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Peng Tan

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3 papers

1 author row

AAAI Conference 2026 Conference Paper

Tabular Learnwares Can Be Repurposed for Seemingly Irrelevant New Tasks

Peng Tan
Feifan Yang
Zhi-Hao Tan
Zhi-Hua Zhou

The learnware paradigm aims to help users solve new tasks by reusing existing models rather than starting from scratch. A learnware consists of a model and the specification describing its capabilities. Numerous learnwares are accommodated by the learnware dock system. When users solve tasks with the system, learnwares that fully match the user task are often scarce or unavailable. This paper focuses on tabular classification tasks and explores reusing learnwares for new user tasks with significantly different feature and label spaces, leveraging the potential of numerous existing specialized tabular models developed for various tasks. Under the learnware paradigm, we find that tabular learnwares that seem semantically irrelevant can sometimes be beneficial for new user tasks. The proposed method relies solely on model-predicted probabilities and does not require gradient information, making it applicable to a wide range of tabular models. Experiments suggest that tabular learnwares can be reused beyond their original purpose across heterogeneous tasks.

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NeurIPS Conference 2024 Conference Paper

Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation

Peng Tan
Hai-Tian Liu
Zhi-Hao Tan
Zhi-Hua Zhou

The learnware paradigm aims to help users leverage numerous existing high-performing models instead of starting from scratch, where a learnware consists of a well-trained model and the specification describing its capability. Numerous learnwares are accommodated by a learnware dock system. When users solve tasks with the system, models that fully match the task feature space are often rare or even unavailable. However, models with heterogeneous feature space can still be helpful. This paper finds that label information, particularly model outputs, is helpful yet previously less exploited in the accommodation of heterogeneous learnwares. We extend the specification to better leverage model pseudo-labels and subsequently enrich the unified embedding space for better specification evolvement. With label information, the learnware identification can also be improved by additionally comparing conditional distributions. Experiments demonstrate that, even without a model explicitly tailored to user tasks, the system can effectively handle tasks by leveraging models from diverse feature spaces.

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IJCAI Conference 2023 Conference Paper

Handling Learnwares Developed from Heterogeneous Feature Spaces without Auxiliary Data

Peng Tan
Zhi-Hao Tan
Yuan Jiang
Zhi-Hua Zhou

The learnware paradigm proposed by Zhou [2016] devotes to constructing a market of numerous well-performed models, enabling users to solve problems by reusing existing efforts rather than starting from scratch. A learnware comprises a trained model and the specification which enables the model to be adequately identified according to the user's requirement. Previous studies concentrated on the homogeneous case where models share the same feature space based on Reduced Kernel Mean Embedding (RKME) specification. However, in real-world scenarios, models are typically constructed from different feature spaces. If such a scenario can be handled by the market, all models built for a particular task even with different feature spaces can be identified and reused for a new user task. Generally, this problem would be easier if there were additional auxiliary data connecting different feature spaces, however, obtaining such data in reality is challenging. In this paper, we present a general framework for accommodating heterogeneous learnwares without requiring additional auxiliary data. The key idea is to utilize the submitted RKME specifications to establish the relationship between different feature spaces. Additionally, we give a matrix factorization-based implementation and propose the overall procedure for constructing and exploiting the heterogeneous learnware market. Experiments on real-world tasks validate the efficacy of our method.

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