Xiaoxi Guo

AAAI Conference 2025 Conference Paper

An Evaluation Framework for Product Images Background Inpainting Based on Human Feedback and Product Consistency

• Yuqi Liang
• Jun Luo
• Xiaoxi Guo
• Jianqi Bi

In product advertising applications, the automated inpainting of backgrounds utilizing AI techniques in product images has emerged as a significant task. However, the techniques still suffer from issues such as inappropriate background and inconsistent product in generated product images, and existing approaches for evaluating the quality of generated product images are mostly inconsistent with human feedback causing the evaluation for this task to depend on manual annotation. To relieve the issues above, this paper proposes Human Feedback and Product Consistency (HFPC), which can automatically assess the generated product images based on two modules. Firstly, to solve inappropriate backgrounds, human feedback on 44,000 automated inpainting product images is collected to train a reward model based on multi-modal features extracted from BLIP and comparative learning. Secondly, to filter generated product images containing inconsistent products, a fine-tuned segmentation model is employed to segment the product of the original and generated product images and then compare the differences between the above two. Extensive experiments have demonstrated that HFPC can effectively evaluate the quality of generated product images and significantly reduce the expense of manual annotation. Moreover, HFPC achieves state-of-the-art (96.4% in precision) in comparison to other open-source visual-quality-assessment models.

JAIR Journal 2023 Journal Article

Favoring Eagerness for Remaining Items: Designing Efficient, Fair, and Strategyproof Mechanisms

• Xiaoxi Guo
• Sujoy Sikdar
• Lirong Xia
• Yongzhi Cao
• Hanpin Wang

In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner. In practice, first-choice maximality, i.e., assigning a maximal number of agents their top items, is often identified as an important efficiency criterion and measure of agents' satisfaction. In this paper, we propose a natural and intuitive efficiency property, favoring-eagerness-for-remaining-items (FERI), which requires that each item is allocated to an agent who ranks it highest among remaining items, thereby implying first-choice maximality. Using FERI as a heuristic, we design mechanisms that satisfy ex-post or ex-ante variants of FERI together with combinations of other desirable properties of efficiency (Pareto-efficiency), fairness (strong equal treatment of equals and sd-weak-envy-freeness), and strategyproofness (sd-weak-strategyproofness). We also explore the limits of FERI mechanisms in providing stronger efficiency, fairness, or strategyproofness guarantees through impossibility results.

IJCAI Conference 2023 Conference Paper

First-Choice Maximality Meets Ex-ante and Ex-post Fairness

• Xiaoxi Guo
• Sujoy Sikdar
• Lirong Xia
• Yongzhi Cao
• Hanpin Wang

For the assignment problem where multiple indivisible items are allocated to a group of agents given their ordinal preferences, we design randomized mechanisms that satisfy first-choice maximality (FCM), i. e. , maximizing the number of agents assigned their first choices, together with Pareto efficiency (PE). Our mechanisms also provide guarantees of ex-ante and ex-post fairness. The generalized eager Boston mechanism is ex-ante envy-free, and ex-post envy-free up to one item (EF1). The generalized probabilistic Boston mechanism is also ex-post EF1, and satisfies ex-ante efficiency instead of fairness. We also show that no strategyproof mechanism satisfies ex-post PE, EF1, and FCM simultaneously. In doing so, we expand the frontiers of simultaneously providing efficiency and both ex-ante and ex-post fairness guarantees for the assignment problem.

AIJ Journal 2023 Journal Article

Multi resource allocation with partial preferences

• Haibin Wang
• Sujoy Sikdar
• Xiaoxi Guo
• Lirong Xia
• Yongzhi Cao
• Hanpin Wang

AAMAS Conference 2022 Conference Paper

Designing Efficient and Fair Mechanisms for Multi-Type Resource Allocation

• Xiaoxi Guo
• Sujoy Sikdar
• Haibin Wang
• Lirong Xia
• Yongzhi Cao
• Hanpin Wang

In the multi-type resource allocation problem (MTRA), there are 𝑑 ≥ 2 types of items, and 𝑛 agents who each demand one unit of items of each type and have strict linear preferences over bundles consisting of one item of each type. For MTRAs with indivisible items, we first present an impossibility result that no mechanism can satisfy both sd-efficiency and sd-envy-freeness. We show that this impossibility result is circumvented under the natural assumption of lexicographic preferences by providing lexicographic probabilistic serial (LexiPS) as an extension of the probabilistic serial (PS) mechanism. We also prove that LexiPS satisfies sd-efficiency and sd-envy-freeness. Moreover, LexiPS satisfies sd-weak-strategy proofness when agents are not allowed to misreport their importance orders. The multi-type probabilistic serial cannot deal with indivisible items, but provides a stronger efficiency guarantee under the unrestricted domain of strict linear preferences for divisible items, while also retaining desirable fairness guarantees.

