Automated Model Selection for Generalized Linear Models

• URL: http://arxiv.org/abs/2404.16560v1
• Date: Thu, 25 Apr 2024 12:16:58 GMT
• Title: Automated Model Selection for Generalized Linear Models
• Authors: Benjamin Schwendinger, Florian Schwendinger, Laura Vana-Gür,
• Abstract summary: We show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models. We propose a novel pairwise correlation constraint that combines the sign coherence constraint with ideas from classical statistical models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we show how mixed-integer conic optimization can be used to combine feature subset selection with holistic generalized linear models to fully automate the model selection process. Concretely, we directly optimize for the Akaike and Bayesian information criteria while imposing constraints designed to deal with multicollinearity in the feature selection task. Specifically, we propose a novel pairwise correlation constraint that combines the sign coherence constraint with ideas from classical statistical models like Ridge regression and the OSCAR model.

Related papers

• Stabilizing black-box model selection with the inflated argmax [8.52745154080651]
  This paper presents a new approach to stabilizing model selection that leverages a combination of bagging and an "inflated" argmax operation. Our method selects a small collection of models that all fit the data, and it is stable in that, with high probability, the removal of any training point will result in a collection of selected models that overlaps with the original collection. In both settings, the proposed method yields stable and compact collections of selected models, outperforming a variety of benchmarks.
  arXiv  Detail & Related papers  (2024-10-23T20:39:07Z)
• An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting [53.36437745983783]
  We first construct a max-margin optimization-based model to model potentially non-monotonic preferences. We devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration. Two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences.
  arXiv  Detail & Related papers  (2024-09-04T14:36:20Z)
• You Only Merge Once: Learning the Pareto Set of Preference-Aware Model Merging [11.186194228460273]
  We propose preference-aware model merging in which the performance of the merged model on each base model's task is treated as an objective. In only one merging process, the proposed parameter-efficient structure can generate the whole set of merged models. We show that the proposed preference-aware model merging can obtain a diverse set of trade-off models and outperforms state-of-the-art model merging baselines.
  arXiv  Detail & Related papers  (2024-08-22T03:41:14Z)
• The Interpolating Information Criterion for Overparameterized Models [49.283527214211446]
  We show that the Interpolating Information Criterion is a measure of model quality that naturally incorporates the choice of prior into the model selection. Our new information criterion accounts for prior misspecification, geometric and spectral properties of the model, and is numerically consistent with known empirical and theoretical behavior.
  arXiv  Detail & Related papers  (2023-07-15T12:09:54Z)
• Personalized Federated Learning via Convex Clustering [72.15857783681658]
  We propose a family of algorithms for personalized federated learning with locally convex user costs. The proposed framework is based on a generalization of convex clustering in which the differences between different users' models are penalized.
  arXiv  Detail & Related papers  (2022-02-01T19:25:31Z)
• Data Summarization via Bilevel Optimization [48.89977988203108]
  A simple yet powerful approach is to operate on small subsets of data. In this work, we propose a generic coreset framework that formulates the coreset selection as a cardinality-constrained bilevel optimization problem.
  arXiv  Detail & Related papers  (2021-09-26T09:08:38Z)
• Split Modeling for High-Dimensional Logistic Regression [0.2676349883103404]
  A novel method is proposed to an ensemble logistic classification model briefly compiled. Our method learns how to exploit the bias-off resulting in excellent prediction accuracy. An open-source software library implementing the proposed method is discussed.
  arXiv  Detail & Related papers  (2021-02-17T05:57:26Z)
• Autoregressive Score Matching [113.4502004812927]
  We propose autoregressive conditional score models (AR-CSM) where we parameterize the joint distribution in terms of the derivatives of univariable log-conditionals (scores) For AR-CSM models, this divergence between data and model distributions can be computed and optimized efficiently, requiring no expensive sampling or adversarial training. We show with extensive experimental results that it can be applied to density estimation on synthetic data, image generation, image denoising, and training latent variable models with implicit encoders.
  arXiv  Detail & Related papers  (2020-10-24T07:01:24Z)
• Identification of Probability weighted ARX models with arbitrary domains [75.91002178647165]
  PieceWise Affine models guarantees universal approximation, local linearity and equivalence to other classes of hybrid system. In this work, we focus on the identification of PieceWise Auto Regressive with eXogenous input models with arbitrary regions (NPWARX) The architecture is conceived following the Mixture of Expert concept, developed within the machine learning field.
  arXiv  Detail & Related papers  (2020-09-29T12:50:33Z)
• Semi-nonparametric Latent Class Choice Model with a Flexible Class Membership Component: A Mixture Model Approach [6.509758931804479]
  The proposed model formulates the latent classes using mixture models as an alternative approach to the traditional random utility specification. Results show that mixture models improve the overall performance of latent class choice models.
  arXiv  Detail & Related papers  (2020-07-06T13:19:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.