly utilized in wireless x-haul transport to meet growing service demands. However, the inherent susceptibility of mmWave links to weather-related attenuation creates uncertainty about future network capacity which can significantly affect Quality of Service (QoS). This creates a critical challenge: how to make admission control decisions for slices with QoS requirements, balancing acceptance rewards against the risk of future QoS-violation penalties due to capacity uncertainty? To address this, we develop a proactive slice admission control framework that tightly integrates: (i) a predictor that leverages historical link measurements to forecast short-term attenuation and quantify uncertainty; and (ii) an admission control algorithm that incorporates both the predictions and uncertainties to maximize rewards and minimize QoS-violation penalties. We compare our framework against baseline, state-of-the-art, and idealized oracle algorithms using real-world mmWave x-haul data and residential traffic traces. Simulations suggest that our framework can achieve revenues that are 250% larger than baseline algorithms and 75% larger than state-of-the-art algorithms.

cs.NI↗

arXiv2025

Improved Bounds for Codes over Trees

Codes over trees were introduced recently to bridge graph theory and coding theory with diverse applications in computer science and beyond. A central challenge lies in determining the maximum number of labelled trees over $n$ nodes with pairwise distance at least $d$, denoted by $A(n,d)$, where the distance between any two labelled trees is the minimum number of edit edge operations in order to transform one tree to another. By various tools from graph theory and algebra, we show that when $n$ is large, $A(n,d)=O((Cn)^{n-d})$ for any $d\leq n-2$, and $A(n,d)=Ω((cn)^{n-d})$ for any $d$ linear with $n$, where constants $c\in(0,1)$ and $C\in [1/2,1)$ depending on $d$. Previously, only $A(n,d)=O(n^{n-d-1})$ for fixed $d$ and $A(n,d)=Ω(n^{n-2d})$ for $d\leq n/2$ were known, while the upper bound is improved for any $d$ and the lower bound is improved for $d\geq 2\sqrt{n}$. Further, for any fixed integer $k$, we prove the existence of codes of size $Ω(n^k)$ when $n-d=o(n)$, and give explicit constructions of codes which show $A(n,n-4)=Ω(n^2)$ and $A(n,n-13)=Ω(n^3)$.

math.CO↗

arXiv2019

Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs

Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap of recommended routes. The multi-taxicab recommendation problem is challenging and has been less studied. In this paper, we first formalize a collective mobile sequential recommendation problem based on a classic mathematical model, which characterizes time-varying influence among competing taxicabs. Next, we propose a new evaluation metric for a collection of taxicab routes aimed to minimize the sum of potential travel time. We then develop an efficient algorithm to calculate the metric and design a greedy recommendation method to approximate the solution. Finally, numerical experiments show the superiority of our methods. In trace-driven simulation, the set of routes recommended by our method significantly outperforms those obtained by conventional methods.

cs.DS↗