← Authors

Jacob Gardner

30 papers · 2014–2024 · 7 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (7) πŸ—ΊοΈ Taxonomy Completionist (50) 🌈 Renaissance Researcher (8) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (14) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (10) 🀝 Dynamic Duo (10) πŸ”¬ Deep Specialist (20) πŸ† Keyword Champion (3) πŸ“ˆ Trend Setter ⚑ Prolific Year (6) πŸ—ƒοΈ Keyword Collector (124) ❓ The Questioner (2) πŸ’Ž Century Club (30) πŸ”₯ Unstoppable (8)

Conferences

NIPS (14) AISTATS (6) ICML (6) AAAI (1) CVPR (1) EACL (1) UAI (1)

Top co-authors

Geoff Pleiss (10) Kilian Weinberger (7) Kaiwen Wu (6) Roman Garnett (6) Kyurae Kim (5) David Eriksson (3) Andrew Gordon Wilson (3) David Bindel (3) Martin Jankowiak (3) Kilian Q. Weinberger (3)

Research topics

Differential Privacy (1)

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