Publications and preprints

• Model-free Estimation of Latent Structure via Multiscale Nonparametric Maximum Likelihood.

  Bryon Aragam and Ruiyi Yang. arXiv preprint, 2024.

• Gaussian Process Regression under Computational and Epistemic Misspecification.

  Daniel Sanz-Alonso and Ruiyi Yang. To appear in SIAM Journal on Numerical Analysis, 2024+.

• Alignment of Density Maps in Wasserstein Distance. [arXiv]

  Amit Singer and Ruiyi Yang. Biological Imaging 4:e5, 2024.

• Optimization on Manifolds via Graph Gaussian Processes. [arXiv]

  Hwanwoo Kim, Daniel Sanz-Alonso, and Ruiyi Yang. SIAM Journal on Mathematics of Data Science 6(1):1-25, 2024.

• Uniform Consistency in Nonparametric Mixture Models. [arXiv]

  Bryon Aragam and Ruiyi Yang. The Annals of Statistics 51(1):362-390, 2023.

• Mathematical Foundations of Graph-Based Bayesian Semi-Supervised Learning. [arXiv]

  Nicolás García Trillos, Daniel Sanz-Alonso, and Ruiyi Yang. Notices of the American Mathematical Society 69(10):1717-1729, 2022.

• Finite Element Representations of Gaussian Processes: Balancing Numerical and Statistical Accuracy. [arXiv]

  Daniel Sanz-Alonso and Ruiyi Yang. SIAM/ASA Journal on Uncertainty Quantification 10(4):1323-1349, 2022.

• Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective. [arXiv]

  Daniel Sanz-Alonso and Ruiyi Yang. Journal of Machine Learning Research 23(97):1-28, 2022.

• The SPDE Approach to Matérn Fields: Graph Representations. [arXiv]

  Daniel Sanz-Alonso and Ruiyi Yang. Statistical Science 37(4):519-540, 2022.

• Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. [arXiv]

  John Harlim, Daniel Sanz-Alonso, and Ruiyi Yang. SIAM/ASA Journal on Uncertainty Quantification 8(4): 1414-1445, 2020.

• Local Regularization of Noisy Point Clouds: Improved Global Geometric Estimates and Data Analysis. [arXiv]

  Nicolás García Trillos, Daniel Sanz-Alonso, and Ruiyi Yang. Journal of Machine Learning Research 20(136):1-37, 2019.

Talks

• Alignment of Density Maps in Wasserstein Distance. SIAM Conference on Mathematics of Data Science, October 2024.

• Optimization on Manifolds via Graph Gaussian Processes. UC Davis MADDD Seminar, Feburary 2024.

• Optimization on Manifolds via Graph Gaussian Processes. IMS Young Mathematical Scientists Forum—Applied Mathematics, January 2024.

• Optimization on Manifolds via Graph Gaussian Processes. New Jersey Institute of Technology Statistics Seminar, March 2023.

• Unlabeled Data Help in Graph-Based Bayesian Semi-Supervised Learning. SIAM Conference on Mathematics of Data Science, September 2022.

• Graph-Based Approximation of Matérn Gaussian Fields. IMSI Workshop on Gaussian processes, August 2022.

• Balancing Numerical and Statistical Accuracy in the SPDE Approach to Gaussian Processes. SIAM Conference on Uncertainty Quantification, April 2022.

• Matérn Gaussian Fields on Graphs: Theory and Applications. Joint Statistical Meetings, August 2021.

• Graph-Based Methods for Bayesian Elliptic Inverse Problems on Manifold. SIAM Conference on Computational Science and Engineering, March 2021.

• Graph-Based Approximation of Matérn Gaussian Fields. University of Wisconsin-Madison Statistics Seminar, Feburary 2021.

• Graph-Based Methods for Inverse Problems on Manifolds and Point Clouds. SIAM Conference on Mathematics of Data Science, June 2020.

• Local Regularization of Noisy Point Clouds. GTDAML Graduate Student Conference, The Ohio State University, June 2019.