**The Behavior and Convergence of Local Bayesian Optimization[Paper]**

Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner

Neural Information Processing Systems (NeurIPS 2023, to appear).

**Variational Gaussian Processes with Decoupled Conditionals[Paper]**

Xinran Zhu, Kaiwen Wu, Natalie Maus, Jacob R. Gardner, David Bindel

Neural Information Processing Systems (NeurIPS 2023, to appear).

**On the Convergence of Black-Box Variational Inference[Paper]**

Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma, Jacob R. Gardner

Neural Information Processing Systems (NeurIPS 2023, to appear).

**Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference.[Paper]**

Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner

International Conference on Machine Learning (ICML 2023). **Oral.**

**Adversarial Prompting for Black Box Foundation Models[Paper]**

Natalie Maus, Patrick Chao, Eric Wong, Jacob Gardner

(Preprint).

**Discovering Many Diverse Solutions with Bayesian Optimization[Paper]**

Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner

Artificial Intelligence and Statistics (AISTATS 2023). **Notable paper.**

**Local Latent Space Bayesian Optimization over Structured Inputs[Paper]**

Natalie Maus, Haydn T Jones, Juston S Moore, Matt J Kusner, John Bradshaw, Jacob R Gardner

Neural Information Processing Systems (NeurIPS 2022).

**Local Bayesian optimization via maximizing probability of descent[Paper]**

Quan Nguyen, Kaiwen Wu, Jacob R Gardner, Roman Garnett

Neural Information Processing Systems (NeurIPS 2022). **Oral.**

**Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients[Paper]**

Kyurae Kim, Jisu Oh, Jacob R Gardner, Adji Bousso Dieng, Hongseok Kim

Neural Information Processing Systems (NeurIPS 2022).

**Preconditioning for Scalable Gaussian Process Hyperparameter Optimization[Paper]**

Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P Cunningham, Jacob R Gardner

International Conference on Machine Learning (ICML 2022). **Long talk.**

**Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction[Paper]**

Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R Gardner, Muhao Chen, Dan Roth

(Preprint.)

**Scaling gaussian processes with derivative information using variational inference[Paper]**

Misha Padidar, Xinran Zhu, Leo Huang, Jacob Gardner, David Bindel

Neural Information Processing Systems (NeurIPS 2021).

**Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM[Paper]**

Jerry Fong, Jacob R Gardner, Jared M Andrews, Amanda F Cashen, Jacqueline E Payton, Kilian Q Weinberger, John R Edwards

Nucleic Acids Research (2021).

**Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees[Paper]**

Shali Jiang, Daniel R Jiang, Maximilian Balandat, Brian Karrer, Jacob R Gardner, Roman Garnett

Neural Information Processing Systems (NeurIPS 2020).

**Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization[Paper]**

Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner

Neural Information Processing Systems (NeurIPS 2020).

**Deep Sigma Point Processes[Paper]**

Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

Conference on Uncertainty in Artifical Intelligence (UAI 2020).

**Parametric Gaussian Process Regressors [Paper]**

Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

International Conference on Machine Learning (ICML 2020).

**Scalable Global Optimization via Local Bayesian Optimization [Paper]**

David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek

Neurial Information Processing Systems (NeurIPS 2019) **Spotlight.**

**Exact Gaussian Processes on a Million Data Points [Paper]**

Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew G. Wilson

Neurial Information Processing Systems (NeurIPS 2019)

**Simple Blackbox Adversarial Attacks [Paper]**

Chuan Guo, Jacob R. Gardner, Yurong You, Andrew G. Wilson, Kilian Q. Weinberger

International Conference on Machine Learning (ICML 2019).

**GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration [Paper]**

Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew G. Wilson

Neurial Information Processing Systems (NeurIPS 2018). **Spotlight.**

**Constant Time Predictive Distributions for Gaussian Processes [Paper]**

Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew G. Wilson

International Conference on Machine Learning (ICML 2018).

**Product Kernel Interpolation for Scalable Gaussian Processes [Paper]**

Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew G. Wilson

Artificial Intelligence and Statistics (AISTATS 2018)

**Discovering and Exploiting Additive Structure for Bayesian Optimization [Paper]**

Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger Grosse

Artificial Intelligence and Statistics (AISTATS 2017)

**Bayesian Active Model Selection with an Application to Automated Audiometry [Paper]**

Jacob R. Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham

Neural Information Processing Systems (NeurIPS 2015)

**Psychophysical Detection Testing with Bayesian Active Learning [Paper]**

Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis Barbour, John P. Cunningham

Uncertainty in Artifical Intelligence (UAI 2015)

**Deep feature interpolation for image content changes [Paper]**

Paul Upchurch*, Jacob R. Gardner*, Kavita Bala, Robert Pless, Noah Snavely, Kilian Q. Weinberger

Computer Vision and Pattern Recognition (CVPR 2016)

* authors contributed equally

**Deep manifold traversal: Changing labels with convolutional features [Paper]**

Jacob R. Gardner*, Paul Upchurch*, Matt J. Kusner, Yixuan Li, Kilian Q. Weinberger, Kavita Bala, John E. Hopcroft

* authors contributed equally

**Differentially Private Bayesian Optimization [Paper]**

Matthew J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger

International Conference on Machine Learning (ICML 2015)

**A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing. [Paper]**

Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen

Association for the Advancement of Artificial Intelligence (AAAI 2015)

**Bayesian Optimization with Inequality Constraints. [Paper]**

Jacob R. Gardner, Matt J. Kusner, Zhixiang Xu, Kilian Q. Weinberger, John P. Cunningham

International Conference on Machine Learning (ICML 2014)