research
∙
10/20/2022
Scalable Bayesian Transformed Gaussian Processes
The Bayesian transformed Gaussian process (BTG) model, proposed by Kedem...
share
research
∙
11/12/2021
On-the-Fly Rectification for Robust Large-Vocabulary Topic Inference
Across many data domains, co-occurrence statistics about the joint appea...
research
∙
09/30/2021
Surveillance Evasion Through Bayesian Reinforcement Learning
We consider a 2D continuous path planning problem with a completely unkn...
research
∙
07/08/2021
Scaling Gaussian Processes with Derivative Information Using Variational Inference
Gaussian processes with derivative information are useful in many settin...
research
∙
10/21/2020
Density of States Graph Kernels
An important problem on graph-structured data is that of quantifying sim...
research
∙
03/18/2020
On the Distribution of Minima in Intrinsic-Metric Rotation Averaging
Rotation Averaging is a non-convex optimization problem that determines ...
research
∙
02/24/2020
Efficient Rollout Strategies for Bayesian Optimization
Bayesian optimization (BO) is a class of sample-efficient global optimiz...
research
∙
07/30/2019
pySOT and POAP: An event-driven asynchronous framework for surrogate optimization
This paper describes Plumbing for Optimization with Asynchronous Paralle...
research
∙
10/29/2018
Scaling Gaussian Process Regression with Derivatives
Gaussian processes (GPs) with derivatives are useful in many application...
research
∙
09/28/2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
Despite advances in scalable models, the inference tools used for Gaussi...
research
∙
11/19/2017
Prior-aware Dual Decomposition: Document-specific Topic Inference for Spectral Topic Models
Spectral topic modeling algorithms operate on matrices/tensors of word c...
research
∙
11/09/2017
Scalable Log Determinants for Gaussian Process Kernel Learning
For applications as varied as Bayesian neural networks, determinantal po...
research
∙
11/01/2016