8 citations · 22 across the 12 of their papers we have counts for
13 papers
Orientation Estimation of Cryo-EM Images for Molecules with Arbitrary Symmetry by Semidefinite Programming
Yunpeng Shi, Amit Singer, Jinxin Wang +1
This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures. Precisely, we consider the problem \[ \begin{aligned} \under…
Functional Multi-Target Detection via Bispectrum Inversion
Anna Little, Daniel Sanz-Alonso, Mikhail Sweeney +1
This paper develops a functional theory for multi-target detection, where a compactly supported signal is recovered from a single noisy observation containing many unknown translat…
Bayesian Optimization on Networks
Wenwen Li, Daniel Sanz-Alonso, Ruiyi Yang
This paper studies optimization on networks modeled as metric graphs. Motivated by applications where the objective function is expensive to evaluate or only available as a black b…
Gaussian Process Regression under Computational and Epistemic Misspecification
Daniel Sanz-Alonso, Ruiyi Yang
Gaussian process regression is a classical kernel method for function estimation and data interpolation. In large data applications, computational costs can be reduced using low-ra…
Alignment of Density Maps in Wasserstein Distance
Amit Singer, Ruiyi Yang
In this paper we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algor…
Optimization on Manifolds via Graph Gaussian Processes
Hwanwoo Kim, Daniel Sanz-Alonso, Ruiyi Yang
This paper integrates manifold learning techniques within a \emph{Gaussian process upper confidence bound} algorithm to optimize an objective function on a manifold. Our approach i…
Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning
Nicolas García Trillos, Daniel Sanz-Alonso, Ruiyi Yang
In recent decades, science and engineering have been revolutionized by a momentous growth in the amount of available data. However, despite the unprecedented ease with which data a…
Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy
Daniel Sanz-Alonso, Ruiyi Yang
The stochastic partial differential equation approach to Gaussian processes (GPs) represents Matérn GP priors in terms of finite element basis functions and Gaussian coefficien…
Uniform Consistency in Nonparametric Mixture Models
Bryon Aragam, Ruiyi Yang
We study uniform consistency in nonparametric mixture models as well as closely related mixture of regression (also known as mixed regression) models, where the regression function…
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective
Daniel Sanz-Alonso, Ruiyi Yang
In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption. We adopt a Bayesian perspective and demonstrate that, for a suitable choi…
The SPDE Approach to Matérn Fields: Graph Representations
Daniel Sanz-Alonso, Ruiyi Yang
This paper investigates Gaussian Markov random field approximations to nonstationary Gaussian fields using graph representations of stochastic partial differential equations. We es…
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds
John Harlim, Daniel Sanz-Alonso, Ruiyi Yang
This paper investigates the formulation and implementation of Bayesian inverse problems to learn input parameters of partial differential equations (PDEs) defined on manifolds. Spe…
Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis
Nicolas Garcia Trillos, Daniel Sanz-Alonso, Ruiyi Yang
Several data analysis techniques employ similarity relationships between data points to uncover the intrinsic dimension and geometric structure of the underlying data-generating me…