# Random Feature-based Online Multi-kernel Learning in Environments with Unknown Dynamics

arXiv: https://arxiv.org/abs/1712.09983 · HTML: https://mlsanity.com/paper/1712.09983/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2017.

## Built on by (most cited first, 28 of 28)

- [Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network](https://mlsanity.com/paper/2107.00894/) (2021)
- [Incremental Ensemble Gaussian Processes](https://mlsanity.com/paper/2110.06777/) (2021)
- [Tighter Regret Analysis and Optimization of Online Federated Learning](https://mlsanity.com/paper/2205.06491/) (2022)
- [Personalized Online Federated Learning with Multiple Kernels](https://mlsanity.com/paper/2311.05108/) (2023)
- [Distributed Online Learning with Multiple Kernels](https://mlsanity.com/paper/2102.12733/) (2021)
- [Gossiped and Quantized Online Multi-Kernel Learning](https://mlsanity.com/paper/2301.09848/) (2023)
- [On the Necessity of Collaboration for Online Model Selection with Decentralized Data](https://mlsanity.com/paper/2404.09494/) (2024)
- [Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees](https://mlsanity.com/paper/2205.15942/) (2022)
- [Low-Rank Adaptation Redux for Large Models](https://mlsanity.com/paper/2604.21905/) (2026)
- [Graph-Aided Online Multi-Kernel Learning](https://mlsanity.com/paper/2102.04690/) (2021)
- [Online Learning with Uncertain Feedback Graphs](https://mlsanity.com/paper/2106.08441/) (2021)
- [Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees](https://mlsanity.com/paper/2310.15974/) (2023)
- [Robust, Online, and Adaptive Decentralized Gaussian Processes](https://mlsanity.com/paper/2509.18011/) (2025)
- [Improved Regret Bounds for Online Kernel Selection under Bandit Feedback](https://mlsanity.com/paper/2303.05018/) (2023)
- [An Online Multiple Kernel Parallelizable Learning Scheme](https://mlsanity.com/paper/2308.10101/) (2023)
- [Decentralized Kernel Ridge Regression Based on Data-Dependent Random Feature](https://mlsanity.com/paper/2405.07791/) (2024)
- [Learning Expressive Random Feature Models via Parametrized Activations](https://mlsanity.com/paper/2411.19468/) (2024)
- [Decentralized Online Ensembles of Gaussian Processes for Multi-Agent Systems](https://mlsanity.com/paper/2502.05301/) (2025)
- [Random feature-based double Vovk-Azoury-Warmuth algorithm for online multi-kernel learning](https://mlsanity.com/paper/2503.20087/) (2025)
- [A hierarchical Vovk-Azoury-Warmuth forecaster with discounting for online regression in RKHS](https://mlsanity.com/paper/2506.22631/) (2025)
- [On the Generalization Properties of Learning the Random Feature Models with Learnable Activation Functions](https://mlsanity.com/paper/2510.15327/) (2025)
- [Multiple Kernel-Based Online Federated Learning](https://mlsanity.com/paper/2102.10861/) (2021)
- [Random Feature Approximation for Online Nonlinear Graph Topology Identification](https://mlsanity.com/paper/2110.09935/) (2021)
- [Online Deep Learning based on Auto-Encoder](https://mlsanity.com/paper/2201.07383/) (2022)
- [Model Extraction Attacks Against Reinforcement Learning Based Controllers](https://mlsanity.com/paper/2304.13090/) (2023)
- [Learnability in Online Kernel Selection with Memory Constraint via Data-dependent Regret Analysis](https://mlsanity.com/paper/2407.00916/) (2024)
- [Bayesian meta learning for trustworthy uncertainty quantification](https://mlsanity.com/paper/2407.19287/) (2024)
- [Incremental Structure Discovery of Classification via Sequential Monte Carlo](https://mlsanity.com/paper/2408.07875/) (2024)

## Abstract (arXiv)

Kernel-based methods exhibit well-documented performance in various nonlinear learning tasks. Most of them rely on a preselected kernel, whose prudent choice presumes task-specific prior information. Especially when the latter is not available, multi-kernel learning has gained popularity thanks to its flexibility in choosing kernels from a prescribed kernel dictionary. Leveraging the random feature approximation and its recent orthogonality-promoting variant, the present contribution develops a scalable multi-kernel learning scheme (termed Raker) to obtain the sought nonlinear learning function `on the fly,' first for static environments. To further boost performance in dynamic environments, an adaptive multi-kernel learning scheme (termed AdaRaker) is developed. AdaRaker accounts not only for data-driven learning of kernel combination, but also for the unknown dynamics. Performance is analyzed in terms of both static and dynamic regrets. AdaRaker is uniquely capable of tracking nonlinear learning functions in environments with unknown dynamics, and with with analytic performance guarantees. Tests with synthetic and real datasets are carried out to showcase the effectiveness of the novel algorithms.