A method to select validation data from a dataset using statistical criteria.
problem Selecting a validation basis from a full dataset for machine learning model validation.
method Adopting a 'design of experiments' point of view and using statistical criteria, particularly Maximum Mean Discrepancy criteria.
result The 'support points' concept is particularly relevant for selecting validation data.
The paper approximates supply curves using a one-step basis method.
problem Computing supply curves accurately and efficiently.
method Derives L2 approximation expression and proposes node selection procedure.
result Illustrates the approach with European electricity market bid curves.
This paper optimizes PCE for efficient surrogate modeling in engineering.
problem Efficiently selecting polynomial regressors for surrogate modeling in computationally expensive models.
method Three state-of-the-art basis-adaptive sparse PCE methods are compared and analyzed.
result Automatic selection of the best solver and basis-adaptive scheme improves surrogate model accuracy.
We consider the problem of designing a sparse Gaussian process classifier (SGPC) that generalizes well. Viewing SGPC design as constructing an additive model like in boosting, we present an efficient and effective SGPC design method to perform a stage-wise optimization of a predictive loss function. We introduce new me…
Method selects interpretable circular coordinates from data.
problem Abstract circular coordinates are hard to interpret.
method Minimum-weight basis problem in vector matroid for selecting interpretable circle-valued coordinates.
result Proves consistency of cochain inner product estimator.
New model uses financial news to predict stock returns.
problem Predicting stock returns based on financial news.
method Derive company embedding vectors from news, select basis assets, and use statistical methods.
result NEUS model outperforms Fama-French 5-factor model.
A new WNN framework selects wavelet bases for efficient learning.
problem Challenges in constructing accurate wavelet bases and high computational costs in WNN.
method Introduces a constructive WNN that selects initial bases and trains functions by introducing new bases for predefined accuracy while reducing computational costs.
result Significantly improves computational efficiency through a frequency estimator and wavelet-basis increase mechanism.
Study automates feature selection and clustering for HFT stock price forecasting.
problem Manual feature selection and clustering for high-frequency trading (HFT) stock price forecasting.
method Dual competitive feature importance mechanism and clustering via shallow neural network topology.
result Enhanced forecasting ability of the RBFNN regressor through automated feature selection and clustering.
New algorithm improves asset pricing model for high-dimensional financial data.
problem Estimating high-dimensional financial data with many risk-factors.
method Groupwise Interpretable Basis Selection (GIBS) algorithm for adaptive multi-factor model.
result AMF model outperforms Fama-French 5-factor model in fitting and prediction.
New model explains how concepts grow based on experience.
problem Existing models assume fixed representation; new model allows for growth.
method Geometric framework with MDL criterion for basis extension.
result Conceptual growth is selective and conservative, exposing or amplifying residual error.
Deep P-Spline automates DNN structure selection for complex regression problems.
problem Challenges in selecting optimal network structures for DNNs.
method Linking neuron selection to knot placement in basis expansion techniques, introducing a difference penalty for automated knot selection.
result Deep P-Spline extends model class and forms a latent variable modeling framework with theoretical guarantees.
A machine learning method selects optimal orthonormal bases for functional data analysis.
problem Lack of formal criteria for choosing initial orthonormal bases in functional data methods.
method Proposes a machine learning algorithm to learn and place knots for efficient orthogonal spline bases (splinets).
result Demonstrates efficiency, especially for sparse functional data and complex physical systems.
ControlBurn selects few features from tree ensembles for better model interpretability.
problem Improving model interpretability in machine learning models.
method Sparse tree ensembles with lasso optimization.
result ControlBurn selects feature-sparse subsets for better model interpretability.
A new knot selection method for GAMs reduces model complexity.
problem Choosing optimal knots for B-spline regression in GAMs.
method Adaptive splines combined with Fellner-Schall tuning for automatic knot selection.
result Comparable performance with P-splines but using fewer knots.
Self-guided ALPs improve MDP policies without domain knowledge.
problem Improving MDP policies with minimal domain knowledge.
method Self-guided sequence of ALPs with random basis functions and state-relevance distribution.
result High probability error bounds and improved policy performance.
Proposes a new model for high-dimensional data analysis with unknown link function.
problem Estimating link function, component functions, and variable interactions in high-dimensional data.
method Generalized Sparse Additive Model with Unknown Link Function (GSAMUL) using B-spline basis and MLP network for link estimation, with ℓ2,1-norm regularizer for variable selection. result Can realize both variable selection and hidden interaction.
