research
∙
07/27/2023
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
We prove that black-box variational inference (BBVI) with control variat...
share
research
∙
05/24/2023
The Behavior and Convergence of Local Bayesian Optimization
A recent development in Bayesian optimization is the use of local optimi...
research
∙
05/24/2023
Black-Box Variational Inference Converges
We provide the first convergence guarantee for full black-box variationa...
research
∙
03/18/2023
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference
Understanding the gradient variance of black-box variational inference (...
research
∙
10/21/2022
Local Bayesian optimization via maximizing probability of descent
Local optimization presents a promising approach to expensive, high-dime...
research
∙
10/10/2022
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
In this paper, we seek to improve the faithfulness of TempRel extraction...
research
∙
06/13/2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
Minimizing the inclusive Kullback-Leibler (KL) divergence with stochasti...
research
∙
01/28/2022
Local Latent Space Bayesian Optimization over Structured Inputs
Bayesian optimization over the latent spaces of deep autoencoder models ...
research
∙
07/08/2021
Scaling Gaussian Processes with Derivative Information Using Variational Inference
Gaussian processes with derivative information are useful in many settin...
research
∙
07/01/2021
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning
Gaussian processes remain popular as a flexible and expressive model cla...
research
∙
06/29/2020
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Bayesian optimization is a sequential decision making framework for opti...
research
∙
06/19/2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learn...
research
∙
02/21/2020
Deep Sigma Point Processes
We introduce Deep Sigma Point Processes, a class of parametric models in...
research
∙
10/16/2019
Sparse Gaussian Process Regression Beyond Variational Inference
The combination of inducing point methods with stochastic variational in...
research
∙
10/03/2019
Scalable Global Optimization via Local Bayesian Optimization
Bayesian optimization has recently emerged as a popular method for the s...
research
∙
05/17/2019
Simple Black-box Adversarial Attacks
We propose an intriguingly simple method for the construction of adversa...
research
∙
03/19/2019
Exact Gaussian Processes on a Million Data Points
Gaussian processes (GPs) are flexible models with state-of-the-art perfo...
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
∙
03/16/2018
Constant-Time Predictive Distributions for Gaussian Processes
One of the most compelling features of Gaussian process (GP) regression ...
research
∙
02/24/2018
Product Kernel Interpolation for Scalable Gaussian Processes
Recent work shows that inference for Gaussian processes can be performed...
research
∙
11/19/2015
Deep Manifold Traversal: Changing Labels with Convolutional Features
Many tasks in computer vision can be cast as a "label changing" problem,...
research
∙
01/16/2015
