Latest Meta-Learning Research Papers

The newest Meta-Learning papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Meta-Learning so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• LLM4CKD: Large Language Models for Early Stage Chronic Kidney Disease Screening
  Muhammad Ashad Kabir, Sirajam Munira · arXiv · Sep 3, 2026

  Early screening of chronic kidney disease (CKD) is critical for timely intervention, yet most machine learning (ML) and deep learning (DL) approaches require labeled data and model training, limiting their use in real-world screening settin…

• LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
  Dmitrii Andriianov, Andrey Veprikov, Aleksandr Beznosikov · arXiv · Sep 2, 2026

  Low-rank adaptation (LoRA) is the standard way to fine-tune large models, yet when its two factors are trained independently, the update ignores the geometry of the low-rank weight change it induces. We introduce LoRA-TSD, an optimizer that…

• MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
  ChengAo Shen, Wenchao Yu, Fangyu Wu, Dongjin Song et al. · arXiv · Aug 24, 2026

  Time series forecasting (TSF) is evolving toward multimodal and agentic settings, yet using foundation models remains uneconomical in resource-constrained scenarios, where compact, specialized forecasters are more desirable. However, lightw…

• On the Fragility of Self-Improving Agents: Variance, Task Order, and Underspecification
  Qinyuan Ye, Yu Li, Yada Pruksachatkun, Jiaxin Zhang et al. · arXiv · Aug 18, 2026

  Memory-based self-improving agents--those that learn from an online stream of tasks and improve over time by maintaining a textual memory bank--have shown great promise in recent literature. However, the reliability aspects of these methods…

• ScreenShot: A Foundation Model for Few-Shot Combination Drug Screening
  Antoine de Mathelin, Christopher Tosh, Wesley Tansey · arXiv · Aug 12, 2026

  Treating patients with combinations of drugs reduces the risk of resistance to any individual drug. Finding effective combinations is difficult because the large search space makes combinatorial screens prohibitively expensive, time consumi…

• BnBERT-iPET: Sparse Few-Shot Language Modeling for Bengali via Lottery Ticket Pruning
  Sajib Hossain, Md Kamrus Samad, Anan Ghosh, Labib Imam Chowdhury et al. · arXiv · Aug 5, 2026

  Deep neural networks have shown impressive success in NLP tasks owing to their complex structure and huge number of edges. Achieving state-of-the-art performance in natural language processing with a large pre-trained model such as BERT is …

• Federated Meta-Learning Based Computation Offloading Approach With Energy-Delay Tradeoffs in UAV-Assisted VEC
  Chunlin Li, Chao Deng, Yong Zhang, Shaohua Wan · IEEE Transactions on Mobile Computing · Oct 1, 2025

  Federated learning (FL) provides an applicable solution for computation offloading in Unmanned Aerial Vehicle(UAV)-assisted Vehicular Edge Computing (VEC) by preserving privacy. However, the heterogeneity of clients brings challenges to the…

• Meta-XPFL: An Explainable and Personalized Federated Meta-Learning Framework for Privacy-Aware IoMT
  M. Serhani, A. Tariq, Tariq Qayyum, Ikbal Taleb et al. · IEEE Internet of Things Journal · May 15, 2025

  In the Internet of Medical Things (IoMT), specifically in the field of medical image classification—particularly for skin cancer detection—traditional methods face challenges related to data privacy, heterogeneity, and the need for personal…

• System Prompt Optimization with Meta-Learning
  Yumin Choi, Jinheon Baek, Sung Ju Hwang · Neural Information Processing Systems · May 14, 2025

  Large Language Models (LLMs) have shown remarkable capabilities, with optimizing their input prompts playing a pivotal role in maximizing their performance. However, while LLM prompts consist of both the task-agnostic system prompts and tas…

• Optimized Intrusion Detection Approach for Cyber‐Physical System Using Meta‐Learning With Stacked Generalization: An Ensemble Learning Inspired Approach
  Ram Ji, Neerendra Kumar, Devanand Padha · Security and Privacy · Apr 27, 2025

