T
Tianbao Yang
Researcher at University of Iowa
Publications - 278
Citations - 7349
Tianbao Yang is an academic researcher from University of Iowa. The author has contributed to research in topics: Computer science & Convex optimization. The author has an hindex of 38, co-authored 247 publications receiving 5848 citations. Previous affiliations of Tianbao Yang include General Electric & Princeton University.
Papers
More filters
Posted Content
Learning Attributes Equals Multi-Source Domain Generalization
TL;DR: In this article, a multi-source domain generalization approach is proposed for attribute detection, where each category is treated as a domain and attribute detectors should generalize well across different categories, including those previously unseen.
Journal ArticleDOI
Improved Bound for the Nystrom's Method and its Application to Kernel Classification
TL;DR: A kernel classification approach based on the Nyström method is presented and it is shown that when the eigenvalues of the kernel matrix follow a p-power law, the number of support vectors can be reduced to N2p/(p2 - 1), which is sublinear in N when p > 1+√2, without seriously sacrificing its generalization performance.
Proceedings Article
Stochastic Gradient Descent with Only One Projection
TL;DR: This work develops novel stochastic optimization algorithms that do not need intermediate projections to obtain a feasible solution in the given domain and achieves an O(1/√T) convergence rate for general convex optimization and a O(ln T/T) rate for strongly conveX optimization under mild conditions about the domain and the objective function.
Posted Content
Recovering the Optimal Solution by Dual Random Projection
TL;DR: The theoretical analysis shows that with a high probability, the proposed algorithm is able to accurately recover the optimal solution to the original problem, provided that the data matrix is of low rank or can be well approximated by a low rank matrix.
Proceedings Article
A Bayesian Approach Toward Finding Communities and Their Evolutions in Dynamic Social Networks.
TL;DR: This paper proposes a dynamic stochastic block model for finding communities and their evolutions in a dynamic social network that captures the evolution of communities by explicitly modeling the transition of community memberships for individual nodes in the network.