# Continuous Meta Learning without tasks

## Motivation

Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks.&#x20;

However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task, and at test-time, the algorithms are optimized to learn in a single task.&#x20;

In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.&#x20;

We present meta-learning via online changepoint analysis (MOCA), an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme.

## Problem Statement

![](https://1687130946-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MEnQbUIupyAn8eMmrmG%2F-MKYEu49j6qgG_jc57L_%2F-MKYFKLGCyyV9hzWKX7M%2Fimage.png?alt=media\&token=56a1c4a4-c030-4035-9ceb-cd1c12754cc4)

We assume that we have access to a representative time series generated in the same manner from the same distribution of tasks, and use this time series to optimize in an offline, meta-training phase.&#x20;

Critically, however, in stark contrast to standard meta-learning approaches, we do not assume access to task segmentation.&#x20;

Moreover, we highlight that we consider the case of individual data points provided sequentially, in contrast to the common “k-shot, n-way” problem setting prevalent in few-shot learning (especially classification).

## Bayesian Online Changepoint Detection (BOCPD)

We build on Bayesian online changepoint detection (Adams & MacKay, 2007), an approach for detecting changepoints (i.e. task switches) originally presented in a streaming unconditional density estimation context.

BOCPD operates by maintaining a belief distribution over run lengths, i.e. how many of the past data points $$y\_t$$ correspond to the current task.

In this work, we extend this approach of Adams & MacKay (2007) beyond Bayesian unconditional density estimation to apply to general meta-learning models operating in the conditional density estimation setting.

Details could be found from: [http://gregorygundersen.com/blog/2019/08/13/bocd/&#x20; <br>](http://gregorygundersen.com/blog/2019/08/13/bocd/)

![](https://1687130946-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MEnQbUIupyAn8eMmrmG%2F-MKYEu49j6qgG_jc57L_%2F-MKYFsP7hTXOc4AItLYJ%2Fimage.png?alt=media\&token=89574d51-872b-48c9-835a-5324e141e858)

## Overview of MOCA

![](https://1687130946-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MEnQbUIupyAn8eMmrmG%2F-MKYEu49j6qgG_jc57L_%2F-MKYG-zr6B6Xc8oy3IVz%2Fimage.png?alt=media\&token=dab22f0c-7cb5-47f8-bb85-5a61c341a7e4)

## Meta-Learning via Online Changepoint Analysis

![](https://1687130946-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MEnQbUIupyAn8eMmrmG%2F-MKYEu49j6qgG_jc57L_%2F-MKYG5vRsiAKqHcDqfyN%2Fimage.png?alt=media\&token=68036a57-361f-4517-8f23-bd3d6196e3fd)

![](https://1687130946-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MEnQbUIupyAn8eMmrmG%2F-MKYEu49j6qgG_jc57L_%2F-MKYG9yx-C_ux41zo8J3%2Fimage.png?alt=media\&token=11382af9-6cf3-440a-a366-b226d3e3cc3f)

#### NOTE: Adding more about Connection with meta-learning, online learning, and continual learning

## Reference

* <https://arxiv.org/pdf/1912.08866.pdf> (latest version)
