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Found 19 papers, 5 papers with code
A Flexible Framework for Nonparametric Graphical Modeling that Accommodates Machine Learning
In addition, we show that for 2 of these measures there are simple, strong plug-in estimators that require only the estimation of a conditional mean.
Mesh-Based Solutions for Nonparametric Penalized Regression
It is often of interest to estimate regression functions non-parametrically.
The Future will be Different than Today: Model Evaluation Considerations when Developing Translational Clinical Biomarker
Finding translational biomarkers stands center stage of the future of personalized medicine in healthcare.
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure
Originally developed for imputing missing entries in low rank, or approximately low rank matrices, matrix completion has proven widely effective in many problems where there is no reason to assume low-dimensional linear structure in the underlying matrix, as would be imposed by rank constraints.
Ensembled sparse-input hierarchical networks for high-dimensional datasets
Neural networks have seen limited use in prediction for high-dimensional data with small sample sizes, because they tend to overfit and require tuning many more hyperparameters than existing off-the-shelf machine learning methods.
Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep
Successful deployment of machine learning algorithms in healthcare requires careful assessments of their performance and safety.
Selective prediction-set models with coverage guarantees
Though black-box predictors are state-of-the-art for many complex tasks, they often fail to properly quantify predictive uncertainty and may provide inappropriate predictions for unfamiliar data.
An analysis of the cost of hyper-parameter selection via split-sample validation, with applications to penalized regression
We establish that the fitted models are Lipschitz in the penalty parameters and thus our oracle inequalities apply.
Wavelet regression and additive models for irregularly spaced data
We prove minimax optimal convergence rates under a weak compatibility condition for sparse additive models.
Generalized Sparse Additive Models
We present a unified framework for estimation and analysis of generalized additive models in high dimensions.
Nonparametric variable importance using an augmented neural network with multi-task learning
In predictive modeling applications, it is often of interest to determine the relative contribution of subsets of features in explaining the variability of an outcome.
Sparse-Input Neural Networks for High-dimensional Nonparametric Regression and Classification
In addition, we characterize the statistical convergence of the penalized empirical risk minimizer to the optimal neural network: we show that the excess risk of this penalized estimator only grows with the logarithm of the number of input features; and we show that the weights of irrelevant features converge to zero.
Gradient-based Regularization Parameter Selection for Problems with Non-smooth Penalty Functions
It is more efficient to tune parameters if the gradient can be determined, but this is often difficult for problems with non-smooth penalty functions.
Nonparametric Regression with Adaptive Truncation via a Convex Hierarchical Penalty
We further establish minimax rates for a large class of sparse additive models.
Convex Modeling of Interactions with Strong Heredity
We consider the task of fitting a regression model involving interactions among a potentially large set of covariates, in which we wish to enforce strong heredity.
Fused Lasso Additive Model
We consider the problem of predicting an outcome variable using $p$ covariates that are measured on $n$ independent observations, in the setting in which flexible and interpretable fits are desirable.
Selection Bias Correction and Effect Size Estimation under Dependence
Many authors have proposed methods to reduce the effects of selection bias under the assumption that the naive estimates of the effect sizes are independent.
A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression
In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression.
On Estimating Many Means, Selection Bias, and the Bootstrap
With recent advances in high throughput technology, researchers often find themselves running a large number of hypothesis tests (thousands+) and esti- mating a large number of effect-sizes.
