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Found 13 papers, 8 papers with code
Smoothing the Edges: A General Framework for Smooth Optimization in Sparse Regularization using Hadamard Overparametrization
This is particularly useful in non-convex regularization, where finding global solutions is NP-hard and local minima often generalize well.
Factorized Structured Regression for Large-Scale Varying Coefficient Models
Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models.
Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches
In a high-throughput mouse phenotyping environment, both methods perform well as part of an automated end-to-end screening pipeline to detect candidate genes for hearing involvement.
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.
STENCIL-NET: Data-driven solution-adaptive discretization of partial differential equations
Often, this requires high-resolution or adaptive discretization grids to capture relevant spatio-temporal features in the PDE solution, e. g., in applications like turbulence, combustion, and shock propagation.
Learning physically consistent mathematical models from data using group sparsity
We propose a statistical learning framework based on group-sparse regression that can be used to 1) enforce conservation laws, 2) ensure model equivalence, and 3) guarantee symmetries when learning or inferring differential-equation models from measurement data.
c-lasso -- a Python package for constrained sparse and robust regression and classification
The underlying statistical forward model is assumed to be of the following form: \[ y = X \beta + \sigma \epsilon \qquad \textrm{subject to} \qquad C\beta=0 \] Here, $X \in \mathbb{R}^{n\times d}$is a given design matrix and the vector $y \in \mathbb{R}^{n}$ is a continuous or binary response vector.
Fast computation of latent correlations
Latent Gaussian copula models provide a powerful means to perform multi-view data integration since these models can seamlessly express dependencies between mixed variable types (binary, continuous, zero-inflated) via latent Gaussian correlations.
Computation Methodology
Stability selection enables robust learning of partial differential equations from limited noisy data
We show that in particular the combination of stability selection with the iterative hard-thresholding algorithm from compressed sensing provides a fast, parameter-free, and robust computational framework for PDE inference that outperforms previous algorithmic approaches with respect to recovery accuracy, amount of data required, and robustness to noise.
Regression models for compositional data: General log-contrast formulations, proximal optimization, and microbiome data applications
This model describes the response as a linear combination of log-ratios of the original compositions and has been extended to the high-dimensional setting via regularization.
Statistics Theory Statistics Theory
Perspective Maximum Likelihood-Type Estimation via Proximal Decomposition
We introduce an optimization model for maximum likelihood-type estimation (M-estimation) that generalizes a large class of existing statistical models, including Huber's concomitant M-estimator, Owen's Huber/Berhu concomitant estimator, the scaled lasso, support vector machine regression, and penalized estimation with structured sparsity.
Statistics Theory Statistics Theory
Generalized Stability Approach for Regularized Graphical Models
Selecting regularization parameters in penalized high-dimensional graphical models in a principled, data-driven, and computationally efficient manner continues to be one of the key challenges in high-dimensional statistics.
Methodology Molecular Networks Applications Computation
