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Xinyue Li

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8 papers

2 author rows

AAAI Conference 2026 Conference Paper

Dual-Path Knowledge-Augmented Contrastive Alignment Network for Spatially Resolved Transcriptomics

Wei Zhang
Jiajun Chu
Xinci Liu
Chen Tong
Xinyue Li

Spatial Transcriptomics (ST) is a technology that measures gene expression profiles within tissue sections while retaining spatial context. It reveals localized gene expression patterns and tissue heterogeneity, both of which are essential for understanding disease etiology. However, its high cost has driven efforts to predict spatial gene expression from whole slide images. Despite recent advancements, current methods still face significant limitations, such as under-exploitation of high-level biological context, over-reliance on exemplar retrievals, and inadequate alignment of heterogeneous modalities. To address these challenges, we propose DKAN, a novel Dual-path Knowledge-Augmented contrastive alignment Network that predicts spatially resolved gene expression by integrating histopathological images and gene expression profiles through a biologically informed approach. Specifically, we introduce an effective gene semantic representation module that leverages the external gene database to provide additional biological insights, thereby enhancing gene expression prediction. Further, we adopt a unified, one-stage contrastive learning paradigm, seamlessly combining contrastive learning and supervised learning to eliminate reliance on exemplars, complemented with an adaptive weighting mechanism. Additionally, we propose a dual-path contrastive alignment module that employs gene semantic features as dynamic cross-modal coordinators to enable effective heterogeneous feature integration. Through extensive experiments across three public ST datasets, DKAN demonstrates superior performance over state-of-the-art models, establishing a new benchmark for spatial gene expression prediction and offering a powerful tool for advancing biological and clinical research.

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JBHI Journal 2026 Journal Article

Img2Gene: Debiased Spatially Resolved Transcriptomics with Biological Context from Pathology Images

Wei Zhang
Tong Chen
Wenxin Xu
Collin Sakal
Xinyue Li

Spatial transcriptomics integrates morphological information from pathology images with gene expression, providing high-resolution spatial gene expression profiles while preserving tissue architectures in a cost-effective manner. However, the inherent heterogeneity between images and gene expression data, coupled with sparse gene expression distribution, poses significant challenges for accurate and unbiased prediction models. To address these issues, we propose Img2Gene, a debiased framework designed to predict gene expression levels from whole slide images by incorporating biological context. Specifically, we integrate causal analysis into the gene expression prediction task to mitigate data sparsity and achieve unbiased predictions. Furthermore, we employ gene set enrichment analysis to identify highly associated pathway information as biological context and introduce a cross-modal coherence loss to align data from different modalities, fostering enhanced interplay among diverse features and achieving improved accuracy of gene expression prediction. Extensive experiments conducted on four public datasets demonstrate that our method achieves state-of-the-art performance. The pathway data and source code are available at https://github.com/coffeeNtv/Img2Gene.

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AAAI Conference 2026 Conference Paper

Post-Hoc Refinement for Multitask Symbolic Regression via Consensus-Accelerated Shapley Analysis

Xinyue Li
Wang Hu
Yu Zhang

Multitask genetic programming (MTGP) is one of the primary methods for solving multitask symbolic regression (MTSR), the problem of discovering mathematical expressions for multiple interconnected tasks simultaneously. However, conventional MTGP approaches discard a wealth of valuable knowledge from the population of expressions due to their inherent “winner-take-all” selection criteria. To address this, we introduce MTGP with bidirectional cooperation and consensus-accelerated Shapley analysis (MTGP-BS), a method whose core is a novel post-hoc refinement framework that shifts from selection to synthesis. Our method first employs a consensus-accelerated Shapley analysis to reliably identify important subexpressions by multi-model attribution. Second, to supply this analysis with high-quality candidates, we design a bidirectional subexpression cooperative extraction method to create a refined archive of effective components by improving knowledge transfer and filtering out redundancies. These allow MTGP-BS to synthesize superior expressions by integrating knowledge dispersed throughout the entire population. On diverse MTSR problems, our algorithm statistically outperformed state-of-the-art approaches in 140 out of 160 direct comparisons, with its effectiveness and practical utility further verified by real-world case studies and in-depth ablation analyses.

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AAAI Conference 2026 Conference Paper

Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling

Xiao Cui
Yulei Qin
Xinyue Li
Wengang Zhou
Hongsheng Li
Houqiang Li

Dataset distillation creates a small distilled set that enables efficient training by capturing key information from the full dataset. While existing dataset distillation methods perform well on balanced datasets, they struggle under long-tailed distributions, where imbalanced class frequencies induce biased model representations and corrupt statistical estimates such as Batch Normalization (BN) statistics. In this paper, we rethink long-tailed dataset distillation by revisiting the limitations of trajectory-based methods, and instead adopt the statistical alignment perspective to jointly mitigate model bias and restore fair supervision. To this end, we introduce three dedicated components that enable unbiased recovery of distilled images and soft relabeling: (1) enhancing expert models (an observer model for recovery and a teacher model for relabeling) to enable reliable statistics estimation and soft-label generation; (2) recalibrating BN statistics via a full forward pass with dynamically adjusted momentum to reduce representation skew; (3) initializing synthetic images by incrementally selecting high-confidence and diverse augmentations via a multi-round mechanism that promotes coverage and diversity. Extensive experiments on four long-tailed benchmarks show consistent improvements over state-of-the-art methods across varying degrees of class imbalance.Notably, our approach improves top-1 accuracy by 15.6% on CIFAR-100-LT and 11.8% on Tiny-ImageNet-LT under IPC=10 and IF=10.

