# Decentralized Parameter-Free Online Learning

arXiv: https://arxiv.org/abs/2510.15644 · HTML: https://mlsanity.com/paper/2510.15644/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 8 of 8)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Coin Betting and Parameter-Free Online Learning](https://mlsanity.com/paper/1602.04128/) (2016)
- [Unconstrained Online Linear Learning in Hilbert Spaces: Minimax Algorithms and Normal Approximations](https://mlsanity.com/paper/1403.0628/) (2014)
- [Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism](https://mlsanity.com/paper/2012.11579/) (2020)
- [Online Convex Optimization with Unconstrained Domains and Losses](https://mlsanity.com/paper/1703.02622/) (2017)
- [Comparator-adaptive Convex Bandits](https://mlsanity.com/paper/2007.08448/) (2020)
- [Distributed Online Learning for Joint Regret with Communication Constraints](https://mlsanity.com/paper/2102.07521/) (2021)
- [Distributed Online Optimization with Stochastic Agent Availability](https://mlsanity.com/paper/2411.16477/) (2024)

## Built on by (most cited first, 1 of 1)

- [Decentralized Parameter-Free Online Learning with Compressed Gossip](https://mlsanity.com/paper/2605.27831/) (2026)

## Abstract (arXiv)

We propose the first parameter-free decentralized online learning algorithms with network regret guarantees, which achieve sublinear regret without requiring hyperparameter tuning. This family of algorithms connects multi-agent coin-betting and decentralized online learning via gossip steps. To enable our decentralized analysis, we introduce a novel "betting function" formulation for coin-betting that simplifies the multi-agent regret analysis. Our analysis shows sublinear network regret bounds and is validated through experiments on synthetic and real datasets. This family of algorithms is applicable to distributed sensing, decentralized optimization, and collaborative ML applications.