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Han Su

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4 papers

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TMLR Journal 2026 Journal Article

Statistical Inference for Generative Model Comparison

Zijun Gao
Han Su
Yan Sun

Generative models have achieved remarkable success across a range of applications, yet their evaluation still lacks principled uncertainty quantification. In this paper, we develop a method for comparing how close different generative models are to the underlying distribution of test samples. Particularly, our approach employs the Kullback-Leibler (KL) divergence to measure the distance between a generative model and the unknown test distribution, as KL requires no tuning parameters such as the kernels used by RKHS-based distances. And the relative KL divergence is the only $f$-divergence that admits a crucial cancellation of the hard-to-estimate term to enable the faithful uncertainty quantification. Furthermore, we extend our method to comparing conditional generative models and leverage Edgeworth expansions to address limited-data settings. On simulated datasets with known ground truth, we show that our approach realizes effective coverage rates, and has higher power compared to kernel-based methods. When applied to generative models on image and text datasets, our procedure yields conclusions consistent with benchmark metrics but with statistical confidence. The source code to reproduce our experiments is available at https://github.com/sylydya/compare-generative-models.

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EAAI Journal 2024 Journal Article

An outranking approach for multi-attribute group decision-making with interval-valued hesitant fuzzy information

Feng Shen
Qinyuan Huang
Han Su
Zeshui Xu

Multi-Attribute Group Decision-Making (MAGDM) problems have become more common, with interval-valued hesitant fuzzy set (IVHFS) being found to be suitable for describing some complex fuzzy information. This paper first determined the additional relationships between generalized interval-valued hesitant fuzzy weighted averaging (GIVHFWA) operators and generalized interval-valued hesitant fuzzy weighted geometric (GIVHFWG) operators, and proposed mean and variance for a sequence of interval-valued hesitant fuzzy elements (IVHFEs). This paper then developed an outranking approach for MAGDM based on these operators to solve a consensus selection problem. In the first stage, which was based on the k-means clustering method for IVHFEs with feedback strategy taking both local and global consensus into consideration and a new consensus measure derived from the proposed variance measure, a compromised consensus was determined for each group involved in the decision. In the second stage, which was based on a probabilistic interval-valued hesitant fuzzy outranking method, the optimal alternative was determined based on the consensus information from the first stage. A case study on the enterprise credit risk assessment was given to illustrate the viability of the proposed method, which was then also compared with other current methods to demonstrate its greater flexibility and potential value.

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TIST Journal 2022 Journal Article

Efficient and Effective Similar Subtrajectory Search: A Spatial-aware Comprehension Approach

Liwei Deng
Hao Sun
Rui Sun
Yan Zhao
Han Su

Although many applications take subtrajectories as basic units for analysis, there is little research on the similar subtrajectory search problem aiming to return a portion of a trajectory (i.e., subtrajectory), which is the most similar to a query trajectory. We find that in some special cases, when a grid-based metric is used, this problem can be formulated as a reading comprehension problem, which has been studied extensively in the field of natural language processing (NLP). By this formulation, we can obtain faster models with better performance than existing methods. However, due to the difference between natural language and trajectory (e.g., spatial relationship), it is impossible to directly apply NLP models to this problem. Therefore, we propose a Similar Subtrajectory Search with a Graph Neural Networks framework. This framework contains four modules including a spatial-aware grid embedding module, a trajectory embedding module, a query-context trajectory fusion module, and a span prediction module. Specifically, in the spatial-aware grid embedding module, the spatial-based grid adjacency is constructed and delivered to the graph neural network to learn spatial-aware grid embedding. The trajectory embedding module aims to model the sequential information of trajectories. The purpose of the query-context trajectory fusion module is to fuse the information of the query trajectory to each grid of the context trajectories. Finally, the span prediction module aims to predict the start and the end of a subtrajectory for the context trajectory, which is the most similar to the query trajectory. We conduct comprehensive experiments on two real world datasets, where the proposed framework outperforms the state-of-the-art baselines consistently and significantly.

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AAAI Conference 2019 Conference Paper

Preference-Aware Task Assignment in Spatial Crowdsourcing

Yan Zhao
Jinfu Xia
Guanfeng Liu
Han Su
Defu Lian
Shuo Shang
Kai Zheng

With the ubiquity of smart devices, Spatial Crowdsourcing (SC) has emerged as a new transformative platform that engages mobile users to perform spatio-temporal tasks by physically traveling to specified locations. Thus, various SC techniques have been studied for performance optimization, among which one of the major challenges is how to assign workers the tasks that they are really interested in and willing to perform. In this paper, we propose a novel preference-aware spatial task assignment system based on workers’ temporal preferences, which consists of two components: History-based Context-aware Tensor Decomposition (HCTD) for workers’ temporal preferences modeling and preference-aware task assignment. We model worker preferences with a three-dimension tensor (worker-task-time). Supplementing the missing entries of the tensor through HCTD with the assistant of historical data and other two context matrices, we recover worker preferences for different categories of tasks in different time slots. Several preference-aware task assignment algorithms are then devised, aiming to maximize the total number of task assignments at every time instance, in which we give higher priorities to the workers who are more interested in the tasks. We conduct extensive experiments using a real dataset, verifying the practicability of our proposed methods.

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