676 citations · 1k across the 35 of their papers we have counts for
9 papers · 1 filter
Integer Programming for Generalized Causal Bootstrap Designs
Jennifer Brennan, Sébastien Lahaie, Adel Javanmard +2
In experimental causal inference, we distinguish between two sources of uncertainty: design uncertainty, due to the treatment assignment mechanism, and sampling uncertainty, when t…
GRASP: A Goodness-of-Fit Test for Classification Learning
Adel Javanmard, Mohammad Mehrabi
Performance of classifiers is often measured in terms of average accuracy on test data. Despite being a standard measure, average accuracy fails in characterizing the fit of the mo…
Pearson Chi-squared Conditional Randomization Test
Adel Javanmard, Mohammad Mehrabi
Conditional independence (CI) testing arises naturally in many scientific problems and applications domains. The goal of this problem is to investigate the conditional independence…
Controlling the False Split Rate in Tree-Based Aggregation
Simeng Shao, Jacob Bien, Adel Javanmard
In many domains, data measurements can naturally be associated with the leaves of a tree, expressing the relationships among these measurements. For example, companies belong to in…
Online Debiasing for Adaptively Collected High-dimensional Data with Applications to Time Series Analysis
Yash Deshpande, Adel Javanmard, Mohammad Mehrabi
Adaptive collection of data is commonplace in applications throughout science and engineering. From the point of view of statistical inference however, adaptive data collection ind…
False Discovery Rate Control via Debiased Lasso
Adel Javanmard, Hamid Javadi
We consider the problem of variable selection in high-dimensional statistical models where the goal is to report a set of variables, out of many predictors , that…
On Online Control of False Discovery Rate
Adel Javanmard, Andrea Montanari
Multiple hypotheses testing is a core problem in statistical inference and arises in almost every scientific field. Given a sequence of null hypotheses $\mathcal{H}(n) = (H_1,...,…
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
Adel Javanmard, Andrea Montanari
Fitting high-dimensional statistical models often requires the use of non-linear parameter estimation procedures. As a consequence, it is generally impossible to obtain an exact ch…
Hypothesis Testing in High-Dimensional Regression under the Gaussian Random Design Model: Asymptotic Theory
Adel Javanmard, Andrea Montanari
We consider linear regression in the high-dimensional regime where the number of observations is smaller than the number of parameters . A very successful approach in this s…