Generalization Guarantees on Data-Driven Tuning of Gradient Descent with Langevin Updates

arXiv:2604.13130 · cs.LG, stat.ML · Submitted 2026-04-13 · Read on arXiv

cs.LG, stat.ML

Submitted: 2026-04-13

Updated: 2026-09-09

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalization — 25× in this paper · explained in 13 episodes
• Loss Function — 17× in this paper · explained in 2 episodes
• Meta-learning — 17× in this paper · explained in 2 episodes
• Regularization — 12× in this paper · explained in 3 episodes
• Unadjusted Langevin Algorithm — 7× in this paper
• Adam — 6× in this paper · explained in 2 episodes
• Langevin Dynamics — 6× in this paper · explained in 2 episodes
• Few-Shot Learning — 5× in this paper · explained in 4 episodes
• Langevin Gradient Descent Algorithm — 5× in this paper
• Bayesian Optimization — 3× in this paper · explained in 3 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Bayesian Inference — 2× in this paper · explained in 5 episodes
• Convergence Guarantees — 2× in this paper · explained in 3 episodes
• Generalization Bounds — 2× in this paper · explained in 2 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Diffusion Processes — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Regret — 1× in this paper · explained in 3 episodes
• Score Function — 1× in this paper · explained in 4 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Optimizing ML Training with Metagradient Descent
• Population Based Training of Neural Networks
• Adam: A Method for Stochastic Optimization
• On First-Order Meta-Learning Algorithms
• Meta-Learning with Implicit Gradients

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