JAAMAS Journal 2021 Journal Article

Probabilistic serial mechanism for multi-type resource allocation

• Xiaoxi Guo
• Sujoy Sikdar
• Hanpin Wang

Abstract In multi-type resource allocation (MTRA) problems, there are \(d\ge 2\) types of items, and n agents who each demand one unit of items of each type and have strict linear preferences over bundles consisting of one item of each type. For MTRAs with indivisible items, our first result is an impossibility theorem that is in direct contrast to the single type ( \(d=1\) ) setting: no mechanism, the output of which is always decomposable into a probability distribution over discrete assignments (where no item is split between agents), can satisfy both sd-efficiency and sd-envy-freeness. We show that this impossibility result is circumvented under the natural assumption of lexicographic preferences. We provide lexicographic probabilistic serial (LexiPS) as an extension of the probabilistic serial (PS) mechanism for MTRAs with lexicographic preferences, and prove that LexiPS satisfies sd-efficiency and sd-envy-freeness, retaining the desirable properties of PS. Moreover, LexiPS satisfies sd-weak-strategyproofness when agents are not allowed to misreport their importance orders. For MTRAs with divisible items, we show that the existing multi-type probabilistic serial (MPS) mechanism satisfies the stronger efficiency notion of lexi-efficiency, and is sd-envy-free under strict linear preferences and sd-weak-strategyproof under lexicographic preferences. We also prove that MPS can be characterized both by leximin-optimality and by item-wise ordinal fairness, and the family of eating algorithms which MPS belongs to can be characterized by lexi-efficiency.

AAMAS Conference 2021 Conference Paper

Sequential Mechanisms for Multi-type Resource Allocation

• Sujoy Sikdar
• Xiaoxi Guo
• Haibin Wang
• Lirong Xia
• Yongzhi Cao

Several resource allocation problems involve multiple types of resources, with a different agency being responsible for “locally” allocating the resources of each type, while a central planner wishes to provide a guarantee on the properties of the final allocation given agents’ preferences. We study the relationship between properties of the local mechanisms, each responsible for assigning all of the resources of a designated type, and the properties of a sequential mechanism which is composed of these local mechanisms, one for each type, applied sequentially, under lexicographic preferences, a well studied model of preferences over multiple types of resources in artificial intelligence and economics. We show that when preferences are 𝑂-legal, meaning that agents share a common importance order on the types, sequential mechanisms satisfy the desirable properties of anonymity, neutrality, non-bossiness, or Pareto-optimality if and only if every local mechanism also satisfies the same property, and they are applied sequentially according to the order 𝑂. Our main results are that under 𝑂-legal lexicographic preferences, every mechanism satisfying strategyproofness and a combination of these properties must be a sequential composition of local mechanisms that are also strategyproof, and satisfy the same combinations of properties.

AAAI Conference 2020 Conference Paper

Multi-Type Resource Allocation with Partial Preferences

• Haibin Wang
• Sujoy Sikdar
• Xiaoxi Guo
• Lirong Xia
• Yongzhi Cao
• Hanpin Wang

We propose multi-type probabilistic serial (MPS) and multitype random priority (MRP) as extensions of the well-known PS and RP mechanisms to the multi-type resource allocation problems (MTRAs) with partial preferences. In our setting, there are multiple types of divisible items, and a group of agents who have partial order preferences over bundles consisting of one item of each type. We show that for the unrestricted domain of partial order preferences, no mechanism satisfies both sd-efficiency and sd-envy-freeness. Notwithstanding this impossibility result, our main message is positive: When agents’ preferences are represented by acyclic CPnets, MPS satisfies sd-efficiency, sd-envy-freeness, ordinal fairness, and upper invariance, while MRP satisfies ex-postefficiency, sd-strategyproofness, and upper invariance, recovering the properties of PS and RP. Besides, we propose a hybrid mechanism, multi-type general dictatorship (MGD), combining the ideas of MPS and MRP, which satisfies sd-efficiency, equal treatment of equals and decomposability under the unrestricted domain of partial order preferences.