Jacob Gardner
30 papers · 2014–2024 · 7 venues · across top CS/AI venues
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(20)
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(6)
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(2)
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Research topics
Machine Learning
(27)
Optimization & Theory
(26)
Bayesian & Probabilistic Β· Machine Learning
(17)
Artificial Intelligence
(12)
Deep Learning
(9)
Mathematics & Optimization
(9)
Optimization
(8)
Bayesian & Probabilistic Β· Artificial Intelligence
(6)
Core Methods
(6)
Learning Types
(5)
Models
(5)
Learning Paradigms
(4)
Application Areas
(3)
Computer Vision
(2)
Core AI
(2)
Keywords
gaussian process
(14)
bayesian optimization
(13)
variational inference
(7)
black-box optimization
(4)
kernel matrix
(4)
predictive distribution
(3)
scalable inference
(3)
stochastic gradient descent
(3)
local optimization
(2)
matrix multiplication
(2)
inducing point method
(2)
bayesian inference
(2)
surrogate model
(2)
inducing point
(2)
trust region
(2)
conjugate gradient
(2)
hyperparameter tuning
(2)
markov chain monte carlo
(2)
gradient variance
(2)
scalable learning
(1)
Papers
Generative Adversarial Model-Based Optimization via Source Critic Regularization
NeurIPS 2024
Large-Scale Gaussian Processes via Alternating Projection
AISTATS 2024
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
AISTATS 2024
Stochastic Approximation with Biased MCMC for Expectation Maximization
AISTATS 2024
Discovering Many Diverse Solutions with Bayesian Optimization
AISTATS 2023
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
EACL 2023
Learning to Select Pivotal Samples for Meta Re-weighting
AAAI 2023
On the Convergence of Black-Box Variational Inference
NeurIPS 2023
The Behavior and Convergence of Local Bayesian Optimization
NeurIPS 2023
Variational Gaussian Processes with Decoupled Conditionals
NeurIPS 2023
Local Bayesian optimization via maximizing probability of descent
NeurIPS 2022
Local Latent Space Bayesian Optimization over Structured Inputs
NeurIPS 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
NeurIPS 2022
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
ICML 2022
Scaling Gaussian Processes with Derivative Information Using Variational Inference
NeurIPS 2021
Deep Sigma Point Processes
UAI 2020
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
NeurIPS 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
NeurIPS 2020
Parametric Gaussian Process Regressors
ICML 2020
Exact Gaussian Processes on a Million Data Points
NeurIPS 2019
Scalable Global Optimization via Local Bayesian Optimization
NeurIPS 2019
Simple Black-box Adversarial Attacks
ICML 2019
Constant-Time Predictive Distributions for Gaussian Processes
ICML 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
NeurIPS 2018
Product Kernel Interpolation for Scalable Gaussian Processes
AISTATS 2018
Deep Feature Interpolation for Image Content Changes
CVPR 2017
Discovering and Exploiting Additive Structure for Bayesian Optimization
AISTATS 2017
Bayesian Active Model Selection with an Application to Automated Audiometry
NeurIPS 2015
Differentially Private Bayesian Optimization
ICML 2015
Bayesian Optimization with Inequality Constraints
ICML 2014