Meta-Learning

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Meta-learning studies how experience across related tasks can prepare a model to learn a new task from a small support set, brief feedback, or context alone. Common approaches include gradient-based initialization methods such as MAML, metric and prototype methods, Bayesian methods, learned optimizers and hypernetworks, and episodic training for in-context or test-time adaptation. Applications range from few-shot vision and domain-shifted control to AutoML, scientific system identification, and language or robotic agents, where fast adaptation can reduce labeling, retraining, or optimization costs. The central challenge is transferring useful shared structure without negative transfer: task diversity, distribution shift, uncertainty, costly bilevel training, and fair evaluation of adaptation remain active concerns.

Latest papers

166 papers20 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

Does language matter for spoken word classification? A multilingual generative meta-learning approach
May 13, 2026Batsirayi Mupamhi Ziki, Louise Beyers, Ruan van der MerweMeta-Learning Audio Classification
1Bytefuse.
MetaColloc: Optimization-Free PDE Solving via Meta-Learned Basis Functions
May 12, 2026Zichuan YangNeural PDE Solvers Meta-Learning
School of Mathematical Sciences Tongji University Shanghai, P.R. China
Internalizing Curriculum Judgment for LLM Reinforcement Fine-Tuning
May 11, 2026Han Zheng, Yining Ma, Karthick Gunasekaran +4Reinforcement Learning Meta-Learning
MIT · Amazon AGI
LLM-Driven Performance-Space Augmentation for Meta-Learning-Based Algorithm Selection
May 10, 2026Darren Zhu, Daren LerSynthetic Data Augmentation Algorithm Selection
Department of Statistics and Data Science National University of Singapore · Department of Computer Science National University of Singapore
From Regression to Inference: Meta-Learning Predictors for Neural Architecture Search
May 10, 2026Liping Deng, MingQing XiaoNeural Surrogate Modeling Meta-Learning
Department of Mathematics University of California, Riverside Riverside, CA 92507 USA · School of Mathematical and Statistical Sciences Southern Illinois University Carbondale Carbondale, IL 62901 USA
PRIM: Meta-Learned Bayesian Root Cause Analysis
May 9, 2026Christopher Lohse, Anish Dhir, Amadou Ba +3Meta-Learning Neural Processes
University of Dublin, Trinity College, Dublin, Ireland · IBM · Gatsby Computational Neuroscience Unit, University College London +2
HARMONY: Bridging the Personalization-Generalization Gap by Mitigating Representation Skew in Heterogeneous Split Federated Learning
May 8, 2026Jiseok Youn, You Rim Choi, Goodsol Lee +3Meta-Learning OOD Generalization
Department of ECE and INMC, Seoul National University, Seoul, South Korea · Graduate School of Data Science, Seoul National University, Seoul, South Korea · Department of Computer Science, University of Colorado Boulder, CO, USA
Region-adaptable retrieval of coastal biogeochemical parameters from near-surface hyperspectral remote sensing reflectance using physics-aware meta-learning
May 7, 2026Yiqing Guo, Nagur R. C. Cherukuru, Eric A. Lehmann +7Meta-Learning Remote Sensing
a · b · c +5
Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
May 6, 2026Becky Langdon, Gabriel D. Patrón, Chrysoula D. Kappatou +6Meta-Learning Bayesian Optimization
Department of Computing, Imperial College London, United Kingdom · BASF SE, Ludwigshafen, Germany · School of Mathematical Sciences, Lancaster University, United Kingdom +1
Meta-Inverse Physics-Informed Neural Networks for High-Dimensional Ordinary Differential Equations
May 5, 2026Zhao Wei, Kenneth Hor Cheng Koh, Sheng Yuan Chin +3Meta-Learning Physics-Informed ML
Centre for Frontier AI Research (CFAR) and the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore · Singapore Institute of Food and Biotechnology Innovation (SIFBI), Agency for Science, Technology and Research (A*STAR), 31 Biopolis Way, Nanos, Singapore 138669, Singapore · A*STAR Skin Research Labs (A*SRL), Agency for Science, Technology and Research (A*STAR), 8A Biomedical Grove, Immunos, Singapore 138648, Singapore +1
A Meta Reinforcement Learning Approach to Goals-Based Wealth Management
May 4, 2026Sanjiv R. Das, Harshad Khadilkar, Sukrit Mittal +3Financial Services Meta-Learning
Dept. of Finance, Santa Clara University, Santa Clara, CA, USA · Indian Institute of Technology, Mumbai, India · Franklin Templeton, Mumbai, India +4
Learn-To-Learn on Arbitrary Textual Conditioning: A Hypernetwork-Driven Meta-Gated LLM
May 3, 2026Luo Ji, Qi Qin, Ningyuan Xi +3Gated Recurrent Unit Meta-Learning
Geely AI Lab, Geely Auto Group, Zhejiang, China · Peking University, Beijing, China
Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications
Apr 30, 2026Ricardo Luna Gutierrez, Sahand Ghorbanpour, Ejaz Rahman +5Meta-Learning Bayesian Optimization
1Hewlett Packard Enterprise · University of Rochester
Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks
Apr 29, 2026Beomchul Park, Minsu Koh, Heejo Kong +1Neural PDE Solvers Meta-Learning
Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Republic of Korea · Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul, 02841, Republic of Korea
Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models
Apr 28, 2026Xianghao Meng, James L. Beck, Yong Huang +1Markov Chain Monte Carlo Meta-Learning
Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China · Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China · Division of Engineering and Applied Science, California Institute of Technology, CA, USA
Meta-Aligner: Bidirectional Preference-Policy Optimization for Multi-Objective LLMs Alignment
Apr 27, 2026Wenzhe Xu, Biao Liu, Yiyang Sun +2Meta-Learning Multi-Objective Language Model Alignment
Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
Apr 23, 2026João Mattos, Arlei SilvaGraph Representation Learning Meta-Learning
Computer Science Department · Ken Kennedy Institute
The Last Harness You'll Ever Build
Apr 22, 2026Haebin Seong, Li Yin, Haoran Zhang +1Meta-Learning Agent Harness Optimization
Model-Agnostic Meta Learning for Class Imbalance Adaptation
Apr 20, 2026Hanshu Rao, Guangzeng Han, Xiaolei HuangClass-Imbalanced Learning Instance Reweighting
University of Memphis
MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring
Apr 19, 2026Salam Albatarni, May Bashendy, Sohaila Eltanbouly +1Meta-Learning Automated Essay Scoring
Computer Science and Engineering Department, Qatar University
Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning
Apr 19, 2026Ziqing Zhuang, Linhai Zhang, Jiasheng Si +2Continual Learning for LLMs Meta-Learning
Southeast University · King’s College London · Qilu University of Technology (Shandong Academy of Sciences) +1
Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation
Apr 14, 2026Yilang Zhang, Abraham Jaeger Mountain, Bingcong Li +1Meta-Learning
Department of Electrical and Computer Engineering University of Minnesota Minneapolis, MN 55455, USA · Department of Computer Science ETH Zürich 8092 Zürich, Switzerland
GeM-EA: A Generative and Meta-learning Enhanced Evolutionary Algorithm for Streaming Data-Driven Optimization
Apr 14, 2026Yue Wu, Yuan-Ting Zhong, Ze-Yuan Ma +1Evolutionary Optimization Surrogate-Assisted Optimization
South China University of Technology, Guangzhou, China
Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks
Apr 14, 2026Azza Fadhel, The Hung Tran, Trong Nghia Hoang +1Black-Box Optimization Surrogate-Assisted Optimization
School of EECS, Washington State University, Pullman, WA, USA
Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates
Apr 13, 2026Saumya Goyal, Rohith Rongali, Ritabrata Ray +1Meta-Learning Gradient Descent
Machine Learning Department, Carnegie Mellon University · Electrical and Computer Engineering Department, Carnegie Mellon University.
FGML-DG: Feynman-Inspired Cognitive Science Paradigm for Cross-Domain Medical Image Segmentation
Apr 12, 2026Yucheng Song, Chenxi Li, Haokang Ding +2Domain Generalization Cross-Domain Generalization
School of Computer Science and Engineering, Central South University, Changsha, China · School of Health & Wellbeing, University of Glasgow, UK
Meta-Learned Basis Adaptation for Parametric Linear PDEs
Apr 10, 2026Vikas Dwivedi, Monica Sigovan, Bruno SixouPDE Solving PDE Surrogate Modeling
CREATIS Biomedical Imaging Laboratory INSA, CNRS UMR 5220, Inserm, Université Lyon 1 Lyon 69621, France
Meta-learning In-Context Enables Training-Free Cross Subject Brain Decoding
Apr 9, 2026Mu Nan, Muquan Yu, Weijian Mai +12fMRI-Based Visual Decoding Neural Decoding
University of Hong Kong · Shenzhen Loop Area Institute · Chinese University of Hong Kong +5
Meta-TTL: Meta-Learning Self-Improvement Policies for Language Agents
Apr 1, 2026Zhanzhi Lou, Hui Chen, Yibo Li +2Meta-Learning Test-Time Training
National University of Singapore
HSFM: Hard-Set-Guided Feature-Space Meta-Learning for Robust Classification under Spurious Correlations
Mar 31, 2026Aryan Yazdan Parast, Khawar Islam, Soyoun Won +2Spurious Correlation Robustness Data Augmentation
The University of Melbourne, Melbourne, Australia

May 13, 2026J/K move · Enter open · S save

Does language matter for spoken word classification? A multilingual generative meta-learning approach

Batsirayi Mupamhi Ziki, Louise Beyers, Ruan van der Merwe

1Bytefuse.

Meta-Learning Audio Classification

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Meta-learning has been shown to have better performance than supervised learning for few-shot monolingual spoken word classification. However, the meta-learning approach remains under-explored in multilingual spoken word classification. In this paper, we apply the Generative Meta-Continual Learning algorithm to spoken word classification. The generative nature of this algorithm makes it viable for use in application, and the meta-learning aspect promotes generalisation, which is crucial in a multilingual setting. We train monolingual models on English, German, French, and Catalan, a bilingual model on English and German, and a multilingual model on all four languages. We find that although the multilingual model performs best, the differences between model performance is unexpectedly low. We also find that the hours of unique data seen during training seems to be a stronger performance indicator than the number of languages included in the training data.