# Distribution-Free One-Pass Learning

arXiv: https://arxiv.org/abs/1706.02471 · HTML: https://mlsanity.com/paper/1706.02471/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2017.

## Built on by (most cited first, 10 of 10)

- [Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization](https://mlsanity.com/paper/2112.14368/) (2021)
- [A Simple Approach for Non-stationary Linear Bandits](https://mlsanity.com/paper/2103.05324/) (2021)
- [Open-environment Machine Learning](https://mlsanity.com/paper/2206.00423/) (2022)
- [Non-stationary Online Learning with Memory and Non-stochastic Control](https://mlsanity.com/paper/2102.03758/) (2021)
- [ORFit: One-Pass Learning via Bridging Orthogonal Gradient Descent and Recursive Least-Squares](https://mlsanity.com/paper/2207.13853/) (2022)
- [Revisiting Weighted Strategy for Non-stationary Parametric Bandits and MDPs](https://mlsanity.com/paper/2601.01069/) (2026)
- [A Generalized Unbiased Risk Estimator for Learning with Augmented Classes](https://mlsanity.com/paper/2306.06894/) (2023)
- [Distributed Management of Fluctuating Energy Resources in Dynamic Networked Systems](https://mlsanity.com/paper/2405.19015/) (2024)
- [Revisiting Weighted Strategy for Non-stationary Parametric Bandits](https://mlsanity.com/paper/2303.02691/) (2023)
- [Theoretically Guaranteed Distribution Adaptable Learning](https://mlsanity.com/paper/2411.02921/) (2024)

## Abstract (arXiv)

In many large-scale machine learning applications, data are accumulated with time, and thus, an appropriate model should be able to update in an online paradigm. Moreover, as the whole data volume is unknown when constructing the model, it is desired to scan each data item only once with a storage independent with the data volume. It is also noteworthy that the distribution underlying may change during the data accumulation procedure. To handle such tasks, in this paper we propose DFOP, a distribution-free one-pass learning approach. This approach works well when distribution change occurs during data accumulation, without requiring prior knowledge about the change. Every data item can be discarded once it has been scanned. Besides, theoretical guarantee shows that the estimate error, under a mild assumption, decreases until convergence with high probability. The performance of DFOP for both regression and classification are validated in experiments.