pared: Model selection using multi-objective optimization

• URL: http://arxiv.org/abs/2505.21730v1
• Date: Tue, 27 May 2025 20:20:04 GMT
• Title: pared: Model selection using multi-objective optimization
• Authors: Priyam Das, Sarah Robinson, Christine B. Peterson,
• Abstract summary: We present the R package pared to enable the use of multi-objective optimization for model selection.<n>Our approach entails the use of Gaussian process-based optimization to efficiently identify solutions that represent desirable trade-offs.
• Score: 0.351124620232225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Motivation: Model selection is a ubiquitous challenge in statistics. For penalized models, model selection typically entails tuning hyperparameters to maximize a measure of fit or minimize out-of-sample prediction error. However, these criteria fail to reflect other desirable characteristics, such as model sparsity, interpretability, or smoothness. Results: We present the R package pared to enable the use of multi-objective optimization for model selection. Our approach entails the use of Gaussian process-based optimization to efficiently identify solutions that represent desirable trade-offs. Our implementation includes popular models with multiple objectives including the elastic net, fused lasso, fused graphical lasso, and group graphical lasso. Our R package generates interactive graphics that allow the user to identify hyperparameter values that result in fitted models which lie on the Pareto frontier. Availability: We provide the R package pared and vignettes illustrating its application to both simulated and real data at https://github.com/priyamdas2/pared.

Related papers

• Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing [58.52119063742121]
  Retraining a model using its own predictions together with the original, potentially noisy labels is a well-known strategy for improving the model performance.<n>This paper addresses the question of how to optimally combine the model's predictions and the provided labels.<n>Our main contribution is the derivation of the Bayes optimal aggregator function to combine the current model's predictions and the given labels.
  arXiv  Detail & Related papers  (2025-05-21T07:16:44Z)
• MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation [80.47072100963017]
  We introduce a novel and low-compute algorithm, Model Merging with Amortized Pareto Front (MAP)<n>MAP efficiently identifies a set of scaling coefficients for merging multiple models, reflecting the trade-offs involved.<n>We also introduce Bayesian MAP for scenarios with a relatively low number of tasks and Nested MAP for situations with a high number of tasks, further reducing the computational cost of evaluation.
  arXiv  Detail & Related papers  (2024-06-11T17:55:25Z)
• REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values [17.489279048199304]
  REFRESH is a method to reselect features so that additional constraints that are desirable towards model performance can be achieved without having to train several new models. REFRESH's underlying algorithm is a novel technique using SHAP values and correlation analysis that can approximate for the predictions of a model without having to train these models.
  arXiv  Detail & Related papers  (2024-03-13T18:06:43Z)
• Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective [0.7373617024876725]
  Kernel density estimation (KDE) based models are popular choices for this task, but they fail to adapt to data regions with varying densities. An adaptive KDE model is employed to circumvent this, where each kernel in the model has an individual bandwidth. A modified expectation-maximization algorithm is employed to accelerate the optimization speed reliably.
  arXiv  Detail & Related papers  (2023-10-05T14:08:42Z)
• Out-of-sample scoring and automatic selection of causal estimators [0.0]
  We propose novel scoring approaches for both the CATE case and an important subset of instrumental variable problems. We implement that in an open source package that relies on DoWhy and EconML libraries.
  arXiv  Detail & Related papers  (2022-12-20T08:29:18Z)
• Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications [0.0]
  We show that combining different regression models can yield better results than selecting a single ('best') regression model. We outline an efficient method that obtains optimally weighted linear combination from a heterogeneous set of regression models.
  arXiv  Detail & Related papers  (2022-06-22T09:11:14Z)
• Approximate Bayesian Optimisation for Neural Networks [6.921210544516486]
  A body of work has been done to automate machine learning algorithm to highlight the importance of model choice. The necessity to solve the analytical tractability and the computational feasibility in a idealistic fashion enables to ensure the efficiency and the applicability.
  arXiv  Detail & Related papers  (2021-08-27T19:03:32Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• 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)
• Probabilistic Circuits for Variational Inference in Discrete Graphical Models [101.28528515775842]
  Inference in discrete graphical models with variational methods is difficult. Many sampling-based methods have been proposed for estimating Evidence Lower Bound (ELBO) We propose a new approach that leverages the tractability of probabilistic circuit models, such as Sum Product Networks (SPN) We show that selective-SPNs are suitable as an expressive variational distribution, and prove that when the log-density of the target model is aweighted the corresponding ELBO can be computed analytically.
  arXiv  Detail & Related papers  (2020-10-22T05:04:38Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)

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.