{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/meta-learning-priors-for-efficient-online","title":"Meta-Learning Priors for Efficient Online Bayesian Regression","arxiv_id":"1807.08912","date":"2018-07-24","proceeding":null,"authors":["James Harrison","Apoorva Sharma","Marco Pavone"],"abstract":"Gaussian Process (GP) regression has seen widespread use in robotics due to\nits generality, simplicity of use, and the utility of Bayesian predictions. The\npredominant implementation of GP regression is a nonparameteric kernel-based\napproach, as it enables fitting of arbitrary nonlinear functions. However, this\napproach suffers from two main drawbacks: (1) it is computationally\ninefficient, as computation scales poorly with the number of samples; and (2)\nit can be data inefficient, as encoding prior knowledge that can aid the model\nthrough the choice of kernel and associated hyperparameters is often\nchallenging and unintuitive. In this work, we propose ALPaCA, an algorithm for\nefficient Bayesian regression which addresses these issues. ALPaCA uses a\ndataset of sample functions to learn a domain-specific, finite-dimensional\nfeature encoding, as well as a prior over the associated weights, such that\nBayesian linear regression in this feature space yields accurate online\npredictions of the posterior predictive density. These features are neural\nnetworks, which are trained via a meta-learning (or \"learning-to-learn\")\napproach. ALPaCA extracts all prior information directly from the dataset,\nrather than restricting prior information to the choice of kernel\nhyperparameters. Furthermore, by operating in the weight space, it\nsubstantially reduces sample complexity. We investigate the performance of\nALPaCA on two simple regression problems, two simulated robotic systems, and on\na lane-change driving task performed by humans. We find our approach\noutperforms kernel-based GP regression, as well as state of the art\nmeta-learning approaches, thereby providing a promising plug-in tool for many\nregression tasks in robotics where scalability and data-efficiency are\nimportant.","url_abs":"http://arxiv.org/abs/1807.08912v2","url_pdf":"http://arxiv.org/pdf/1807.08912v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"meta-learning-priors-for-efficient-online","repo_url":"https://github.com/StanfordASL/ALPaCA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"meta-learning-priors-for-efficient-online","repo_url":"https://github.com/StanfordASL/moca","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"meta-learning-priors-for-efficient-online","repo_url":"https://github.com/yilun-wu/MetaL-Benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"linear-regression","method_name":"Linear Regression"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.08912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.08912"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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