57 citations · 479 across the 53 of their papers we have counts for
4 papers · 1 filter
Structured Prediction by Conditional Risk Minimization
Chong Yang Goh, Patrick Jaillet
We propose a general approach for supervised learning with structured output spaces, such as combinatorial and polyhedral sets, that is based on minimizing estimated conditional ri…
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond
Chun Kai Ling, Kian Hsiang Low, Patrick Jaillet
This paper presents a novel nonmyopic adaptive Gaussian process planning (GPP) framework endowed with a general class of Lipschitz continuous reward functions that can unify some a…
Parallel Gaussian Process Regression for Big Data: Low-Rank Representation Meets Markov Approximation
Kian Hsiang Low, Jiangbo Yu, Jie Chen +1
The expressive power of a Gaussian process (GP) model comes at a cost of poor scalability in the data size. To improve its scalability, this paper presents a low-rank-cum-Markov ap…
Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations
Jie Chen, Nannan Cao, Kian Hsiang Low +3
Gaussian processes (GP) are Bayesian non-parametric models that are widely used for probabilistic regression. Unfortunately, it cannot scale well with large data nor perform real-t…