Variable Selection
127 papers with code • 0 benchmarks • 0 datasets
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Structured Learning in Time-dependent Cox Models
We propose a flexible framework for variable selection in time-dependent Cox models, accommodating complex selection rules.
TreeDQN: Learning to minimize Branch-and-Bound tree
Combinatorial optimization problems require an exhaustive search to find the optimal solution.
Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
larselund/sparsifying-bnns-with-lrt-and-nf
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In this paper, we will consider two extensions to the LBBNN method: Firstly, by using the local reparametrization trick (LRT) to sample the hidden units directly, we get a more computationally efficient algorithm.
System Identification with Copula Entropy
In this paper we propose a method for identifying differential equation of dynamical systems with CE.
Synthesize High-dimensional Longitudinal Electronic Health Records via Hierarchical Autoregressive Language Model
btheodorou99/halo_inpatient
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In this paper, we propose Hierarchical Autoregressive Language mOdel (HALO) for generating longitudinal high-dimensional EHR, which preserve the statistical properties of real EHR and can be used to train accurate ML models without privacy concerns.
Dual-sPLS: a family of Dual Sparse Partial Least Squares regressions for feature selection and prediction with tunable sparsity; evaluation on simulated and near-infrared (NIR) data
A quantitative prediction objective can be enriched by qualitative data interpretation, for instance by locating the most influential features.
Learning a Generic Value-Selection Heuristic Inside a Constraint Programming Solver
Important design choices in a solver are the branching heuristics, which are designed to lead the search to the best solutions in a minimum amount of time.
Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach
Allowing the regression coefficients of tasks to have different sparsity patterns (i. e., different supports), we propose a modeling framework for MTL that encourages models to share information across tasks, for a given covariate, through separately 1) shrinking the coefficient supports together, and/or 2) shrinking the coefficient values together.
RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
mpotto/pyselect
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Kernel methods provide a flexible and theoretically grounded approach to nonlinear and nonparametric learning.
Near-optimal multiple testing in Bayesian linear models with finite-sample FDR control
In this paper, we develop near-optimal multiple testing procedures for high dimensional Bayesian linear models with isotropic covariates.
