A hybrid model for Bayesian optimization handles mixed variables using MCTS for categorical and GP for continuous.
problem Optimizing functions with mixed variable types (continuous, integer, categorical).
method Merges MCTS for categorical and GP for continuous variables, integrates UCTS search strategy, and dynamically selects kernels.
result Hybrid models outperform traditional methods in Bayesian optimization.
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.
Dynamic assessment of patient status (e.g. by an automated, continuously updated assessment of outcome) in the Intensive Care Unit (ICU) is of paramount importance for early alerting, decision support and resource allocation. Extraction and cleaning of expert-selected clinical variables discards information and protrac…
Estimates and infers multi-stage stationary treatment policies with variable selection.
problem Valid inference for multi-stage stationary treatment policies with high-dimensional feature variables.
method Estimate the value function using augmented inverse probability weighted estimator, apply penalty for variable selection, construct one-step improvements for valid inference.
result Improved estimators are asymptotically normal, valid inference for policy parameters demonstrated.
A fast, approximate method for variable selection in GLMs tackles correlated data.
problem Variable selection in generalized linear models with correlated data.
method Replica method of statistical mechanics and vector approximate message passing.
result The proposed algorithm provides fast convergence and high approximation accuracy.
Stock selection improved with a novel neural model capturing continuous stock dynamics.
problem Lack of continuous stock dynamics prediction and implicit cross-domain dependencies.
method StockODE, a latent variable model with NRODEs and hierarchical hypergraph for continuous stock volatility and inter-domain dependencies.
result Significantly outperforms baselines, improving Sharpe Ratio by up to 18.57%.
In this paper we propose a computationally efficient algorithm for on-line variable selection in multivariate regression problems involving high dimensional data streams. The algorithm recursively extracts all the latent factors of a partial least squares solution and selects the most important variables for each facto…
This paper improves volatility forecasting using dynamic subset selection in genetic programming.
problem Improving accuracy of implied volatility forecasting.
method Dynamic training-subset selection methods applied to genetic programming.
result Dynamic subset selection improves predictive accuracy of genetic programming models.
New method improves Bayesian model selection for neural dynamics.
problem Bayesian methods struggle with large datasets and computational constraints.
method Computation-Aware State-Space Model (CASSM) with model selection.
result CASSM yields competitive performance and improved uncertainty calibration.
A novel Bayesian method for dynamic sparsity in Gaussian dynamic linear regression.
problem Variable selection and shrinkage in time-varying regression models.
method Time-varying sparsity via Markov switching priors for coefficients' variances, extending spike-and-slab priors.
result Induces smoothness or shrinkage towards zero at each time point, leading to improved model performance.
Algorithm selects best model based on state, reducing costs.
problem Choosing the best model among many in different states of the world.
method Reinforcement learning algorithm to estimate optimal policy.
result Algorithm consistently selects optimal model based on covariates.
Paper corrects bias in online learning algorithms with endogenous data.
problem Dynamic selection problems in online learning algorithms with endogenous data.
method Instrumental-variable-based algorithm to correct bias, proving central limit theorem.
result Obtains true parameter values and low regret levels.
Data-driven spatial filtering algorithms optimize scores such as the contrast between two conditions to extract oscillatory brain signal components. Most machine learning approaches for filter estimation, however, disregard within-trial temporal dynamics and are extremely sensitive to changes in training data and invol…
DFR models dynamic distributional data with weighted Fréchet means.
problem Regression of distribution-valued responses over time.
method Dynamic Fréchet Regression (DFR) with index-aware weighting and feature selection.
result Improved predictive accuracy and feature recovery over existing methods.
EXFormer predicts foreign exchange returns with high accuracy using a multi-scale self-attention mechanism and dynamic variable selection.
problem Accurately forecasting daily exchange rate returns in international finance.
method EXFormer uses a multi-scale trend-aware self-attention mechanism with dynamic variable selection and embedded squeeze-and-excitation blocks.
result EXFormer outperforms other models in forecasting daily exchange rate returns, achieving statistically significant improvements in directional accuracy.
