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Jiaying Zhou

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AAAI Conference 2025 Conference Paper

Joint Class-level and Instance-level Relationship Modeling for Novel Class Discovery

Jiaying Zhou
Qingchao Chen

Novel class discovery(NCD) aims to cluster the unlabeled data with the help of a labeled set containing different but related classes. The key to solving NCD is the knowledge transfer between labeled and unlabeled sets.Since NCD requires that known classes and unknown classes are related, it is significant to explore class-level relationships between known and unknown for more effective knowledge transfer. However, most existing methods either facilitate knowledge transfer by learning a shared representation space or by modeling coarse-grained or asymmetric relationships between known and unknown, neglecting class-level relationships. To tackle these challenges, we propose a symmetric class-to-class relationship modeling and knowledge transfer method, achieving bidirectional knowledge transfer at class-level. Considering that class-level modeling often overlooks the subtle distinctions between samples, we propose pairwise similarity-based relationship modeling and consistency constraint for instance-level knowledge transfer. Extensive experiments on CIFAR100 and three fine-grained datasets demonstrate that our method achieves significant performance improvements compared to state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

Novel Class Discovery in Chest X-rays via Paired Images and Text

Jiaying Zhou
Yang Liu
Qingchao Chen

Novel class discover(NCD) aims to identify new classes undefined during model training phase with the help of knowledge of known classes. Many methods have been proposed and notably boosted performance of NCD in natural images. However, there has been no work done in discovering new classes based on medical images and disease categories, which is crucial for understanding and diagnosing specific diseases. Moreover, most of the existing methods only utilize information from image modality and use labels as the only supervisory information. In this paper, we propose a multi-modal novel class discovery method based on paired images and text, inspired by the low classification accuracy of chest X-ray images and the relatively higher accuracy of the paired text. Specifically, we first pretrain the image encoder and text encoder with multi-modal contrastive learning on the entire dataset and then we generate pseudo-labels separately on the image branch and text branch. We utilize intra-modal consistency to assess the quality of pseudo-labels and adjust the weights of the pseudo-labels from both branches to generate the ultimate pseudo-labels for training. Experiments on eight subset splits of MIMIC-CXR-JPG dataset show that our method improves the clustering performance of unlabeled classes by about 10% on average compared to state-of-the-art methods. Code is available at: https://github.com/zzzzzzzzjy/MMNCD-main.

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TMLR Journal 2023 Journal Article

Assisted Learning for Organizations with Limited Imbalanced Data

Cheng Chen
Jiaying Zhou
Jie Ding
Yi Zhou

In the era of big data, many big organizations are integrating machine learning into their work pipelines to facilitate data analysis. However, the performance of their trained models is often restricted by limited and imbalanced data available to them. In this work, we develop an assisted learning framework for assisting organizations to improve their learning performance. The organizations have sufficient computation resources but are subject to stringent data-sharing and collaboration policies. Their limited imbalanced data often cause biased inference and sub-optimal decision-making. In assisted learning, an organizational learner purchases assistance service from an external service provider and aims to enhance its model performance within only a few assistance rounds. We develop effective stochastic training algorithms for both assisted deep learning and assisted reinforcement learning. Different from existing distributed algorithms that need to frequently transmit gradients or models, our framework allows the learner to only occasionally share information with the service provider, but still obtain a model that achieves near-oracle performance as if all the data were centralized.

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JMLR Journal 2021 Journal Article

Model Linkage Selection for Cooperative Learning

Jiaying Zhou
Jie Ding
Kean Ming Tan
Vahid Tarokh

We consider the distributed learning setting where each agent or learner holds a specific parametric model and a data source. The goal is to integrate information across a set of learners and data sources to enhance the prediction accuracy of a given learner. A natural way to integrate information is to build a joint model across a group of learners that shares common parameters of interest. However, the underlying parameter sharing patterns across a set of learners may not be known a priori. Misspecifying the parameter sharing patterns or the parametric model for each learner often yields a biased estimator that degrades the prediction accuracy. We propose a general method to integrate information across a set of learners that is robust against misspecification of both models and parameter sharing patterns. The main crux of our proposed method is to sequentially incorporate additional learners that can enhance the prediction accuracy of an existing joint model based on user- specified parameter sharing patterns across a set of learners. Theoretically, we show that the proposed method can data-adaptively select a parameter sharing pattern that enhances the predictive performance of a given learner. Extensive numerical studies are conducted to assess the performance of the proposed method. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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