Integrating regularization methods with standard loss functions such as the least squares, hinge loss, etc., within a regression framework has become a popular choice for researchers to learn predictive models with lower variance and better generalization ability. Regularizers also aid in building interpretable models with high-dimensional data which makes them very appealing. It is observed that each regularizer is uniquely formulated in order to capture data-specific properties such as correlation, structured sparsity and temporal smoothness. The problem of obtaining a consensus among such diverse regularizers while learning a predictive model is extremely important in order to determine the optimal regularizer for the problem. The advantage of such an approach is that it preserves the simplicity of the final model learned by selecting a single candidate model which is not the case with ensemble methods as they use multiple candidate models for prediction. This is called the consensus regularization problem which has not received much attention in the literature due to the inherent difficulty associated with learning and selecting a model from an integrated regularization framework. To solve this problem, in this paper, we propose a method to generate a committee of non-convex regularized linear regression models, and use a consensus criterion to determine the optimal model for prediction. Each corresponding non-convex optimization problem in the committee is solved efficiently using the cyclic-coordinate descent algorithm with the generalized thresholding operator. Our Consensus RegularIzation Selection based Prediction (CRISP) model is evaluated on electronic health records (EHRs) obtained from a large hospital for the congestive heart failure readmission prediction problem. We also evaluate our model on high-dimensional synthetic datasets to assess its performance. The results indicate that CRISP outperforms several state-of-the-art methods such as additive, interactions-based and other competing non-convex regularized linear regression methods.
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CoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed
computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend
the framework to cover general non-strongly-convex regularizers, including L1-regularized
problems like lasso, sparse logistic regression, and elastic net regularization, and show how
earlier work can be derived as a special case. We provide convergence guarantees for the
class of convex regularized loss minimization objectives, leveraging a novel approach in
handling non-strongly-convex regularizers and non-smooth loss functions. The resulting
framework has markedly improved performance over state-of-the-art methods, as we illustrate with an extensive set of experiments on real distributed datasets.
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- NSF-PAR ID:
- 10110876
- Date Published:
- Journal Name:
- Journal of machine learning research
- Volume:
- 18
- ISSN:
- 1532-4435
- Page Range / eLocation ID:
- 1-49
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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