用通用框架快速搭建和求解离散潜变量模型，省去重复编码。
Multi-convex Programming for Discrete Latent Factor Models Prototyping
- 基于CVXPY构建通用优化框架，支持多种离散潜变量模型
- 只需简短脚本即可完成建模与求解，适配回归与分类任务
- 支持正则化与参数约束，适合快速原型设计
离散潜变量模型（DLFMs）广泛应用于机器学习、经济学、神经科学、心理学等领域。当前拟合DLFM需为每个模型定制求解器，实现成本高且仅适用于特定实例。本文提出基于CVXPY的通用框架，用户可在极短脚本内指定并求解多种DLFM，涵盖回归与分类模型。该框架灵活，天然支持对模型参数和潜因子施加正则化项与约束，便于根据数据集和应用场景快速原型化模型结构。我们公开了Python实现，并通过多个示例展示其应用。
原文摘要 · Abstract (English)
Discrete latent factor models (DLFMs) are widely used in various domains such as machine learning, economics, neuroscience, psychology, etc. Currently, fitting a DLFM to some dataset relies on a customized solver for individual models, which requires lots of effort to implement and is limited to the targeted specific instance of DLFMs. In this paper, we propose a generic framework based on CVXPY, which allows users to specify and solve the fitting problem of a wide range of DLFMs, including both regression and classification models, within a very short script. Our framework is flexible and inherently supports the integration of regularization terms and constraints on the DLFM parameters and latent factors, such that the users can easily prototype the DLFM structure according to their dataset and application scenario. We introduce our open-source Python implementation and illustrate the framework in several examples.
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