arXiv research

New method estimates selection event for black-box models.

Automates model selection for GLMs using optimization.

A framework for optimizing prompt selection in generative language models.

Enhances selective inference for generalized lasso using parametric programming.

The paper bridges theory and practice in query-driven selectivity learning.

The paper proposes methods to control errors in language generation models using textual entailment.

OptCS optimizes model selection after conformal inference, controlling FDR and power loss.

The paper addresses selection bias in conformal prediction for focal units.

We develop a general approach to valid inference after model selection. At the core of our framework is a result that characterizes the distribution of a post-selection estimator conditioned on the selection event. We specialize the approach to model selection by the lasso to form valid confidence intervals for the sel…

Study compares mutation validation and cross-validation for model selection.

Develops a more powerful selective inference method for stepwise feature selection.

The study improves compound selection in in silico screening by focusing on model's ability to predict desirable outcomes.

Generalizes region select game to $k$-colored knot diagrams.

Recently, many regularized procedures have been proposed for variable selection in linear regression, but their performance depends on the tuning parameter selection. Here a criterion for the tuning parameter selection is proposed, which combines the strength of both stability selection and cross-validation and therefo…

CASP selects reliable policies for two-stage recommender systems by considering both value and support.

Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of feature stream. For instance, in image analysis, features are generated in groups which represent color, texture…

This paper improves volatility forecasting using dynamic subset selection in genetic programming.

Model selection is difficult to analyse yet theoretically and empirically important, especially for high-dimensional data analysis. Recently the least absolute shrinkage and selection operator (Lasso) has been applied in the statistical and econometric literature. Consis- tency of Lasso has been established under vario…

Optimizes kernel discrepancies by selecting subsets efficiently.

Many deep models have been recently proposed for anomaly detection. This paper presents comparison of selected generative deep models and classical anomaly detection methods on an extensive number of non--image benchmark datasets. We provide statistical comparison of the selected models, in many configurations, archite…

Anisotropic neural network selects relevant features from datasets.

New method corrects selection bias in post-selective inference for Group LASSO.

Bayesian Pseudo Label Selection reduces overfitting in semi-supervised learning.

This paper introduces a new method for model selection and more generally hyperparameter selection in machine learning. Minimum description length (MDL) is an established method for model selection, which is however not directly aimed at minimizing generalization error, which is often the primary goal in machine learni…

Paper proposes an unsupervised feature selection algorithm with stability guarantees.

Cluster stability selection improves feature selection in correlated data.

The study evaluates different parameter selection methods for Gaussian process interpolation.

We describe a simple, efficient, permutation based procedure for selecting the penalty parameter in the LASSO. The procedure, which is intended for applications where variable selection is the primary focus, can be applied in a variety of structural settings, including generalized linear models. We briefly discuss conn…

Study improves model estimation and variable selection using GANs with Lasso penalty.

The paper proves generalization bounds and stopping rules for self-selected data in reciprocal learning.

A fast algorithm selects best subsets in high-dimensional models.

New GIC improves model selection for structured sparse models.

Introduces model class selection to find sets of near-optimal models.

ChatGPT selects stocks for investment portfolios, but optimization models improve results.

Transformers excel at sparse token selection, surpassing FCNs in both worst and average cases.

We analyze general model selection procedures using penalized empirical loss minimization under computational constraints. While classical model selection approaches do not consider computational aspects of performing model selection, we argue that any practical model selection procedure must not only trade off estimat…

New model selects uncorrelated and discriminative features for unsupervised feature selection.

This paper improves model selection with cross-validation using domain knowledge.

Proposes a method for stable variable selection in high-dimensional data.

Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features. In this paper, we present a generalized Fisher score to jointly select features. It aims …

Variable selection is of significant importance for classification and regression tasks in machine learning and statistical applications where both predictability and explainability are needed. In this paper, a Copula Entropy (CE) based method for variable selection which use CE based ranks to select variables is propo…

Study on selecting between base algorithms in stochastic bandit problems.

Proposes PEMI for online selective conformal prediction with asymmetric rules.

IWeS selects examples by entropy-based importance sampling for subset selection.

The paper examines domain generalization algorithms and finds empirical risk minimization performs well.

Real networks exhibit nontrivial topological features such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the…

In dictionary selection, several atoms are selected from finite candidates that successfully approximate given data points in the sparse representation. We propose a novel efficient greedy algorithm for dictionary selection. Not only does our algorithm work much faster than the known methods, but it can also handle mor…

This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a target matrix B. The paper then proposes a fast greedy algorithm for solving this problem and draws connections to different problems that ca…