  Cyber‐physical systems (CPSs) are crucial in providing vital infrastructure like smart grids, smart cities, smart automobiles, smart healthcare systems, and so forth, for many nations. CPSs are vulnerable to various attacks due to their lar…

• A framework reforming personalized Internet of Things by federated meta-learning
  Linlin You, Zihan Guo, Chau Yuen, C. Chen et al. · Nature Communications · Apr 20, 2025

  Advances in Artificial Intelligence envision a promising future, where the personalized Internet of Things can be revolutionized with the ability to continuously improve system efficiency and service quality. However, with the introduction …

• An Adaptive Framework for Intrusion Detection in IoT Security Using MAML (Model-Agnostic Meta-Learning)
  Fatma S. Alrayes, Syed Umar Amin, N. Hakami · Italian National Conference on Sensors · Apr 1, 2025

  With the rapid emergence of the Internet of Things (IoT) devices, there were new vectors for attacking cyber, so there was a need for approachable intrusion detection systems (IDSs) with more innovative custom tactics. The traditional IDS m…

• A Hybrid Model for Few-Shot Text Classification Using Transfer and Meta-Learning
  Jia Gao, Shuangquan Lyu, Guiran Liu, Binrong Zhu et al. · 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE) · Feb 13, 2025

  With the continuous development of natural language processing (NLP) technology, text classification tasks have been widely used in multiple application fields. However, obtaining labeled data is often expensive and difficult, especially in…

• Improving landslide susceptibility prediction through ensemble recursive feature elimination and meta-learning framework
  K. Halder, A. Srivastava, Anitabha Ghosh, Subhabrata Das et al. · Scientific Reports · Feb 12, 2025

  Landslides pose significant threats to ecosystems, lives, and economies, particularly in the geologically fragile Sub-Himalayan region of West Bengal, India. This study enhances landslide susceptibility prediction by developing an ensemble …

• Learning to Imbalanced Open Set Generalize: A Meta-Learning Framework for Enhanced Mechanical Diagnosis
  Changdong Wang, Zhou Shu, Jingli Yang, Zhenyu Zhao et al. · IEEE Transactions on Cybernetics · Feb 5, 2025

  To alleviate data distribution under different operating conditions, domain generalization (DG) has been applied in mechanical diagnosis. Still, its effectiveness is limited when unknown fault states appear in the target domain. Consequentl…

• Evaluation-free Time-series Forecasting Model Selection via Meta-learning
  Mustafa Abdallah, R. Rossi, K. Mahadik, Sungchul Kim et al. · ACM Transactions on Knowledge Discovery from Data · Jan 24, 2025

  Time-series forecasting models are invariably used in a variety of domains for crucial decision-making. Traditionally these models are constructed by experts with considerable manual effort. Unfortunately, this approach has poor scalability…

• MAML-KalmanNet: A Neural Network-Assisted Kalman Filter Based on Model-Agnostic Meta-Learning
  Shanli Chen, Yunfei Zheng, Dongyuan Lin, Peng Cai et al. · IEEE Transactions on Signal Processing · Jan 1, 2025

  Neural network-assisted (NNA) Kalman filters provide an effective solution to addressing the filtering issues involving partially unknown system information by incorporating neural networks to compute the intermediate values influenced by u…

• Meta-Learning Enhanced Model Predictive Contouring Control for Agile and Precise Quadrotor Flight
  Mingxin Wei, Lanxiang Zheng, Ying Wu, Ruidong Mei et al. · IEEE Transactions on robotics · Jan 1, 2025

  In agile quadrotor flight, accurately modeling the varying aerodynamic drag forces encountered at different speeds is critical. These drag forces significantly impact the performance and maneuverability of the quadrotor, especially during h…

• Meta Learning Strategies for Comparative and Efficient Adaptation to Financial Datasets
  Kubra Noor, Ubaida Fatima · IEEE Access · Jan 1, 2025

  This research proposes a Meta learning framework for financial time series forecasting, designed to rapidly adapt to novel market conditions with minimal retraining. The framework operates in two stages: 1) pretraining on a diverse set of f…

