Sequential operator learning under dependent data

arXiv:2608.24426 · stat.ML, cs.LG · Submitted 2026-08-25 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-08-25

Updated: 2026-09-07

Terminology

• Trace — 25× in this paper · explained in 2 episodes
• Hilbert Space — 23× in this paper · explained in 2 episodes
• Bayesian Optimization — 7× in this paper · explained in 3 episodes
• DeepONet — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• International Conference on Machine Learning — 3× in this paper
• Regret — 3× in this paper · explained in 3 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Active Learning — 2× in this paper · explained in 6 episodes
• Information Gain — 2× in this paper · explained in 2 episodes
• Adaptivity — 1× in this paper · explained in 1 episode
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• EMMA — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• A variational approach to dimension-free self-normalized concentration
• Composite Bayesian Optimization In Function Spaces Using NEON -- Neural Epistemic Operator Networks
• Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
• Bayesian Optimization with Structured Measurements: A Vector-Valued RKHS Framework

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