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JBHI Journal 2025 Journal Article

Quantum-Resistant Privacy Preservation for Mobile Healthcare Services in Connected Transportation Systems via Deep Neural Architectures

Xinyue Li
Bo Yi
Xingsi Xue
Zhi Wang
Jing Yang

The rapid convergence of connected transportation networks and real-time healthcare services has given rise to new security and privacy challenges. Conventional cryptographic mechanisms, primarily designed for classical adversaries, may soon be rendered obsolete by quantum computers, posing dire risks to the confidentiality of sensitive medical data. This work proposes a quantum-resistant privacy preservation framework for mobile healthcare systems operating in vehicular networks. Leveraging lattice-based cryptography-specifically Ring Learning-with-Errors (Ring-LWE)-our approach ensures robust encryption and key management, rendering patient data impervious to quantum-based attacks. Complementing this cryptographic layer is a deep neural network architecture that integrates convolutional and attention-based modules to detect network anomalies with high accuracy and minimal latency. We demonstrate the feasibility of our method through comprehensive experiments that measure (1) cryptographic overhead, (2) intrusion detection effectiveness, and (3) end-to-end system performance under realistic conditions and varied load scenarios. Experimental results show that the proposed scheme can maintain sub-100 ms end-to-end latencies for healthcare data transfer in high-traffic urban networks, detecting a wide range of attacks at accuracy levels exceeding 95%. These findings underscore the potential of combining post-quantum cryptographic primitives with advanced deep learning to secure time-sensitive medical applications within next-generation intelligent transportation systems.

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NeurIPS Conference 2025 Conference Paper

Sekai: A Video Dataset towards World Exploration

Zhen Li
Chuanhao Li
Xiaofeng Mao
Shaoheng Lin
Ming Li
Shitian Zhao
Zhaopan Xu
Xinyue Li

Video generation techniques have made remarkable progress, promising to be the foundation of interactive world exploration. However, existing video generation datasets are not well-suited for world exploration training as they suffer from some limitations: limited locations, short duration, static scenes, and a lack of annotations about exploration and the world. In this paper, we introduce Sekai (meaning "world" in Japanese), a high-quality first-person view worldwide video dataset with rich annotations for world exploration. It consists of over 5, 000 hours of walking or drone view (FPV and UVA) videos from over 100 countries and regions across 750 cities. We develop an efficient and effective toolbox to collect, pre-process and annotate videos with location, scene, weather, crowd density, captions, and camera trajectories. Comprehensive analyses and experiments demonstrate the dataset’s scale, diversity, annotation quality, and effectiveness for training video generation models. We believe Sekai will benefit the area of video generation and world exploration, and motivate valuable applications.

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NeurIPS Conference 2024 Conference Paper

Least Squares Regression Can Exhibit Under-Parameterized Double Descent

Xinyue Li
Rishi Sonthalia

The relationship between the number of training data points, the number of parameters, and the generalization capabilities of models has been widely studied. Previous work has shown that double descent can occur in the over-parameterized regime and that the standard bias-variance trade-off holds in the under-parameterized regime. These works provide multiple reasons for the existence of the peak. We postulate that the location of the peak depends on the technical properties of both the spectrum as well as the eigenvectors of the sample covariance. We present two simple examples that provably exhibit double descent in the under-parameterized regime and do not seem to occur for reasons provided in prior work.

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ICLR Conference 2023 Conference Paper

PLOT: Prompt Learning with Optimal Transport for Vision-Language Models

Guangyi Chen 0002
Weiran Yao
Xiangchen Song
Xinyue Li
Yongming Rao
Kun Zhang 0001

With the increasing attention to large vision-language models such as CLIP, there has been a significant amount of effort dedicated to building efficient prompts. Unlike conventional methods of only learning one single prompt, we propose to learn multiple comprehensive prompts to describe diverse characteristics of categories such as intrinsic attributes or extrinsic contexts. However, directly matching each prompt to the same visual feature is problematic, as it pushes the prompts to converge to one point. To solve this problem, we propose to apply optimal transport to match the vision and text modalities. Specifically, we first model images and the categories with visual and textual feature sets. Then, we apply a two-stage optimization strategy to learn the prompts. In the inner loop, we optimize the optimal transport distance to align visual features and prompts by the Sinkhorn algorithm, while in the outer loop, we learn the prompts by this distance from the supervised data. Extensive experiments are conducted on the few-shot recognition task and the improvement demonstrates the superiority of our method. The code is available at https://github.com/CHENGY12/PLOT.

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