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Found 14 papers, 3 papers with code
Bayesian Optimization with Noise-Free Observations: Improved Regret Bounds via Random Exploration
This paper studies Bayesian optimization with noise-free observations.
Gaussian Process Regression under Computational and Epistemic Misspecification
Gaussian process regression is a classical kernel method for function estimation and data interpolation.
Ensemble Kalman Filters with Resampling
Filtering is concerned with online estimation of the state of a dynamical system from partial and noisy observations.
Reduced-Order Autodifferentiable Ensemble Kalman Filters
This paper introduces a computational framework to reconstruct and forecast a partially observed state that evolves according to an unknown or expensive-to-simulate dynamical system.
Optimization on Manifolds via Graph Gaussian Processes
We rely on a point cloud of manifold samples to define a graph Gaussian process surrogate model for the objective.
Non-Asymptotic Analysis of Ensemble Kalman Updates: Effective Dimension and Localization
Many modern algorithms for inverse problems and data assimilation rely on ensemble Kalman updates to blend prior predictions with observed data.
Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning
In recent decades, science and engineering have been revolutionized by a momentous growth in the amount of available data.
A Variational Inference Approach to Inverse Problems with Gamma Hyperpriors
Hierarchical models with gamma hyperpriors provide a flexible, sparse-promoting framework to bridge $L^1$ and $L^2$ regularizations in Bayesian formulations to inverse problems.
Auto-differentiable Ensemble Kalman Filters
Data assimilation is concerned with sequentially estimating a temporally-evolving state.
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective
In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption.
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis
Several data analysis techniques employ similarity relationships between data points to uncover the intrinsic dimension and geometric structure of the underlying data-generating mechanism.
Variational Characterizations of Local Entropy and Heat Regularization in Deep Learning
The aim of this paper is to provide new theoretical and computational understanding on two loss regularizations employed in deep learning, known as local entropy and heat regularization.
On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms
A popular approach to semi-supervised learning proceeds by endowing the input data with a graph structure in order to extract geometric information and incorporate it into a Bayesian framework.
Continuum Limit of Posteriors in Graph Bayesian Inverse Problems
We consider the problem of recovering a function input of a differential equation formulated on an unknown domain $M$.
