3 citations · 3 across the 3 of their papers we have counts for
9 papers
Unsupervised Co-Learning on -Manifolds Across Irreducible Representations
Yifeng Fan, Tingran Gao, Zhizhen Zhao
We introduce a novel co-learning paradigm for manifolds naturally equipped with a group action, motivated by recent developments on learning a manifold from attached fibre bundle s…
SelectNet: Learning to Sample from the Wild for Imbalanced Data Training
Yunru Liu, Tingran Gao, Haizhao Yang
Supervised learning from training data with imbalanced class sizes, a commonly encountered scenario in real applications such as anomaly/fraud detection, has long been considered a…
Uniform-in-Time Weak Error Analysis for Stochastic Gradient Descent Algorithms via Diffusion Approximation
Yuanyuan Feng, Tingran Gao, Lei Li +2
Diffusion approximation provides weak approximation for stochastic gradient descent algorithms in a finite time horizon. In this paper, we introduce new tools motivated by the back…
Multi-Frequency Phase Synchronization
Tingran Gao, Zhizhen Zhao
We propose a novel formulation for phase synchronization -- the statistical problem of jointly estimating alignment angles from noisy pairwise comparisons -- as a nonconvex optimiz…
Wasserstein Soft Label Propagation on Hypergraphs: Algorithm and Generalization Error Bounds
Tingran Gao, Shahab Asoodeh, Yi Huang +1
Inspired by recent interests of developing machine learning and data mining algorithms on hypergraphs, we investigate in this paper the semi-supervised learning algorithm of propag…
Gaussian Process Landmarking for Three-Dimensional Geometric Morphometrics
Tingran Gao, Shahar Z. Kovalsky, Doug M. Boyer +1
We demonstrate applications of the Gaussian process-based landmarking algorithm proposed in [T. Gao, S.Z. Kovalsky, and I. Daubechies, SIAM Journal on Mathematics of Data Science (…