New method estimates selection event for black-box models.
problem Infeasibility of conditional selective inference due to unavailable selection event.
method Bootstrapping to estimate selection event and conditional distribution.
result Feasibility of conditional selective inference for models without exact selection event.
An algorithm finds a compact Hankel submatrix for spectral learning.
problem Efficiently computing SVD for large Hankel matrices in spectral learning.
method Maximal bipartite matching algorithm to select rows and columns of Hankel matrix.
result Compact Hankel submatrix with full structural rank.
Decision stumps accurately screen variables in nonparametric models.
problem Challenges in theoretical properties of tree-based variable importance measures.
method Derive performance guarantees for variable selection using a single-level CART decision tree (decision stump).
result Decision stumps can perform consistent model selection despite being inaccurate for estimation.
Isometry pursuit identifies orthonormal submatrices from wide matrices.
problem Identifying isometric embeddings from wide matrices.
method A convex algorithm combining normalization and multitask basis pursuit.
result The method identifies isometric embeddings from interpretable dictionaries.
Significant differences in the evolution of firm size distribution for various industries in the United States have been revealed and documented. For theoretical considerations, this finding puts major constraints on the modelling of firm growth. For practical purposes, the observed differences create a solid basis for…
Superensemble classifier improves predictions in imbalanced datasets.
problem Imbalanced datasets lead to poor performance in minority class examples.
method Maps Hellinger distance decision trees into RBFN framework for universal consistency and parameter optimization.
result The proposed model outperforms state-of-the-art models in various real-life data sets.
FrequentNet uses frequency domain basis vectors for image classification, making models more interpretable and efficient.
problem Image classification models are often complex and hard to interpret.
method FrequentNet selects filter vectors from frequency domain basis vectors instead of training them with back propagation.
result The method improves interpretability and efficiency of image classification models.
Python library automates feature engineering and selection for linear models.
problem Difficulties in training and explaining complex machine learning models.
method Automated feature engineering and selection for linear models.
result Improves prediction accuracy of linear models while retaining interpretability.
Data-aware methods for dimensionality reduction and matrix decomposition aim to find low-dimensional structure in a collection of data. Classical approaches discover such structure by learning a basis that can efficiently express the collection. Recently, "self expression", the idea of using a small subset of data vect…
Paper proposes a new adaptive multiscale value function approximation for reinforcement learning.
problem Value function approximation in reinforcement learning with varying complexity.
method Adaptive multiscale approximation using multiresolution analysis and tree approximation.
result Convergence rate of the multiscale approximation is independent of basis function regularity.
We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated annealing algorithm to optimize radial basis function (RBF) networks. This algorithm enables us to maximize the joint posterior distribution of the network parameters and the number of basis functions. It performs a global search in the joint …
BP fails to find sparsest solution for structured matrices.
problem Finding sparsest solution to linear equations with structured matrices.
method Introduced class of structured matrices for BP failure.
result Determines columns corresponding to unrecoverable non-zero entries.
This paper proposes a method to select bases for spectral learning of PSRs using model entropy.
problem Learning PSR models with limited data and computational resources.
method Adopting model entropy to select columns for spectral learning of PSRs.
result The proposed method can effectively select bases for spectral learning of PSRs.
A new method selects variables efficiently for fast and accurate dynamic system identification.
problem Efficiently selecting variables for scalable Gaussian processes.
method Forward variable selection using Karhunen-Loève decomposition and Gibbs sampling.
result Method yields competitive accuracies and inference times for dynamic systems.
L2SC improves spectral clustering performance by selectively transferring knowledge across tasks.
problem L2SC tackles the challenge of incorporating new spectral clustering tasks without relearning all previous tasks.
method L2SC uses an orthogonal basis library and feature embedding library to selectively transfer knowledge from previously learned tasks to new tasks.
result L2SC outperforms state-of-the-art spectral clustering algorithms on real-world benchmark datasets.
Proposes a method to estimate functional graphical models from multivariate random functions.
problem Estimating conditional independence structure of multivariate random functions.
method Neighborhood selection approach combining function-on-function regression and graph recovery.
result Statistical consistency of the method in high-dimensional settings.
