# On statistics, computation and scalability

arXiv: https://arxiv.org/abs/1309.7804 · HTML: https://mlsanity.com/paper/1309.7804/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2013.

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

- [Scalable Subsampling Inference for Deep Neural Networks](https://mlsanity.com/paper/2405.08276/) (2024)
- [Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data](https://mlsanity.com/paper/2303.08242/) (2023)

## Abstract (arXiv)

How should statistical procedures be designed so as to be scalable computationally to the massive datasets that are increasingly the norm? When coupled with the requirement that an answer to an inferential question be delivered within a certain time budget, this question has significant repercussions for the field of statistics. With the goal of identifying "time-data tradeoffs," we investigate some of the statistical consequences of computational perspectives on scability, in particular divide-and-conquer methodology and hierarchies of convex relaxations.