• Global or Local Adaptation? Client-Sampled Federated Meta-Learning for Personalized IoT Intrusion Detection
  Haorui Yan, Xi Lin, Shenghong Li, Hao Peng et al. · IEEE Transactions on Information Forensics and Security · Jan 1, 2025

  With the increasing size of Internet of Things (IoT) devices, cyber threats to IoT systems have increased. Federated learning (FL) has been implemented in an anomaly-based intrusion detection system (NIDS) to detect malicious traffic in IoT…

• Short-term Load Forecasting of Distribution Transformer Supply Zones Based on Federated Model-Agnostic Meta Learning
  Changsen Feng, Liang Shao, Jiaying Wang, Youbing Zhang et al. · IEEE Transactions on Power Systems · Jan 1, 2025

  With the increasing data privacy concerns raised by not only organizations but also individuals in distribution systems, traditional centralized data-driven forecasting approaches for short-term load forecasting (STLF) in distribution trans…

• Meta-learning with Heterogeneous Tasks
  Zhaofeng Si, Shu Hu, Kaiyi Ji, Siwei Lyu · Crossref · Jan 1, 2025

  Meta-learning is a general approach to equip machine learning models with the ability to handle few-shot scenarios when dealing with many tasks. Most existing meta-learning methods work based on the assumption that all tasks are of equal im…

• Informed Meta-Learning
  Kasia Kobalczyk, Mihaela van der Schaar · ICML 2024 Workshop MHFAIA Poster · Jun 17, 2024

  In noisy and low-data regimes prevalent in real-world applications, a key challenge of machine learning lies in effectively incorporating inductive biases that promote data efficiency and robustness. Meta-learning and informed ML stand out …

• Informed Meta-Learning
  Kasia Kobalczyk, Mihaela van der Schaar · 2nd SPIGM @ ICML Poster · Jun 17, 2024

  In noisy and low-data regimes prevalent in real-world applications, a key challenge of machine learning lies in effectively incorporating inductive biases that promote data efficiency and robustness. Meta-learning and informed ML stand out …

• Meta-Learning with Heterogeneous Tasks
  Zhaofeng Si, Shu Hu, Kaiyi Ji, Siwei Lyu · CoRR 2024 · Jan 1, 2024

  Meta-learning is a general approach to equip machine learning models with the ability to handle few-shot scenarios when dealing with many tasks. Most existing meta-learning methods work based on the assumption that all tasks are of equal im…

• Neuromodulated Meta-Learning
  Jingyao Wang, Huijie Guo, Wenwen Qiang, Jiangmeng Li et al. · CoRR 2024 · Jan 1, 2024

  Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain region…

• A Game Theoretic Approach to Meta-Learning: Nash Model-Agnostic Meta-Learning
  Jihwan Yu, Jaeyeon Jo, Taeyoung Yun, Jinkyoo Park · ICLR 2024 Conference Withdrawn Submission · Sep 22, 2023

  Meta-learning, or learning to learn, aims to develop algorithms that can quickly adapt to new tasks and environments. Model-agnostic meta-learning (MAML), proposed as a bi-level optimization problem, is widely used as a baseline for gradien…

• Making Scalable Meta Learning Practical
  Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger et al. · NeurIPS 2023 poster · Sep 21, 2023

  Despite its flexibility to learn diverse inductive biases in machine learning programs, meta learning (i.e.,\ learning to learn) has long been recognized to suffer from poor scalability due to its tremendous compute/memory costs, training i…

• Making Scalable Meta Learning Practical
  Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger et al. · CoRR 2023 · Jan 1, 2023

  Despite its flexibility to learn diverse inductive biases in machine learning programs, meta learning (i.e., learning to learn) has long been recognized to suffer from poor scalability due to its tremendous compute/memory costs, training in…

• Making Scalable Meta Learning Practical
  Sang Keun Choe, Sanket Vaibhav Mehta, Hwijeen Ahn, Willie Neiswanger et al. · NeurIPS 2023 · Jan 1, 2023

  Despite its flexibility to learn diverse inductive biases in machine learning programs, meta learning (i.e.,\ learning to learn) has long been recognized to suffer from poor scalability due to its tremendous compute/memory costs, training i…