# Performance of Distribution Regression with Doubling Measure under the seek of Closest Point

arXiv: https://arxiv.org/abs/2203.00155 · HTML: https://mlsanity.com/paper/2203.00155/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Distribution-Free Distribution Regression](https://mlsanity.com/paper/1302.0082/) (2013)
- [Low-Rank Matrix and Tensor Completion via Adaptive Sampling](https://mlsanity.com/paper/1304.4672/) (2013)
- [An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments](https://mlsanity.com/paper/2106.15609/) (2021)
- [Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets](https://mlsanity.com/paper/2002.08528/) (2020)

## Built on by (most cited first, 2 of 2)

- [Lifelong Matrix Completion with Sparsity-Number](https://mlsanity.com/paper/2203.07637/) (2022)
- [Adaptive Noisy Matrix Completion](https://mlsanity.com/paper/2203.08340/) (2022)

## Abstract (arXiv)

We study the distribution regression problem assuming the distribution of distributions has a doubling measure larger than one. First, we explore the geometry of any distributions that has doubling measure larger than one and build a small theory around it. Then, we show how to utilize this theory to find one of the nearest distributions adaptively and compute the regression value based on these distributions. Finally, we provide the accuracy of the suggested method here and provide the theoretical analysis for it.