Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection

• URL: http://arxiv.org/abs/2509.06383v1
• Date: Mon, 08 Sep 2025 07:06:10 GMT
• Title: Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection
• Authors: Hyungjoon Soh, Dongha Lee, Vipul Periwal, Junghyo Jo,
• Abstract summary: We revisit the statistical physics-based Variational Garrote (VG) method, which introduces explicit feature selection spin variables.<n>We evaluate VG on both fully controllable synthetic datasets and complex real-world datasets.<n> VG offers strong potential for sparse modeling across a wide range of applications, including compressed sensing and model pruning in machine learning.
• Score: 8.312621461460148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Selecting key variables from high-dimensional data is increasingly important in the era of big data. Sparse regression serves as a powerful tool for this purpose by promoting model simplicity and explainability. In this work, we revisit a valuable yet underutilized method, the statistical physics-based Variational Garrote (VG), which introduces explicit feature selection spin variables and leverages variational inference to derive a tractable loss function. We enhance VG by incorporating modern automatic differentiation techniques, enabling scalable and efficient optimization. We evaluate VG on both fully controllable synthetic datasets and complex real-world datasets. Our results demonstrate that VG performs especially well in highly sparse regimes, offering more consistent and robust variable selection than Ridge and LASSO regression across varying levels of sparsity. We also uncover a sharp transition: as superfluous variables are admitted, generalization degrades abruptly and the uncertainty of the selection variables increases. This transition point provides a practical signal for estimating the correct number of relevant variables, an insight we successfully apply to identify key predictors in real-world data. We expect that VG offers strong potential for sparse modeling across a wide range of applications, including compressed sensing and model pruning in machine learning.

Related papers

• Knoop: Practical Enhancement of Knockoff with Over-Parameterization for Variable Selection [27.563529091471935]
  This work introduces a novel approach namely Knockoff with over- parameterization (Knoop) to enhance variable selection.<n>Knoop generates multiple knockoff variables for each original variable and integrates them with the original variables into a Ridgeless regression model.<n>Experiments demonstrate superior performance compared to existing methods in both simulation and real-world datasets.
  arXiv  Detail & Related papers  (2025-01-28T09:27:04Z)
• Model-independent variable selection via the rule-based variable priority [1.2771542695459488]
  We introduce a new model-independent approach, Variable Priority (VarPro) VarPro works by utilizing rules without the need to generate artificial data or evaluate prediction error. We show that VarPro has a consistent filtering property for noise variables.
  arXiv  Detail & Related papers  (2024-09-13T17:32:05Z)
• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• Dynamic Model Switching for Improved Accuracy in Machine Learning [0.0]
  We introduce an adaptive ensemble that intuitively transitions between CatBoost and XGBoost. The user sets a benchmark, say 80% accuracy, prompting the system to dynamically shift to the new model only if it guarantees improved performance. This dynamic model-switching mechanism aligns with the evolving nature of data in real-world scenarios.
  arXiv  Detail & Related papers  (2024-01-31T00:13:02Z)
• Contextual Feature Selection with Conditional Stochastic Gates [9.784482648233048]
  Conditional Gates (c-STG) models the importance of features using conditional variables whose parameters are predicted based on contextual variables. We show that c-STG can lead to improved feature selection capabilities while enhancing prediction accuracy and interpretability.
  arXiv  Detail & Related papers  (2023-12-21T19:12:59Z)
• Lazy Estimation of Variable Importance for Large Neural Networks [22.95405462638975]
  We propose a fast and flexible method for approximating the reduced model with important inferential guarantees. We demonstrate our method is fast and accurate under several data-generating regimes, and we demonstrate its real-world applicability on a seasonal climate forecasting example.
  arXiv  Detail & Related papers  (2022-07-19T06:28:17Z)
• Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations [76.82124752950148]
  We develop a convenient gradient-based method for selecting the data augmentation. We use a differentiable Kronecker-factored Laplace approximation to the marginal likelihood as our objective.
  arXiv  Detail & Related papers  (2022-02-22T02:51:11Z)
• X-model: Improving Data Efficiency in Deep Learning with A Minimax Model [78.55482897452417]
  We aim at improving data efficiency for both classification and regression setups in deep learning. To take the power of both worlds, we propose a novel X-model. X-model plays a minimax game between the feature extractor and task-specific heads.
  arXiv  Detail & Related papers  (2021-10-09T13:56:48Z)
• CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals [92.60744099084157]
  We propose differentiable data augmentation amenable to gradient-based learning. We demonstrate the relevance of our approach on the clinically relevant sleep staging classification task.
  arXiv  Detail & Related papers  (2021-06-25T15:28:48Z)
• Variable Skipping for Autoregressive Range Density Estimation [84.60428050170687]
  We show a technique, variable skipping, for accelerating range density estimation over deep autoregressive models. We show that variable skipping provides 10-100$times$ efficiency improvements when targeting challenging high-quantile error metrics.
  arXiv  Detail & Related papers  (2020-07-10T19:01:40Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)

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.