Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Task Agnostic Meta-Learning for Few-Shot Learning (CVPR 2019 · similarity 0.89)
- Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels (NeurIPS 2020 · similarity 0.89)
- Variational Multi-Task Learning with Gumbel-Softmax Priors (NeurIPS 2021 · similarity 0.88)
- More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms (ICML 2024 · similarity 0.88)
- Fast Rate Bounds for Multi-Task and Meta-Learning with Different Sample Sizes (NeurIPS 2025 · similarity 0.88)
- Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning (ICML 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.