gaussian process
A non-parametric model used in statistics and machine learning that defines a distribution over functions, allowing for flexible modeling of uncertain data and enabling predictions with confidence intervals.
- Adaptive Kernel Design for Bayesian Optimization Is a Piece of CAKE with LLMs
- Efficient Training of Minimal and Maximal Low-Rank Recurrent Neural Networks
- Exploring and Exploiting Model Uncertainty in Bayesian Optimization
- Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
- Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
- Hessian-guided Perturbed Wasserstein Gradient Flows for Escaping Saddle Points
- Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
- Improved Regret Bounds for Gaussian Process Upper Confidence Bound in Bayesian Optimization
- In-Context Learning of Stochastic Differential Equations with Foundation Inference Models
- Interpretable and Parameter Efficient Graph Neural Additive Models with Random Fourier Features
- Multi-View Oriented GPLVM: Expressiveness and Efficiency
- Optimal kernel regression bounds under energy-bounded noise
- Robust Satisficing Gaussian Process Bandits Under Adversarial Attacks
- Uncertainty Quantification with the Empirical Neural Tangent Kernel