Develops methods to identify and estimate causal effects with instrumental variables.
problem Causal inference with confounded treatment assignment and unobserved variables.
method General nonparametric causal framework, debiased machine learning, semiparametric theory.
result Consistent and asymptotically normal estimators for average treatment effect.
New CGMD model predicts non-equilibrium processes better than existing methods.
problem Inconsistency in conditional distribution of unresolved variables.
method Time-lagged independent component analysis to minimize entropy contribution of unresolved variables.
result The model's generalization ability for non-equilibrium processes is significantly improved.
We develop a novel "decouple-recouple" dynamic predictive strategy and contribute to the literature on forecasting and economic decision making in a data-rich environment. Under this framework, clusters of predictors generate different latent states in the form of predictive densities that are later synthesized within …
The paper solves multi-period portfolio selection with constraints using a dynamic factor model.
problem Multi-period mean-variance portfolio selection with constraints.
method Dynamic factor model, dynamic programming, piecewise linear feedback policy.
result Optimal portfolio policies determined by two stochastic processes.
SEEK algorithm selects minimal state in reinforcement learning for better policy learning.
problem Challenges in obtaining a state representation that is parsimonious and satisfies the Markov property.
method SEEK algorithm estimates the minimal sufficient state in reinforcement learning.
result The SEEK algorithm achieves selection consistency in large samples.
A new method combines predictors and their lags using supervised PCA for dynamic forecasting.
problem Dynamic forecasting with many predictors.
method Supervised PCA with re-scaling and penalized methods.
result The method outperforms traditional PCA and diffusion-index approaches in prediction.
Proposes a new signal model for high-dimensional, small-sample-size data.
problem Signal detection in high-dimensional, small-sample-size datasets.
method Intrinsic signal model based on dynamical system assumption.
result Taguchi method effectively detects signals in the proposed model.
Unified algorithm for efficient pure exploration using dual variables.
problem Efficiently achieving a specific goal through adaptive experimentation.
method Introducing dual variables to derive optimal allocation conditions, leading to Information-Directed Selection.
result Top-two Thompson sampling attains asymptotic optimality for Gaussian best-arm identification.
AFS-BM improves model accuracy by dynamically selecting features.
problem Feature selection challenges in ML, especially scalability and adaptability.
method Joint optimization for feature selection and model training with binary masking.
result AFS-BM achieves significant improvements in model accuracy and computational efficiency.
Study optimal adjustment sets for causal policies with hidden variables.
problem Estimating dynamic treatment regimes with hidden variables.
method Developed criteria for graphs without hidden variables to compare estimators, extended to dynamic policies and hidden variables.
result Existence and computation of optimal minimal and globally optimal adjustment sets.
Large bundles of myelinated axons, called white matter, anatomically connect disparate brain regions together and compose the structural core of the human connectome. We recently proposed a method of measuring the local integrity along the length of each white matter fascicle, termed the local connectome. If communicat…
Proposes a regularization approach to model German power derivative market, identifying significant risk spillovers.
problem Large portfolio of German power derivative contracts, identifying significant risk spillovers.
method Combines high-dimensional variable selection with dynamic network analysis.
result Identifies significant risk contributors and interdependencies between contracts, especially spot contracts.
Paper improves k-NN predictive performance with efficient variable selection.
problem Improving predictive performance of k-NN models. method Efficient forward selection of predictor variables.
result Novel approach approaches outperformance of stepwise selection models.
New metric improves latent dynamics inference from neural data.
problem Limitations of co-smoothing in predicting latent dynamics.
method Few-shot co-smoothing to assess latent dynamics.
result High co-smoothing models often have extraneous dynamics, which few-shot co-smoothing detects.
Solar algorithm selects variables faster and more accurately in high-dimensional data.
problem Variable selection in high-dimensional data with high accuracy and stability.
method Subsample-ordered least-angle regression (solar) and its coordinate descent generalization (solar-cd) using L0 norm solution path averaging. result Solar selects variables with high accuracy and stability, reducing redundant variable selection.
A new method selects important variables for clustering from dependency networks.
problem Variable selection for clustering in high-cost data scenarios.
method Create dependency networks, rank variables by centrality, select top-n variables.
result Top-n variables improve clustering performance compared to existing methods.
