Yunbo Ouyang
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View article: LiMAML: Personalization of Deep Recommender Models via Meta Learning
LiMAML: Personalization of Deep Recommender Models via Meta Learning Open
In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and freque…
View article: LiGNN: Graph Neural Networks at LinkedIn
LiGNN: Graph Neural Networks at LinkedIn Open
In this paper, we present LiGNN, a deployed large-scale Graph Neural Networks (GNNs) Framework. We share our insight on developing and deployment of GNNs at large scale at LinkedIn. We present a set of algorithmic improvements to the quali…
View article: LiRank: Industrial Large Scale Ranking Models at LinkedIn
LiRank: Industrial Large Scale Ranking Models at LinkedIn Open
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods. We unveil several modeling improvements, including Residual DCN, which adds attentio…
View article: Incremental Learning for Personalized Recommender Systems
Incremental Learning for Personalized Recommender Systems Open
Ubiquitous personalized recommender systems are built to achieve two seemingly conflicting goals, to serve high quality content tailored to individual user's taste and to adapt quickly to the ever changing environment. The former requires …
View article: An Ensemble EM Algorithm for Bayesian Variable Selection
An Ensemble EM Algorithm for Bayesian Variable Selection Open
We study the Bayesian approach to variable selection for linear regression models. Motivated by a recent work by Ročková and George (2014), we propose an EM algorithm that returns the MAP estimator of the set of relevant variables. Due to …
View article: Scalable sparsity structure learning using Bayesian methods
Scalable sparsity structure learning using Bayesian methods Open
Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. In this thesis we develop scalable Bayesian algorithms based on EM algorithm and variational inference to learn sparsity structure in vario…
View article: An Empirical Bayes Approach for High Dimensional Classification
An Empirical Bayes Approach for High Dimensional Classification Open
We propose an empirical Bayes estimator based on Dirichlet process mixture model for estimating the sparse normalized mean difference, which could be directly applied to the high dimensional linear classification. In theory, we build a bri…
View article: A Nonparametric Bayesian Approach for Sparse Sequence Estimation
A Nonparametric Bayesian Approach for Sparse Sequence Estimation Open
A nonparametric Bayes approach is proposed for the problem of estimating a sparse sequence based on Gaussian random variables. We adopt the popular two-group prior with one component being a point mass at zero, and the other component bein…
View article: Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network
Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network Open
A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because netw…