no code implementations • 6 Feb 2024 • Liang Ding, Rui Tuo
It is well known that the state space (SS) model formulation of a Gaussian process (GP) can lower its training and prediction time both to $\CalO(n)$ for $n$ data points.
no code implementations • 25 May 2023 • Rui Tuo, Raktim Bhattacharya
The key idea of the proposed method is to add synthetic noise to the data until the predictive variance of the Gaussian process model reaches a prespecified privacy level.
no code implementations • 14 Nov 2022 • Gecheng Chen, Yu Zhou, Xudong Zhang, Rui Tuo
The area of transfer learning comprises supervised machine learning methods that cope with the issue when the training and testing data have different input feature spaces or distributions.
no code implementations • 7 Mar 2022 • HaoYuan Chen, Liang Ding, Rui Tuo
We develop an exact and scalable algorithm for one-dimensional Gaussian process regression with Mat\'ern correlations whose smoothness parameter $\nu$ is a half-integer.
no code implementations • 11 Dec 2021 • Liang Ding, Rui Tuo, Shahin Shahrampour
In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions.
no code implementations • 19 Jul 2021 • Liang Ding, Rui Tuo, Xiaowei Zhang
High-dimensional simulation optimization is notoriously challenging.
1 code implementation • 2 Dec 2020 • Abhinav Prakash, Rui Tuo, Yu Ding
Using existing model selection methods, like cross validation, results in model overfitting in presence of temporal autocorrelation.
no code implementations • 5 Jun 2020 • Liang Ding, Lu Zou, Wenjia Wang, Shahin Shahrampour, Rui Tuo
Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization.
no code implementations • 1 Apr 2020 • Gecheng Chen, Rui Tuo
We show that dimension expansion can help approximate more complex functions.
1 code implementation • 17 Mar 2020 • Abhinav Prakash, Rui Tuo, Yu Ding
This work proposes a new nonparametric method to compare the underlying mean functions given by two noisy datasets.
Methodology Applications
no code implementations • ICML 2020 • Liang Ding, Rui Tuo, Shahin Shahrampour
Despite their success, kernel methods suffer from a massive computational cost in practice.
no code implementations • 4 Feb 2020 • Rui Tuo, Wenjia Wang
Bayesian optimization is a class of global optimization techniques.
no code implementations • NeurIPS 2018 • Rachel Cummings, Sara Krehbiel, Yajun Mei, Rui Tuo, Wanrong Zhang
The change-point detection problem seeks to identify distributional changes at an unknown change-point k* in a stream of data.