34 citations · 93 across the 10 of their papers we have counts for
10 papers
Inference after black box selection
Jelena Markovic, Jonathan Taylor, Jeremy Taylor
We consider the problem of inference for parameters selected to report only after some algorithm, the canonical example being inference for model parameters after a model selection…
Bouncy Hybrid Sampler as a Unifying Device
Jelena Markovic, Amir Sepehri
This work introduces a class of rejection-free Markov chain Monte Carlo (MCMC) samplers, named the Bouncy Hybrid Sampler, which unifies several existing methods from the literature…
More powerful post-selection inference, with application to the Lasso
Keli Liu, Jelena Markovic, Robert Tibshirani
Investigators often use the data to generate interesting hypotheses and then perform inference for the generated hypotheses. P-values and confidence intervals must account for this…
Non-reversible, tuning- and rejection-free Markov chain Monte Carlo via iterated random functions
Amir Sepehri, Jelena Markovic
In this work we present a non-reversible, tuning- and rejection-free Markov chain Monte Carlo which naturally fits in the framework of hit-and-run. The sampler only requires access…
Inferactive data analysis
Nan Bi, Jelena Markovic, Lucy Xia +1
We describe inferactive data analysis, so-named to denote an interactive approach to data analysis with an emphasis on inference after data analysis. Our approach is a compromise b…
Unifying approach to selective inference with applications to cross-validation
Jelena Markovic, Lucy Xia, Jonathan Taylor
We develop tools to do valid post-selective inference for a family of model selection procedures, including choosing a model via cross-validated Lasso. The tools apply universally…
An MCMC-free approach to post-selective inference
Snigdha Panigrahi, Jelena Markovic, Jonathan Taylor
We develop a Monte Carlo-free approach to inference post output from randomized algorithms with a convex loss and a convex penalty. The pivotal statistic based on a truncated law,…
Bootstrap inference after using multiple queries for model selection
Jelena Markovic, Jonathan Taylor
In this work, we provide a refinement of the selective CLT result of Tian and Taylor (2015), which allows for selective inference in non-parametric settings by adjusting for the as…
Selective sampling after solving a convex problem
Xiaoying Tian Harris, Snigdha Panigrahi, Jelena Markovic +2
We consider the problem of selective inference after solving a (randomized) convex statistical learning program in the form of a penalized or constrained loss function. Our first m…
Estimating the Ratio of Two Functions in a Nonparametric Regression Model
Jelena Markovic, Lie Wang
Due to measurement noise, a common problem in in various fields is how to estimate the ratio of two functions. We consider this problem of estimating the ratio of two functions in…