# An Online Multiple Kernel Parallelizable Learning Scheme

arXiv: https://arxiv.org/abs/2308.10101 · HTML: https://mlsanity.com/paper/2308.10101/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 43 references that are in the map.
- First posted on arXiv 19 Aug 2023.

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

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Online Optimization : Competing with Dynamic Comparators](https://mlsanity.com/paper/1501.06225/) (2015)
- [Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application](https://mlsanity.com/paper/1309.1541/) (2013)
- [Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics](https://mlsanity.com/paper/1712.09983/) (2017)
- [Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance](https://mlsanity.com/paper/1902.03046/) (2019)
- [Optimally Compressed Nonparametric Online Learning](https://mlsanity.com/paper/1909.11555/) (2019)

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

- [Online Gradient Computation for Warping Gaussian Process Transformations](https://mlsanity.com/paper/2609.16472/) (2026)

## Abstract (arXiv)

The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging and computationally demanding, especially in data-rich tasks without prior information about the solution domain. In this paper, we propose a learning scheme that scalably combines several single kernel-based online methods to reduce the kernel-selection bias. The proposed learning scheme applies to any task formulated as a regularized empirical risk minimization convex problem. More specifically, our learning scheme is based on a multi-kernel learning formulation that can be applied to widen any single-kernel solution space, thus increasing the possibility of finding higher-performance solutions. In addition, it is parallelizable, allowing for the distribution of the computational load across different computing units. We show experimentally that the proposed learning scheme outperforms the combined single-kernel online methods separately in terms of the cumulative regularized least squares cost metric.