AAAI 2024technical1 citations
Shuffled Deep Regression
Abstract
Shuffled regression is the problem of learning regression models from shuffled data that consists of a set of input features and a set of target outputs where the correspondence between the input and output is unknown. This study proposes a new deep learning method for shuffled regression called Shuffled Deep Regression (SDR). We derive the sparse and stochastic variant of the Expectation-Maximization algorithm for SDR that iteratively updates discrete latent variables and the parameters of neural networks. The effectiveness of the proposal is confirmed by benchmark data experiments.
BibTeX
@article{Kohjima_2024, title={Shuffled Deep Regression}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/29224}, DOI={10.1609/aaai.v38i12.29224}, abstractNote={Shuffled regression is the problem of learning regression models from shuffled data that consists of a set of input features and a set of target outputs where the correspondence between the input and output is unknown. This study proposes a new deep learning method for shuffled regression called Shuffled Deep Regression (SDR). We derive the sparse and stochastic variant of the Expectation-Maximization algorithm for SDR that iteratively updates discrete latent variables and the parameters of neural networks. The effectiveness of the proposal is confirmed by benchmark data experiments.}, number={12}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Kohjima, Masahiro}, year={2024}, month={Mar.}, pages={13238-13245} }