Gardner, Jacob

26 publications

NeurIPS 2024 Generative Adversarial Model-Based Optimization via Source Critic Regularization Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob Gardner, James C. Gee, Osbert Bastani

AISTATS 2024 Large-Scale Gaussian Processes via Alternating Projection Kaiwen Wu, Jonathan Wenger, Haydn T Jones, Geoff Pleiss, Jacob Gardner

AISTATS 2024 Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? Kyurae Kim, Yian Ma, Jacob Gardner

AISTATS 2024 Stochastic Approximation with Biased MCMC for Expectation Maximization Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob Gardner

AISTATS 2023 Discovering Many Diverse Solutions with Bayesian Optimization Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner

NeurIPS 2023 On the Convergence of Black-Box Variational Inference Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma, Jacob Gardner

NeurIPS 2023 The Behavior and Convergence of Local Bayesian Optimization Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob Gardner

NeurIPS 2023 Variational Gaussian Processes with Decoupled Conditionals Xinran Zhu, Kaiwen Wu, Natalie Maus, Jacob Gardner, David Bindel

NeurIPS 2022 Local Bayesian Optimization via Maximizing Probability of Descent Quan Nguyen, Kaiwen Wu, Jacob Gardner, Roman Garnett

NeurIPS 2022 Local Latent Space Bayesian Optimization over Structured Inputs Natalie Maus, Haydn Jones, Juston Moore, Matt J Kusner, John Bradshaw, Jacob Gardner

NeurIPS 2022 Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients Kyurae Kim, Jisu Oh, Jacob Gardner, Adji Bousso Dieng, Hongseok Kim

ICML 2022 Preconditioning for Scalable Gaussian Process Hyperparameter Optimization Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John Cunningham, Jacob Gardner

NeurIPS 2021 Scaling Gaussian Processes with Derivative Information Using Variational Inference Misha Padidar, Xinran Zhu, Leo Huang, Jacob Gardner, David Bindel

UAI 2020 Deep Sigma Point Processes Martin Jankowiak, Geoff Pleiss, Jacob Gardner

NeurIPS 2020 Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees Shali Jiang, Daniel Jiang, Maximilian Balandat, Brian Karrer, Jacob Gardner, Roman Garnett

NeurIPS 2020 Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob Gardner

ICML 2020 Parametric Gaussian Process Regressors Martin Jankowiak, Geoff Pleiss, Jacob Gardner

NeurIPS 2019 Exact Gaussian Processes on a Million Data Points Ke Wang, Geoff Pleiss, Jacob Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson

NeurIPS 2019 Scalable Global Optimization via Local Bayesian Optimization David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, Matthias Poloczek

ICML 2019 Simple Black-Box Adversarial Attacks Chuan Guo, Jacob Gardner, Yurong You, Andrew Gordon Wilson, Kilian Weinberger

ICML 2018 Constant-Time Predictive Distributions for Gaussian Processes Geoff Pleiss, Jacob Gardner, Kilian Weinberger, Andrew Gordon Wilson

NeurIPS 2018 GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew G Wilson

CVPR 2017 Deep Feature Interpolation for Image Content Changes Paul Upchurch, Jacob Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Weinberger

NeurIPS 2015 Bayesian Active Model Selection with an Application to Automated Audiometry Jacob Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham

ICML 2015 Differentially Private Bayesian Optimization Matt Kusner, Jacob Gardner, Roman Garnett, Kilian Weinberger

ICML 2014 Bayesian Optimization with Inequality Constraints Jacob Gardner, Matt Kusner, Zhixiang, Kilian Weinberger, John Cunningham