Summary of A Unifying Framework For Generalised Bayesian Online Learning in Non-stationary Environments, by Gerardo Duran-martin et al.
A unifying framework for generalised Bayesian online learning in non-stationary environments
by Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Alexander Y. Shestopaloff, Kevin Murphy
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a unifying framework called BONE (Bayesian Online learning in Non-stationary Environments) for probabilistic online learning in non-stationary environments. This framework provides a common structure to tackle various problems, including online continual learning, prequential forecasting, and contextual bandits. The framework requires specifying three modelling choices: a model for measurements, an auxiliary process to model non-stationarity, and a conditional prior over model parameters. Additionally, two algorithmic choices are needed: estimating beliefs about model parameters given the auxiliary variable and estimating beliefs about the auxiliary variable. This modularity allows existing methods to be reinterpreted as instances of BONE and enables proposing new methods. The paper compares experimentally existing methods with proposed new methods on several datasets, providing insights into suitable tasks for each method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to learn and adapt in changing environments using a framework called BONE. This helps machines learn and make good decisions even when the situation changes. The framework has three main parts: how to measure things, how to understand when the environment is changing, and what the machine believes about its own understanding of the world. It also requires two steps: figuring out what the machine thinks based on new information and adjusting for changes in the environment. This allows many different approaches to be used within this framework, which makes it useful for solving a wide range of problems. |
Keywords
» Artificial intelligence » Continual learning » Online learning
