Tabular Learnwares Can Be Repurposed for Seemingly Irrelevant New Tasks

Abstract 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.