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Yongdao Zhou

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AAAI Conference 2026 Conference Paper

Evidence-aware Integration and Domain Identification of Spatial Transcriptomics Data

Wei Zhang
Siyu Yi
Lezhi Chen
Yifan Wang
Ziyue Qiao
Yongdao Zhou
Wei Ju

Spatial transcriptomics (ST) enables joint profiling of gene expression and spatial positions, thereby revealing spatially resolved biological functions. However, many existing ST analysis methods often fail to explicitly quantify the belief and uncertainty in decisions caused by noisy ST data, making it difficult to handle spots of varying quality in a fine-grained manner. In addition, domain identification is a fundamental and critical task in ST, but commonly used models that separate expression learning and clustering often struggle to learn cluster-friendly latent representations effectively. To address these issues, we propose PREST, a prototype-based evidence-aware integration framework for ST data. PREST performs multi-scale representation learning with fine-grained attention fusion and introduces learnable class prototypes to quantify belief and uncertainty in model decisions. We aim to align overall belief scores with latent semantic information to enhance uncertainty quantification and prototype learning, thereby promoting the learning of clustering-friendly representations. PREST further integrates an uncertainty-aware reconstruction module and spatial regularization to reduce overfitting to unreliable spots and promote denoised, discriminative representations. Extensive experiments on several benchmark datasets validate the effectiveness and superiority of our proposed PREST across various downstream tasks.

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NeurIPS Conference 2025 Conference Paper

Enhancing Deep Batch Active Learning for Regression with Imperfect Data Guided Selection

Yinjie Min
Furong Xu
Xinyao Li
Changliang Zou
Yongdao Zhou

Active learning (AL) reduces annotation costs by selecting the most informative samples based on both model sensitivity and predictive uncertainty. While sensitivity can be measured through parameter gradients in an unsupervised manner, predictive uncertainty can hardly be estimated without true labels especially for regression tasks, reducing the informativeness of actively selected samples. This paper proposes the concept of \textit{auxiliary data} to aid the uncertainty estimation for regression tasks. With detailed theoretical analysis, we reveal that auxiliary data, despite potential distribution shifts, can provide a promising uncertainty surrogate when properly weighted. Such finding inspires our design of AGBAL, a novel AL framework that recalibrates auxiliary data losses through density ratio weighting to obtain reliable uncertainty estimates for sample selection. Extensive experiments show that AGBAL consistently outperforms existing approaches without auxiliary data across diverse synthetic and real-world datasets.

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JMLR Journal 2024 Journal Article

GGD: Grafting Gradient Descent

Yanjing Feng
Yongdao Zhou

Simple random sampling has been widely used in traditional stochastic optimization algorithms. Although the gradient sampled by simple random sampling is a descent direction in expectation, it may have a relatively high variance which will cause the descent curve wiggling and slow down the optimization process. In this paper, we propose a novel stochastic optimization method called grafting gradient descent (GGD), which combines the strength from minibatching and importance sampling, and provide the convergence results of GGD. We show that the grafting gradient possesses a doubly robust property which ensures that the performance of GGD method is superior to the worse one of SGD with importance sampling method and mini-batch SGD method. Combined with advanced variance reduction techniques such as stochastic variance reduced gradient and adaptive stepsize methods such as Adam, these composite GGD-based methods and their theoretical bounds are provided. The real data studies also show that GGD achieves an intermediate performance among SGD with importance sampling and mini-batch SGD, and outperforms original SGD method. Then the proposed GGD is a better and more robust stochastic optimization framework in practice. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2024. ( edit, beta )

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