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Ziqi Yu

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6 papers

2 author rows

I&C Journal 2026 Journal Article

Fair division with prioritized agents

Xiaolin Bu
Zihao Li
Shengxin Liu
Jiaxin Song
Biaoshuai Tao
Ziqi Yu

We study the fair division of indivisible items. Since an envy-free allocation may not exist, a standard relaxation is envy-freeness up to one item (EF1), where any envy can be eliminated by removing a single item from the envied agent's bundle. In many applications, however, it is desirable to designate a subset of prioritized agents for whom strict envy-freeness toward the remaining agents must be guaranteed, while the overall allocation remains EF1. Such agents may correspond to those who were envious in a previous EF1 allocation or to members of underrepresented groups. Motivated by this, we propose a new fairness notion named envy-freeness with prioritized agents EFprior, and study the existence and the algorithmic aspects of computing an EFprior allocation. For additive valuations, the simple round-robin algorithm suffices to compute an EFprior allocation. In this paper, we mainly focus on general valuations. In particular, we present a polynomial-time algorithm that computes an EFprior allocation with most of the items allocated. When all the items need to be allocated, we also present polynomial-time algorithms for several well-motivated special cases. We finally extend the setting to a general prioritized ordering case, where we are given a full ordering of agents and each agent with a higher priority cannot envy an agent with a lower priority. We propose a generalized fairness notion named envy-freeness under rank r EF r and present a polynomial-time algorithm with most of the items allocated.

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UAI Conference 2025 Conference Paper

How Likely Are Two Voting Rules Different?

Ziqi Yu
Lirong Xia
Qishen Han
Chengkai Zhang

We characterize the maximum likelihood that two voting rule outcomes are different and that the winner of one voting rule is the loser of another (implying that they are {\em drastically different}) on positional scoring rules, Condorcet winner/loser, Copeland, Ranked Pairs, and STV (Single Transferable Vote) under any fixed number of alternatives. The most famous problem in this scope is strong Borda’s paradox, in which the winner of the plurality rule is the Condorcet loser. Under mild assumptions, we show that the maximum likelihood that different rules are drastically different is $\Theta(1)$ except for a few special cases, demonstrating the difference between these rules. We also prove that two scoring rules with linear independent scoring vectors have different winners with probability $\Theta(1)$, no matter how similar they are. Our analysis adopts the {\em smoothed social choice framework} \cite{xia2020smoothed} and can be applied to a variety of statistical models, including the standard impartial culture (IC).

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TCS Journal 2025 Journal Article

On the existence of EFX (and Pareto-optimal) allocations for binary chores

Biaoshuai Tao
Xiaowei Wu
Ziqi Yu
Shengwei Zhou

We study the problem of allocating a set of indivisible chores among agents while each chore has a binary marginal. We focus on the fairness criteria of envy-freeness up to any item (EFX) and investigate the existence of EFX allocations. We show that when agents have additive binary cost functions, EFX and Pareto-optimal (PO) allocations can be computed in polynomial time. We further consider more general cost functions: cancelable, submodular, and general (with binary marginal). For binary cancelable chores, we show that EFX allocations can be computed in polynomial time, but EFX is incompatible with PO. For submodular and general functions (with binary marginals), we propose algorithms for the computation of approximate EFX and envy-free (EF) (partial) allocations with at most n − 1 unallocated items respectively.

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NeurIPS Conference 2025 Conference Paper

Unifying Proportional Fairness in Centroid and Non-Centroid Clustering

Benjamin Cookson
Nisarg Shah
Ziqi Yu

Proportional fairness criteria inspired by democratic ideals of proportional representation have received growing attention in the clustering literature. Prior work has investigated them in two separate paradigms. Chen et al. [ICML 2019] study centroid clustering, in which each data point's loss is determined by its distance to a representative point (centroid) chosen in its cluster. Caragiannis et al. [NeurIPS 2024] study non-centroid clustering, in which each data point's loss is determined by its maximum distance to any other data point in its cluster. We generalize both paradigms to introduce semi-centroid clustering, in which each data point's loss is a combination of its centroid and non-centroid losses, and study two proportional fairness criteria---the core and, its relaxation, fully justified representation (FJR). Our main result is a novel algorithm which achieves a constant approximation to the core, in polynomial time, even when the distance metrics used for centroid and non-centroid loss measurements are different. We also derive improved results for more restricted loss functions and the weaker FJR criterion, and establish lower bounds in each case.

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EAAI Journal 2024 Journal Article

Coal allocation optimization based on a hybrid residual prediction model with an improved genetic algorithm

Ming Liu
Ziqi Yu
Boran Li
Qingjie Wang
Huawei Ren
Dong Xu

The objective of the coal blending optimization problem is to find an optimal coal blending in the feasible domain such that the blended coal meets the quality requirements at the end of the coking process and the cost of coal blending is minimized. This paper proposes a hybrid residual prediction model and an improved genetic algorithm to solve this problem and predict coke quality. For this purpose, a hybrid residual prediction model is used to predict coke quality. The model first uses a random forest feature extraction method to reduce the dimensionality of the data, and then trains several prediction models such as eXtreme Gradient Boosting (XGBoost), Adaboost and Light Gradient-Boosting Machine (lightGBM) for different coke indicators an improved genetic algorithm based on the adaptive weighted genetic algorithm (awGA) and another improved genetic algorithm based on a priori knowledge and adaptive random initialization method were designed and implemented to solve the optimization problem under strict constraints (P-awGA). The experimental results show that using the hybrid residual prediction model and the improved genetic algorithm can accurately predict the coke quality and use less time to obtain a lower-cost coal blending solution.

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JBHI Journal 2023 Journal Article

TW-Net: Transformer Weighted Network for Neonatal Brain MRI Segmentation

Shengjie Zhang
Bohan Ren
Ziqi Yu
Haibo Yang
Xiaoyang Han
Xiang Chen
Yuan Zhou
Dinggang Shen

Accurate neonatal brain MRI segmentation is valuable for investigating brain growth patterns and tracking the progression of neurodevelopmental disorders. However, it is a challenging task to use intensity-based methods to segment neonatal brain structures because of small contrast differences between brain regions caused by the inherent myelination process. Although convolutional neural networks offer the potential to segment brain structures in an intensity-independent manner, they suffer from lack of in-plane long-range dependency which is essential for the segmentation. To solve this problem, we propose a novel Transformer-Weighted network (TW-Net) to incorporate in-plane long-range dependency information. TW-Net employs a conventional encoder-decoder architecture with a Transformer module in the middle. The Transformer module uses a rotate-and-flip layer to better calculate the similarity between two patches in a slice to leverage similar patterns of geometrical and texture features within brain structures. In addition, a deep supervision module and squeeze-and-excitation blocks are introduced to incorporate boundary information of brain structures. Compared with state-of-the-art deep learning algorithms, TW-Net outperforms these methods for multiple-label tasks in 2D and 2. 5D configurations on two independent public datasets, demonstrating that TW-Net is a promising method for neonatal brain MRI segmentation.

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