# Distributed Learning with Dependent Samples

arXiv: https://arxiv.org/abs/2002.03757 · HTML: https://mlsanity.com/paper/2002.03757/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Distributed Learning based on 1-Bit Gradient Coding in the Presence of Stragglers](https://mlsanity.com/paper/2403.14716/) (2024)
- [Nystr"om Regularization for Time Series Forecasting](https://mlsanity.com/paper/2111.07109/) (2021)
- [Distributed Gradient Descent for Functional Learning](https://mlsanity.com/paper/2305.07408/) (2023)
- [Theory of Decentralized Robust Kernel-Based Learning](https://mlsanity.com/paper/2506.05215/) (2025)
- [Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach](https://mlsanity.com/paper/2603.02906/) (2026)

## Abstract (arXiv)

This paper focuses on learning rate analysis of distributed kernel ridge regression for strong mixing sequences. Using a recently developed integral operator approach and a classical covariance inequality for Banach-valued strong mixing sequences, we succeed in deriving optimal learning rate for distributed kernel ridge regression. As a byproduct, we also deduce a sufficient condition for the mixing property to guarantee the optimal learning rates for kernel ridge regression. Our results extend the applicable range of distributed learning from i.i.d. samples to non-i.i.d. sequences.