Active learning method reduces labeling cost for regression models with aggregated data.
problem Reducing labeling cost for training regression models with aggregated data.
method Sequentially selects sets to be labeled using mutual information quantifying model parameter uncertainty.
result Achieves better predictive performance with fewer labeled sets.
Signal processing tasks as fundamental as sampling, reconstruction, minimum mean-square error interpolation and prediction can be viewed under the prism of reproducing kernel Hilbert spaces. Endowing this vantage point with contemporary advances in sparsity-aware modeling and processing, promotes the nonparametric basi…
New model explains low-volatility anomaly using adaptive multi-factor approach.
problem Explaining the low-volatility anomaly in stock markets.
method Used Adaptive Multi-Factor (AMF) model with GIBS algorithm to identify significant risk factors.
result Low-volatility portfolios perform better due to loaded risk factors, not just low volatility.
Deep Bayesian neural networks effectively select variables with rigorous uncertainty quantification.
problem High-dimensional variable selection with uncertainty.
method Developed new Bayesian non-parametric theorems for deep BNNs.
result BNNs can learn variable importance effectively in high dimensions and rigorously quantify uncertainty.
Bayesian method identifies dynamical models with uncertainty quantification.
problem Uncertainty in selecting governing equations for dynamical systems.
method Bayesian sparse identification with model averaging.
result Accurately recovers sparse interaction structures with uncertainty quantification.
SURI boosts features with high unique relevant information for better health data analysis.
problem Preserving interpretability in health data analysis.
method Mutual information-based feature selection (MIBFS) method called SURI.
result SURI selects more relevant features leading to higher classification performance.
Abstract notes on robust statistical learning theory.
problem Developing robust estimators for statistical learning.
method Stressing principles of robust estimators construction and analysis.
result Emphasizes main principles of robust estimators construction and analysis.
Algorithm selects variables and bandwidths for geographically weighted regression.
problem Estimating variable subsets and bandwidths for geographically weighted regression.
method Mathematical programming-based approach integrating variable selection and bandwidth estimation.
result Proposed algorithm provides stable spatially varying patterns with competitive explanatory power.
This paper tackles selection bias in recommender systems by considering the neighborhood effect.
problem Selection bias in recommender systems due to filtering and user selection.
method Formalizes neighborhood effect as interference problem, introduces treatment representation, and proposes ideal loss.
result Proposed methods achieve unbiased learning when both selection bias and neighborhood effect are present.
The aim of this chapter is to show how option prices in jump-diffusion models can be computed using meshless methods based on Radial Basis Function (RBF) interpolation. The RBF technique is demonstrated by solving the partial integro-differential equation (PIDE) in one-dimension for the American put and the European va…
Adaptive RBF-KAN improves KANs by dynamically adjusting kernel parameters.
problem Efficiently approximating multivariate functions using learnable univariate edge functions.
method Integrates LOOCV-based kernel scale estimation with adaptive kernel learning.
result Adaptive RBF-KAN outperforms fixed kernel KANs on various benchmark functions.
Weather derivatives help farmers hedge against crop yield risks.
problem High basis risks in weather derivatives pricing models.
method Machine learning ensemble technique to determine yield-weather relationships; mean-reverting model with local temperature dependence.
result Average temperature is the most significant weather variable affecting maize yield.
New ARIMA framework improves forecast accuracy for economic and financial time series.
problem Improving forecast accuracy for nonlinear dynamics in time series data.
method Projection-based ARIMA framework using Galerkin basis expansions.
result Galerkin-SARIMA matches or improves forecast accuracy compared to classical ARIMA/SARIMA.
Optimizes expensive functions using adaptive RBF surrogate model.
problem Global optimization of expensive, possibly non-differentiable functions.
method Adaptive Radial Basis Function (RBF) surrogate model with uncertainty quantification.
result The proposed method identifies optimal points efficiently, especially for non-smooth surfaces.
DoGS improves Gibbs sampling quality with variable selection orders and bounds.
problem Improving Gibbs sampler scan quality.
method Using Dobrushin influence to optimize Gibbs sampling.
result DoGS delivers higher-quality inferences with smaller sampling budgets.
In this work a new way to calculate the multivariate joint entropy is presented. This measure is the basis for a fast information-theoretic based evaluation of gene relevance in a Microarray Gene Expression data context. Its low complexity is based on the reuse of previous computations to calculate current feature rele…