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…
This paper reviews deep learning methods for state space models.
problem Analyzing temporal dynamics in dynamical systems.
method Selective review of deep neural network approaches for state space models.
result Unified perspective on discrete and continuous time SSMs.
Proposes a two-stage method for selecting correlated predictors in high-dimensional data.
problem Selecting correlated predictors in high-dimensional data with unknown group structures.
method Two-stage approach: variable clustering followed by group selection.
result The two-stage method improves prediction accuracy and active predictor selection.
The paper develops a test for independence of selected Gaussian variables after thresholding correlations.
problem Testing independence of selected Gaussian variables after thresholding correlations.
method The approach involves conditioning on the selection event and using a new characterization of the conditioning event in terms of canonical correlation.
result The proposed test has higher power than a naive approach that ignores selection effects.
Fourier Neural Operators accurately predict dynamics of high-dimensional ionic models.
problem Approximating stiff, multiscale ionic models using neural networks.
method Fourier Neural Operators for learning dynamics of high-dimensional ionic models.
result Fourier Neural Operators can accurately predict dynamics of high-dimensional ionic models.
VC-PCR improves prediction by clustering correlated variables.
problem Decreased prediction accuracy due to cluster structure in predictor variables.
method Supervised variable selection and clustering to integrate cluster information into a sparse modeling process.
result VC-PCR achieves better prediction, variable selection, and clustering performance.
Variable selection for Gaussian process models is often done using automatic relevance determination, which uses the inverse length-scale parameter of each input variable as a proxy for variable relevance. This implicitly determined relevance has several drawbacks that prevent the selection of optimal input variables i…
Private variable selection method controls FDR with simulations showing reasonable power.
problem Performing variable selection with privacy constraints.
method Private knockoff filter using Gaussian and Laplace mechanisms.
result Achieves controlled false discovery rate (FDR) in variable selection.
New method selects features for sequential decision making.
problem Dynamic feature selection for instance-wise decisions.
method Latent variable model trained in a supervised manner; reasoning across stochastic latent space.
result Outperforms existing methods on various datasets.
Random forest hyperparameters affect variable selection in omics studies.
problem Impact of hyperparameters on variable selection in random forests.
method Two simulation studies using theoretical and empirical data.
result Hyperparameters influence variable selection more than the splitting strategy and sample fraction.
Study short-term wind power and speed predictions using machine learning.
problem Accurate short-term wind power and speed predictions for energy systems.
method Combining numerical weather prediction models with local observations, using machine learning for variable selection and forecasting.
result Improved wind power and speed predictions for 4-hour ahead using machine learning.
Study improves model estimation and variable selection using GANs with Lasso penalty.
problem Variable selection in high-dimensional data with deep networks.
method Conditional Wasserstein Generative Adversarial Networks with Group Lasso penalization.
result Established convergence rate for variable selection in censored survival data.
Variable selection for optimal treatment regime in a clinical trial or an observational study is getting more attention. Most existing variable selection techniques focused on selecting variables that are important for prediction, therefore some variables that are poor in prediction but are critical for decision-making…
Efficient Bayesian variable selection for binomial and negative binomial data.
problem Computational challenges in Bayesian variable selection for complex models.
method Tempered Gibbs Sampling and MCMC scheme.
result Demonstrated effectiveness on cancer data with thousands of covariates.
New algorithm solves online resource allocation problems efficiently.
problem Dynamic resource allocation in operations research.
method Minimal Selection Principle and MSoE algorithm.
result Ensures optimal cumulative regret bounds in dynamic resource allocation.
Knoop enhances variable selection with over-parameterization and knockoffs.
problem Challenges of variable selection in high-dimensional datasets.
method Generates knockoff variables, integrates them into an over-parameterized model, and uses anomaly-based significance tests.
result Superior performance in variable selection compared to existing methods.
Enhances RSCNs with hybrid regularization for nonlinear dynamics.
problem Modeling nonlinear dynamic systems with uncertainties.
method Recurrent stochastic configuration networks with hybrid regularization.
result The method outperforms other models in nonlinear system identification and industrial tasks.