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Doudou Zhou

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NeurIPS Conference 2025 Conference Paper

Wasserstein Transfer Learning

  • Kaicheng Zhang
  • Sinian Zhang
  • Doudou Zhou
  • Yidong Zhou

Transfer learning is a powerful paradigm for leveraging knowledge from source domains to enhance learning in a target domain. However, traditional transfer learning approaches often focus on scalar or multivariate data within Euclidean spaces, limiting their applicability to complex data structures such as probability distributions. To address this, we introduce a novel framework for transfer learning in regression models, where outputs are probability distributions residing in the Wasserstein space. When the informative subset of transferable source domains is known, we propose an estimator with provable asymptotic convergence rates, quantifying the impact of domain similarity on transfer efficiency. For cases where the informative subset is unknown, we develop a data-driven transfer learning procedure designed to mitigate negative transfer. The proposed methods are supported by rigorous theoretical analysis and are validated through extensive simulations and real-world applications.

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JMLR Journal 2023 Journal Article

Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices

  • Doudou Zhou
  • Tianxi Cai
  • Junwei Lu

Electronic healthcare records (EHR) provide a rich resource for healthcare research. An important problem for the efficient utilization of the EHR data is the representation of the EHR features, which include the unstructured clinical narratives and the structured codified data. Matrix factorization-based embeddings trained using the summary-level co-occurrence statistics of EHR data have provided a promising solution for feature representation while preserving patients' privacy. However, such methods do not work well with multi-source data when these sources have overlapping but non-identical features. To accommodate multi-sources learning, we propose a novel word embedding generative model. To obtain multi-source embeddings, we design an efficient Block-wise Overlapping Noisy Matrix Integration (BONMI) algorithm to aggregate the multi-source pointwise mutual information matrices optimally with a theoretical guarantee. Our algorithm can also be applied to other multi-source data integration problems with a similar data structure. A by-product of BONMI is the contribution to the field of matrix completion by considering the missing mechanism other than the entry-wise independent missing. We show that the entry-wise missing assumption, despite its prevalence in the works of matrix completion, is not necessary to guarantee recovery. We prove the statistical rate of our estimator, which is comparable to the rate under independent missingness. Simulation studies show that BONMI performs well under a variety of configurations. We further illustrate the utility of BONMI by integrating multi-lingual multi-source medical text and EHR data to perform two tasks: (i) co-training semantic embeddings for medical concepts in both English and Chinese and (ii) the translation between English and Chinese medical concepts. Our method shows an advantage over existing methods. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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