Tractable Uncertainty-Aware Meta-Learning

[2210.01881] Tractable Uncertainty-Aware Meta-Learning Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2210.01881 (cs) [Submitted on 4 Oct 2022 ( v1 ), last revised 8 Apr 2026 (this version, v2)] Title: Tractable Uncertainty-Aware Meta-Learning Authors: Young-Jin Park , Cesar Almecija , Apoorva Sharma , Navid Azizan View a PDF of the paper titled Tractable Uncertainty-Aware Meta-Learning, by Young-Jin Park and 2 other authors View PDF HTML (experimental) Abstract: Meta-learning is a popular approach for learning new tasks with limited data by leveraging the commonalities among different tasks. However, meta-learned models can perform poorly when context data is too limited, or when data is drawn from an out-of-distribution (OoD) task. Especially in safety-critical settings, this necessitates an uncertainty-aware approach to meta-learning. In addition, the often multimodal nature of task distributions can pose unique challenges to meta-learning methods. To this end, we present LUMA, a meta-learning method for regression that (1) makes probabilistic predictions on in-distribution tasks efficiently, (2) is capable of detecting OoD context data, and (3) handles heterogeneous, multimodal task distributions effectively. The strength of our framework lies in its solid theoretical basis, enabling analytically tractable Bayesian inference on a linearized model for principled uncertainty estimation and robust generalization. We achieve this by adopting a probabilistic perspective and learning a parametric, tunable task distribution via Bayesian inference on a linearized neural network, leveraging Gaussian process theory. Moreover, we make our approach computationally tractable by leveraging a low-rank prior covariance learning scheme based on the Fisher Information Matrix. Our numerical analysis demonstrates that LUMA quickly adapts to new tasks and remains accurate even in low-data regimes; it effectively detects OoD tasks; and that both of these properties continue to hold for multimodal task distributions. Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2210.01881 [cs.LG] (or arXiv:2210.01881v2 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2210.01881 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Young-Jin Park [ view email ] [v1] Tue, 4 Oct 2022 20:02:25 UTC (1,809 KB) [v2] Wed, 8 Apr 2026 18:43:06 UTC (4,350 KB) Full-text links: Access Paper: View a PDF of the paper titled Tractable Uncertainty-Aware Meta-Learning, by Young-Jin Park and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2022-10 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Learning Global Sensitivity Indices from Observational Data: A Metamodel-Based Approach#1154601
• Meta-Learning For Personalized Healthcare: Designing Adaptive Models for Precision Medicine In#101387
• Are Concept Bottleneck Models Effective as Decision-Support Systems?#1061779
• Transferable Graph Metanetworks#1155355
• Key Path Identification for Resolving Knowledge Conflicts via SAE-based Steering#985551