arXiv Machine Learning
TLXML: Task-Level Explanation of Meta-Learning via Influence Functions
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jun 2
Provable Data Scaling Law for Meta Learning via Complexity Minimization
arXiv:2606. 02008v1 Announce Type: cross Abstract: Pre-training has become a fundamental paradigm in modern machine learning, with one of its key empirical benefits being reduced downstream sample complexity as the scale of pre-training data increases.
Hugging Face Trending Papers
Jun 1
Provable Data Scaling Law for Meta Learning via Complexity Minimization
Pre-training has become a fundamental paradigm in modern machine learning, with one of its key empirical benefits being reduced downstream sample complexity as the scale of pre-training data increases. However, existing theoretical frameworks for pre-training do not fully explain this phenomenon.
arXiv Machine Learning
Aug 13
A New First-Order Meta-Learning Algorithm with Convergence Guarantees
arXiv:2409. 03682v2 Announce Type: replace Abstract: Learning new tasks by leveraging prior experience is a fundamental trait of intelligent systems.
arXiv Statistics ML
3d ago
Effects of Structural Allocation of Geometric Task Diversity in Linear Meta-Learning Models
arXiv:2509.18349v4 Announce Type: replace Abstract: Meta-learning aims to leverage information across related tasks to improve prediction on unlabeled data for new tasks when only a small number of l...
arXiv Machine Learning
Sep 21
Data Attribution via Sketched Metadifferentiation
The paper introduces two algorithms, MAGE and SPELL, that enable efficient data attribution in neural networks by estimating a large influence matrix from a limited number of measurements. These methods leverage existing metagradient techniques without additional computational overhead, addressing the challenge of predicting the impact of removing training data in non‑convex models. Experiments show that MAGE and SPELL outperform current baselines across various training scales and measurement budgets.
arXiv AI
Jul 8
Universal Algorithm-Implicit Learning
arXiv:2602. 14761v2 Announce Type: replace-cross Abstract: Current meta-learning methods are constrained to narrow task distributions with fixed feature and label spaces, limiting applicability.