Search Results for author:
Found 5 papers, 0 papers with code
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
By utilizing a technique called Kernel Packets (KP), we prove that the convergence rate of Back-fitting is no faster than $(1-\mathcal{O}(\frac{1}{n}))^t$, where $n$ and $t$ denote the data size and the iteration number, respectively.
Regret Optimality of GP-UCB
We establish new upper bounds on both the simple and cumulative regret of GP-UCB when the objective function to optimize admits certain smoothness property.
Representing Additive Gaussian Processes by Sparse Matrices
We show how to use these sparse formulas to generalize back-fitting-based algorithms to efficiently compute the posterior mean, posterior variance, log-likelihood, and gradient of these three functions for additive GPs, all in $O(n \log n)$ time.
6D-ViT: Category-Level 6D Object Pose Estimation via Transformer-based Instance Representation Learning
Specifically, a novel two-stream encoder-decoder framework is dedicated to exploring complex and powerful instance representations from RGB images, point clouds and categorical shape priors.
High-Dimensional Non-Parametric Density Estimation in Mixed Smooth Sobolev Spaces
Density estimation plays a key role in many tasks in machine learning, statistical inference, and visualization.
