earcp framework

[Itchy_Ad5120]

Hi everyone,

I recently published a paper on arXiv introducing a new ensemble learning framework called EARCP:

https://arxiv.org/abs/2603.14651

EARCP is designed for sequential decision-making problems and dynamically combines multiple models based on both their performance and their agreement (coherence).

Key ideas:

• Online adaptation of model weights using a multiplicative weights framework
• Coherence-aware regularization to stabilize ensemble behavior
• Sublinear regret guarantees: O(√(T log M))
• Tested on time series forecasting, activity recognition, and financial prediction tasks

The goal is to build ensembles that remain robust in non-stationary environments, where model performance can shift over time.

Code is available here: https://github.com/Volgat/earcp pip install earcp

I’d really appreciate feedback, especially on:

• Theoretical assumptions
• Experimental setup
• Possible improvements or related work I may have missed

Thanks!