# Continuous Meta-Learning without Tasks

arXiv: https://arxiv.org/abs/1912.08866 · HTML: https://mlsanity.com/paper/1912.08866/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 30 of 50)

- [Variational Bayesian Last Layers](https://mlsanity.com/paper/2404.11599/) (2024)
- [Continual Learning via Local Module Composition](https://mlsanity.com/paper/2111.07736/) (2021)
- [On the Soft-Subnetwork for Few-shot Class Incremental Learning](https://mlsanity.com/paper/2209.07529/) (2022)
- [MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild](https://mlsanity.com/paper/2603.17187/) (2026)
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- [A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases](https://mlsanity.com/paper/2209.11208/) (2022)
- [Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning](https://mlsanity.com/paper/2209.11820/) (2022)
- [Learning Options via Compression](https://mlsanity.com/paper/2212.04590/) (2022)
- [Sequoia: A Software Framework to Unify Continual Learning Research](https://mlsanity.com/paper/2108.01005/) (2021)
- [Discrete Key-Value Bottleneck](https://mlsanity.com/paper/2207.11240/) (2022)
- [Learning to Learn from APIs: Black-Box Data-Free Meta-Learning](https://mlsanity.com/paper/2305.18413/) (2023)
- [Curious Replay for Model-based Adaptation](https://mlsanity.com/paper/2306.15934/) (2023)
- [CoMPS: Continual Meta Policy Search](https://mlsanity.com/paper/2112.04467/) (2021)
- [Learning Mixtures of Linear Dynamical Systems](https://mlsanity.com/paper/2201.11211/) (2022)
- [Is Bayesian Model-Agnostic Meta Learning Better than Model-Agnostic Meta Learning, Provably?](https://mlsanity.com/paper/2203.03059/) (2022)
- [Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation](https://mlsanity.com/paper/2210.03209/) (2022)
- [When Meta-Learning Meets Online and Continual Learning: A Survey](https://mlsanity.com/paper/2311.05241/) (2023)
- [Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning](https://mlsanity.com/paper/2303.11183/) (2023)
- [Bayesian Embeddings for Few-Shot Open World Recognition](https://mlsanity.com/paper/2107.13682/) (2021)
- [Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness](https://mlsanity.com/paper/2109.14120/) (2021)
- [Clustering-based Domain-Incremental Learning](https://mlsanity.com/paper/2309.12078/) (2023)
- [On-device edge learning for IoT data streams: a survey](https://mlsanity.com/paper/2502.17788/) (2025)
- [Meta-Learning Parameterized Skills](https://mlsanity.com/paper/2206.03597/) (2022)
- [On Sequential Bayesian Inference for Continual Learning](https://mlsanity.com/paper/2301.01828/) (2023)
- [Time-Varying Propensity Score to Bridge the Gap between the Past and Present](https://mlsanity.com/paper/2210.01422/) (2022)
- [Fully Online Meta-Learning Without Task Boundaries](https://mlsanity.com/paper/2202.00263/) (2022)
- [Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies](https://mlsanity.com/paper/2310.10500/) (2023)
- [T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data](https://mlsanity.com/paper/2309.02610/) (2023)
- [Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks](https://mlsanity.com/paper/2502.02834/) (2025)
- [Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving](https://mlsanity.com/paper/2504.16923/) (2025)

## Abstract (arXiv)

Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks. 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. 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. We present meta-learning via online changepoint analysis (MOCA), an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme. The framework allows both training and testing directly on time series data without segmenting it into discrete tasks. We demonstrate the utility of this approach on a nonlinear meta-regression benchmark as well as two meta-image-classification benchmarks.