Nov 15, 2021 · We introduce Compress++, a simple meta-procedure for speeding up any thinning algorithm while suffering at most a factor of 4 in error.
Jan 28, 2022 · This paper proposes a simple meta algorithm to speed up data thinning algorithms with good theoretical guarantees.
Apr 1, 2022 · In distribution compression, one aims to accurately summarize a probability distribution P using a small number of representative points.
COMPRESS++ can also be directly combined with any thinning algorithm, even those that have suboptimal guarantees but often perform well in practice, like kernel ...
[PDF] Distribution Compression in Near-linear Time - Semantic Scholar
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Supervised Kernel Thinning · Efficient and Accurate Explanation Estimation with Distribution Compression · Compress Then Test: Powerful Kernel Testing in Near- ...
Dec 1, 2023 · Finally, Compress++ converts any unbiased quadratic-time thinning algorithm into a near-linear-time algorithm with comparable error. Based on ...
Mackey. Compress then test: Powerful kernel testing in near-linear time. In Proceedings of The 26th International. Conference on Artificial Intelligence and ...
Jan 1, 2022 · Shetty, Abhishek, Dwivedi, Raaz, and Mackey, Lester. "Distribution Compression in Near-linear Time". Tenth International Conference on Learning ...
Aug 25, 2023 · This talk will introduce two new tools for summarizing a probability distribution more effectively than independent sampling or standard Markov chain Monte ...
GoodPoints is a collection of tools for compressing a distribution more effectively than independent sampling.