25 citations · 94 across the 5 of their papers we have counts for
5 papers
Understanding and Comparing Scalable Gaussian Process Regression for Big Data
Haitao Liu, Jianfei Cai, Yew-Soon Ong +1
As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification…
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Haitao Liu, Jianfei Cai, Yi Wang +1
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distrib…
Latent Tree Models and Approximate Inference in Bayesian Networks
Yi Wang, Nevin L. Zhang, Tao Chen
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and whe…
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach
Xinxi Wang, Yi Wang, David Hsu +1
Current music recommender systems typically act in a greedy fashion by recommending songs with the highest user ratings. Greedy recommendation, however, is suboptimal over the long…
Monte Carlo Bayesian Reinforcement Learning
Yi Wang, Kok Sung Won, David Hsu +1
Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over…