AISTATS 2025
This repository contains the implementations of LITE as well as the experimental setup for the paper. The majority of the code base, including the scripts for running the experiments, can be found in the code directory. For convenience, we additionally provide self-contained code for the fast variant of LITE in flite.py.
If you use the work released here for your research, please cite our paper:
@inproceedings{menet2025lite,
author = {Menet, Nicolas and H{\"u}botter, Jonas and Kassraie, Parnian and Krause, Andreas},
booktitle = {Proc. International Conference on Artificial Intelligence and Statistics (AISTATS)},
pdf = {https://arxiv.org/pdf/2501.13535},
title = {LITE: Efficiently Estimating Gaussian Probability of Maximality},
month = {May},
year = {2025},
organization={PMLR}
}
Copyright (c) 2025 Nicolas Menet, Jonas Hübotter, Parnian Kassraie, Andreas Krause
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