Jacob Gardner
30 papers · 2014–2024 · 7 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (7) 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🧭 Keyword Pioneer 🏃 Academic Marathon (10)
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Keyword Pioneer
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Hot Topic Early Bird
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Academic Marathon
(10)
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Dynamic Duo
(10)
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Deep Specialist
(20)
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Keyword Champion
(3)
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Trend Setter
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Prolific Year
(6)
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Keyword Collector
(124)
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(2)
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Century Club
(30)
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Unstoppable
(8)
Conferences
NIPS (14)
AISTATS (6)
ICML (6)
AAAI (1)
CVPR (1)
EACL (1)
UAI (1)
Top co-authors
Research topics
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
Large-Scale Gaussian Processes via Alternating Projection
AISTATS 2024
Stochastic Approximation with Biased MCMC for Expectation Maximization
AISTATS 2024
Generative Adversarial Model-Based Optimization via Source Critic Regularization
NIPS 2024
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?
AISTATS 2024
Learning to Select Pivotal Samples for Meta Re-weighting
AAAI 2023
Discovering Many Diverse Solutions with Bayesian Optimization
AISTATS 2023
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction
EACL 2023
On the Convergence of Black-Box Variational Inference
NIPS 2023
Variational Gaussian Processes with Decoupled Conditionals
NIPS 2023
The Behavior and Convergence of Local Bayesian Optimization
NIPS 2023
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
ICML 2022
Local Bayesian optimization via maximizing probability of descent
NIPS 2022
Local Latent Space Bayesian Optimization over Structured Inputs
NIPS 2022
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
NIPS 2022
Scaling Gaussian Processes with Derivative Information Using Variational Inference
NIPS 2021
Parametric Gaussian Process Regressors
ICML 2020
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
NIPS 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
NIPS 2020
Deep Sigma Point Processes
UAI 2020
Scalable Global Optimization via Local Bayesian Optimization
NIPS 2019
Exact Gaussian Processes on a Million Data Points
NIPS 2019
Simple Black-box Adversarial Attacks
ICML 2019
Product Kernel Interpolation for Scalable Gaussian Processes
AISTATS 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration
NIPS 2018
Constant-Time Predictive Distributions for Gaussian Processes
ICML 2018
Discovering and Exploiting Additive Structure for Bayesian Optimization
AISTATS 2017
Deep Feature Interpolation for Image Content Changes
CVPR 2017
Differentially Private Bayesian Optimization
ICML 2015
Bayesian Active Model Selection with an Application to Automated Audiometry
NIPS 2015
Bayesian Optimization with Inequality Constraints
ICML 2014