arXiv subjects

Guangya Cai

Publications and source records attributed to Guangya Cai.

5 recordsLinked to original sources

arXiv2026

Finding a Fair Scoring Function for Top-$k$ Selection: From Hardness to Practice

Selecting a subset of the $k$ "best" items from a dataset of $n$ items, based on a scoring function, is a key task in decision-making. Given the rise of automated decision-making software, it is important that the outcome of this process, called top-$k$ selection, is fair. Here we consider the problem of identifying a fair linear scoring function for top-$k$ selection. The function computes a score for each item as a weighted sum of its (numerical) attribute values, and must ensure that the selected subset includes adequate representation of a minority or historically disadvantaged group. Existing algorithms do not scale efficiently, particularly in higher dimensions. Our hardness analysis shows that in more than two dimensions, no algorithm is likely to achieve good scalability with respect to dataset size, and the computational complexity is likely to increase rapidly with dimensionality. However, the hardness results also provide key insights guiding algorithm design, leading to our two-pronged solution: (1) For small values of $k$, our hardness analysis reveals a gap in the hardness barrier. By addressing various engineering challenges, including achieving efficient parallelism, we turn this potential of efficiency into an optimized algorithm delivering substantial practical performance gains. (2) For large values of $k$, where the hardness is robust, we employ a practically efficient algorithm which, despite being theoretically worse, achieves superior real-world performance. Experimental evaluations on real-world datasets then explore scenarios where worst-case behavior does not manifest, identifying areas critical to practical performance. Our solution achieves speed-ups of up to several orders of magnitude compared to SOTA, an efficiency made possible through a tight integration of hardness analysis, algorithm design, practical engineering, and empirical evaluation.

cs.DB↗

arXiv2026

Generalizing Fair Top-$k$ Selection: An Integrative Approach

Fair top-$k$ selection, which ensures appropriate proportional representation of members from minority or historically disadvantaged groups among the top-$k$ selected candidates, has drawn significant attention. We study the problem of finding a fair (linear) scoring function with multiple protected groups while also minimizing the disparity from a reference scoring function. This generalizes the prior setup, which was restricted to the single-group setting without disparity minimization. Previous studies imply that the number of protected groups may have a limited impact on the runtime efficiency. However, driven by the need for experimental exploration, we find that this implication overlooks a critical issue that may affect the fairness of the outcome. Once this issue is properly considered, our hardness analysis shows that the problem may become computationally intractable even for a two-dimensional dataset and small values of $k$. However, our analysis also reveals a gap in the hardness barrier, enabling us to recover the efficiency for the case of small $k$ when the number of protected groups is sufficiently small. Furthermore, beyond measuring disparity as the "distance" between the fair and the reference scoring functions, we introduce an alternative disparity measure$\unicode{x2014}$utility loss$\unicode{x2014}$that may yield a more stable scoring function under small weight perturbations. Through careful engineering trade-offs that balance implementation complexity, robustness, and performance, our augmented two-pronged solution demonstrates strong empirical performance on real-world datasets, with experimental observations also informing algorithm design and implementation decisions.

cs.DS↗

arXiv2021

Testing Boolean Functions Properties

The goal in the area of functions property testing is to determine whether a given black-box Boolean function has a particular given property or is $\varepsilon$-far from having that property. We investigate here several types of properties testing for Boolean functions (identity, correlations and balancedness) using the Deutsch-Jozsa algorithm (for the Deutsch-Jozsa (D-J) problem) and also the amplitude amplification technique. At first, we study here a particular testing problem: namely whether a given Boolean function $f$, of $n$ variables, is identical with a given function $g$ or is $\varepsilon$-far from $g$, where $\varepsilon$ is the parameter. We present a one-sided error quantum algorithm to deal with this problem that has the query complexity $O(\frac{1}{\sqrt{\varepsilon}})$. Moreover, we show that our quantum algorithm is optimal. Afterwards we show that the classical randomized query complexity of this problem is $Θ(\frac{1}{\varepsilon})$. Secondly, we consider the D-J problem from the perspective of functional correlations and let $C(f,g)$ denote the correlation of $f$ and $g$. We propose an exact quantum algorithm for making distinction between $|C(f,g)|=\varepsilon$ and $|C(f,g)|=1$ using six queries, while the classical deterministic query complexity for this problem is $Θ(2^{n})$ queries. Finally, we propose a one-sided error quantum query algorithm for testing whether one Boolean function is balanced versus $\varepsilon$-far balanced using $O(\frac{1}{\varepsilon})$ queries. We also prove here that our quantum algorithm for balancedness testing is optimal. At the same time, for this balancedness testing problem we present a classical randomized algorithm with query complexity of $O(1/\varepsilon^{2})$. Also this randomized algorithm is optimal. Besides, we link the problems considered here together and generalize them to the general case.

quant-ph↗

arXiv2021

Quantum and Classical Query Complexities for Generalized Simon's Problem

Simon's problem is an essential example demonstrating the faster speed of quantum computers than classical computers for solving some problems. The optimal separation between exact quantum and classical query complexities for Simon's problem has been proved by Cai $\&$ Qiu. Generalized Simon's problem can be described as follows. Given a function $f:{\{0, 1\}}^n \to {\{0, 1\}}^m$, with the property that there is some unknown hidden subgroup $S$ such that $f(x)=f(y)$ iff $x \oplus y\in S$, for any $x, y\in {\{0, 1\}}^n$, where $|S|=2^k$ for some $0\leq k\leq n$. The goal is to find $S$. For the case of $k=1$, it is Simon's problem. In this paper, we propose an exact quantum algorithm with $O(n-k)$ queries and an non-adaptive deterministic classical algorithm with $O(k\sqrt{2^{n-k}})$ queries for solving the generalized Simon's problem. Also, we prove that their lower bounds are $Ω(n-k)$ and $Ω(\sqrt{k2^{n-k}})$, respectively. Therefore, we obtain a tight exact quantum query complexity $Θ(n-k)$ and an almost tight non-adaptive classical deterministic query complexities $Ω(\sqrt{k2^{n-k}}) \sim O(k\sqrt{2^{n-k}})$ for this problem.

quant-ph↗

arXiv2017

Optimal Separation in Exact Query Complexities for Simon's Problem

Simon's problem is one of the most important problems demonstrating the power of quantum computers, which achieves a large separation between quantum and classical query complexities. However, Simon's discussion on his problem was limited to bounded-error setting, which means his algorithm can not always get the correct answer. Exact quantum algorithms for Simon's problem have also been proposed, which deterministically solve the problem with O(n) queries. Also the quantum lower bound Ω(n) for Simon's problem is known. Although these algorithms are either complicated or specialized, their results give an O(n) versus Ω(\sqrt{2^{n}}) separation in exact query complexities for Simon's problem (Ω(\sqrt{2^{n}}) is the lower bound for classical probabilistic algorithms), but it has not been proved whether this separation is optimal. In this paper, we propose another exact quantum algorithm for solving Simon's problem with O(n) queries, which is simple, concrete and does not rely on special query oracles. Our algorithm combines Simon's algorithm with the quantum amplitude amplification technique to ensure its determinism. In particular, we show that Simon's problem can be solved by a classical deterministic algorithm with O(\sqrt{2^{n}}) queries (as we are aware, there were no classical deterministic algorithms for solving Simon's problem with O(\sqrt{2^{n}}) queries). Combining some previous results, we obtain the optimal separation in exact query complexities for Simon's problem: Θ({n}) versus Θ({\sqrt{2^{n}}}).

quant-ph↗