gaussian processes
A Bayesian non-parametric model used for regression and classification tasks, characterized by a distribution over functions and providing a principled way to express uncertainty in predictions.
- A Provable Approach for End-to-End Safe Reinforcement Learning
- Convergence Rates of Constrained Expected Improvement
- FSEO: Few-Shot Evolutionary Optimization via Meta-Learning for Expensive Multi-Objective Optimization
- Flow Matching Neural Processes
- Informed Initialization for Bayesian Optimization and Active Learning
- Optimizing the Unknown: Black Box Bayesian Optimization with Energy-Based Model and Reinforcement Learning
- Quantitative convergence of trained neural networks to Gaussian processes
- ROOT: Rethinking Offline Optimization as Distributional Translation via Probabilistic Bridge
- Regression Trees Know Calculus
- Robust and Computation-Aware Gaussian Processes
- STACI: Spatio-Temporal Aleatoric Conformal Inference
- Solving and Learning Partial Differential Equations with Variational Q-Exponential Processes
- Sparse Gaussian Processes: Structured Approximations and Power-EP Revisited
- Squared families are useful conjugate priors
- Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project