48 citations · 56 across the 6 of their papers we have counts for
6 papers
A (tight) upper bound for the length of confidence intervals with conditional coverage
Danijel Kivaranovic, Hannes Leeb
We show that two popular selective inference procedures, namely data carving (Fithian et al., 2017) and selection with a randomized response (Tian et al., 2018b), when combined wit…
Conformal prediction intervals for the individual treatment effect
Danijel Kivaranovic, Robin Ristl, Martin Posch +1
We propose several prediction intervals procedures for the individual treatment effect with either finite-sample or asymptotic coverage guarantee in a non-parametric regression set…
Adaptive, Distribution-Free Prediction Intervals for Deep Networks
Danijel Kivaranovic, Kory D. Johnson, Hannes Leeb
The machine learning literature contains several constructions for prediction intervals that are intuitively reasonable but ultimately ad-hoc in that they do not come with provable…
The Oracle of DLphi
Dominik Alfke, Weston Baines, Jan Blechschmidt +24
We present a novel technique based on deep learning and set theory which yields exceptional classification and prediction results. Having access to a sufficiently large amount of l…
Discussion on "Model Confidence Bounds for Variable Selection" by Yang Li, Yuetian Luo, Davide Ferrari, Xiaonan Hu, and Yichen Qin
Hannes Leeb, Benedikt M. Pötscher, Danijel Kivaranovic
This is a comment on the article mentioned in the title.
On the length of post-model-selection confidence intervals conditional on polyhedral constraints
Danijel Kivaranovic, Hannes Leeb
Valid inference after model selection is currently a very active area of research. The polyhedral method, pioneered by Lee, et al. (2016), allows for valid inference after model se…