<ul><li>Discrete latent factor models (DLFMs) are widely used in various domains such as machine learning, economics, neuroscience, psychology, etc.</li><li>A generic framework based on CVXPY is proposed to specify and solve the fitting problem of a wide range of DLFMs, including regression and classification models.</li><li>The framework allows for the integration of regularization terms and constraints on the DLFM parameters and latent factors, providing flexibility for customization.</li><li>An open-source Python implementation is introduced, and several examples illustrate the usage of the framework.</li></ul>

Multi-convex Programming for Discrete Latent Factor Models Prototyping

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