19 citations · 24 across the 5 of their papers we have counts for
5 papers
On rates of convergence for sample average approximations without smoothness
Hien Duy Nguyen, Jacob Westerhout, Xin Guo
Sample average approximation (SAA) replaces an intractable expected objective by an empirical average and is a basic device of modern stochastic optimization. We develop a rate the…
Anchored Variational Inference for Personalized Sequential Latent-State Models
Xingche Guo
Sequential latent-variable models with subject-specific random effects provide a flexible framework for modeling temporally structured data with both local latent dynamics and stab…
Sparsemax and Relaxed Wasserstein for Topic Sparsity
Tianyi Lin, Zhiyue Hu, Xin Guo
Topic sparsity refers to the observation that individual documents usually focus on several salient topics instead of covering a wide variety of topics, and a real topic adopts a n…
Ambiguity set and learning via Bregman and Wasserstein
Xin Guo, Johnny Hong, Nan Yang
Construction of ambiguity set in robust optimization relies on the choice of divergences between probability distributions. In distribution learning, choosing appropriate probabili…
Relaxed Wasserstein with Applications to GANs
Xin Guo, Johnny Hong, Tianyi Lin +1
Wasserstein Generative Adversarial Networks (WGANs) provide a versatile class of models, which have attracted great attention in various applications. However, this framework has t…