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The proposed method combines scalable gradient-based\nmeta-learning with nonparametric variational inference in a principled\nprobabilistic framework. During fast adaptation, the method is capable of\nlearning complex uncertainty structure beyond a point estimate or a simple\nGaussian approximation. In addition, a robust Bayesian meta-update mechanism\nwith a new meta-loss prevents overfitting during meta-update. Remaining an\nefficient gradient-based meta-learner, the method is also model-agnostic and\nsimple to implement. Experiment results show the accuracy and robustness of the\nproposed method in various tasks: sinusoidal regression, image classification,\nactive learning, and reinforcement learning.","url_abs":"http://arxiv.org/abs/1806.03836v4","url_pdf":"http://arxiv.org/pdf/1806.03836v4.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":"bayesian-model-agnostic-meta-learning","repo_url":"https://github.com/jaesik817/bmaml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"bayesian-model-agnostic-meta-learning","repo_url":"https://github.com/jaesik817/bmaml_rl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"meta-learning","task_name":"Meta-Learning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"variational-inference","task_name":"Variational Inference"},{"task_slug":"image-classification","task_name":"image-classification"},{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.03836","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.03836"}},"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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