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Found 15 papers, 7 papers with code
Functional response regression with funBART: an analysis of patient-specific stillbirth risk
This article introduces functional BART, a new approach for functional response regression--that is, estimating a functional mean response f(t) that depends upon a set of scalar covariates x. Functional BART, or funBART, is based on the successful Bayesian Additive Regression Trees (BART) model.
Methodology
Interpretable Low-Dimensional Regression via Data-Adaptive Smoothing
We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance.
GapTV: Accurate and Interpretable Low-Dimensional Regression and Classification
We consider the problem of estimating a regression function in the common situation where the number of features is small, where interpretability of the model is a high priority, and where simple linear or additive models fail to provide adequate performance.
Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioning
We present an approach to deep estimation of discrete conditional probability distributions.
Diet2Vec: Multi-scale analysis of massive dietary data
Smart phone apps that enable users to easily track their diets have become widespread in the last decade.
Better Conditional Density Estimation for Neural Networks
CDE Trend Filtering applies a k-th order graph trend filtering penalty to the unnormalized logits of a multinomial classifier network, with each edge in the graph corresponding to a neighboring point on a discretized version of the density.
A Fast and Flexible Algorithm for the Graph-Fused Lasso
We propose a new algorithm for solving the graph-fused lasso (GFL), a method for parameter estimation that operates under the assumption that the signal tends to be locally constant over a predefined graph structure.
Tensor decomposition with generalized lasso penalties
We present an approach for penalized tensor decomposition (PTD) that estimates smoothly varying latent factors in multi-way data.
Proximal Algorithms in Statistics and Machine Learning
We provide a discussion of convergence of non-descent algorithms with acceleration and for non-convex functions.
False discovery rate smoothing
We also apply the method to a data set from an fMRI experiment on spatial working memory, where it detects patterns that are much more biologically plausible than those detected by standard FDR-controlling methods.
Methodology Applications Computation
Sampling Polya-Gamma random variates: alternate and approximate techniques
Efficiently sampling from the P\'olya-Gamma distribution, ${PG}(b, z)$, is an essential element of P\'olya-Gamma data augmentation.
Computation
Priors for Random Count Matrices Derived from a Family of Negative Binomial Processes
We define a family of probability distributions for random count matrices with a potentially unbounded number of rows and columns.
False discovery rate regression: an application to neural synchrony detection in primary visual cortex
But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment.
Methodology Applications
Expectation-maximization for logistic regression
We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000).
Bayesian inference for logistic models using Polya-Gamma latent variables
We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods.
