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Found 14 papers, 3 papers with code
Multi-Response Heteroscedastic Gaussian Process Models and Their Inference
By overcoming the limitations of traditional Gaussian process models, our proposed framework offers a robust and versatile tool for a wide array of applications.
Neural Gaussian Mirror for Controlled Feature Selection in Neural Networks
Deep neural networks (DNNs) have become increasingly popular and achieved outstanding performance in predictive tasks.
Measurement error models: from nonparametric methods to deep neural networks
The success of deep learning has inspired recent interests in applying neural networks in statistical inference.
Probabilistic Connection Importance Inference and Lossless Compression of Deep Neural Networks
Deep neural networks (DNNs) can be huge in size, requiring a considerable a mount of energy and computational resources to operate, which limits their applications in numerous scenarios.
False Discovery Rate Control via Data Splitting
Selecting relevant features associated with a given response variable is an important issue in many scientific fields.
Methodology
Minimax Nonparametric Two-sample Test under Smoothing
Under such a framework, the probability density comparison is equivalent to testing the presence/absence of interactions.
The Wang-Landau Algorithm as Stochastic Optimization and Its Acceleration
The optimization formulation provides us a new way to establish the convergence rate of the Wang-Landau algorithm, by exploiting the fact that almost surely, the density estimates (on the logarithmic scale) remain in a compact set, upon which the objective function is strongly convex.
Generative Parameter Sampler For Scalable Uncertainty Quantification
Uncertainty quantification has been a core of the statistical machine learning, but its computational bottleneck has been a serious challenge for both Bayesians and frequentists.
Sentence Segmentation for Classical Chinese Based on LSTM with Radical Embedding
In this paper, we develop a low than character feature embedding called radical embedding, and apply it on LSTM model for sentence segmentation of pre modern Chinese texts.
IMMIGRATE: A Margin-based Feature Selection Method with Interaction Terms
Relief based algorithms have often been claimed to uncover feature interactions.
L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs
In the present paper we analyze algorithms based on covariance screening and least squares with $L_1$ penalization (i. e. LASSO) and demonstrate that they can also enjoy optimal (up to a scalar) rescaled sample size in terms of support recovery, albeit under slightly different assumptions on $f$ and $\varepsilon$ compared to the SIR based algorithms.
Signed Support Recovery for Single Index Models in High-Dimensions
When $s=O(p^{1-\delta})$ for some $\delta>0$, we demonstrate that both procedures can succeed in recovering the support of $\boldsymbol{\beta}$ as long as the rescaled sample size $\kappa=\frac{n}{s\log(p-s)}$ is larger than a certain critical threshold.
A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations
Our main theoretical contribution is to establish a unified Z-estimation theory of confidence regions for high dimensional problems.
Interpretable Selection and Visualization of Features and Interactions Using Bayesian Forests
In many practical applications, it is of interest which features and feature interactions are relevant to the prediction task.
