Search Results for author:
Found 17 papers, 11 papers with code
Targeted active learning for probabilistic models
A fundamental task in science is to design experiments that yield valuable insights about the system under study.
DIET: Conditional independence testing with marginal dependence measures of residual information
DIET tests the marginal independence of two random variables: $F(x \mid z)$ and $F(y \mid z)$ where $F(\cdot \mid z)$ is a conditional cumulative distribution function (CDF).
Quantile regression with deep ReLU Networks: Estimators and minimax rates
Overall, the theoretical and empirical results provide insight into the strong performance of ReLU neural networks for quantile regression across a broad range of function classes and error distributions.
Deep Direct Likelihood Knockoffs
Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance.
A Bayesian Model of Dose-Response for Cancer Drug Studies
The goal in each paired (cell line, drug) experiment is to map out the dose-response curve of the cell line as the dose level of the drug increases.
Interpreting Black Box Models via Hypothesis Testing
In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments.
Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach
Personalized cancer treatments based on the molecular profile of a patient's tumor are an emerging and exciting class of treatments in oncology.
Applications
The Holdout Randomization Test for Feature Selection in Black Box Models
We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models.
Methodology
Black Box FDR
BB-FDR learns a series of black box predictive models to boost power and control the false discovery rate (FDR) at two stages of study analysis.
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.
Deep Nonparametric Estimation of Discrete Conditional Distributions via Smoothed Dyadic Partitioning
We present an approach to deep estimation of discrete conditional probability distributions.
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.
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.
Vector-Space Markov Random Fields via Exponential Families
Specifically, VS-MRFs are the joint graphical model distributions where the node-conditional distributions belong to generic exponential families with general vector space domains.
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
