Jacob R. Gardner

jacobrg@seas.upenn.edu

I am an assistant professor in the Computer and Information Science department at the University of Pennsylvania. I do research in machine learning, with a focus on how we can scale probabilistic machine learning methods to the large and complex datasets faced by machine learning these days. My recent work has had an emphasis on Gaussian processes, Bayesian optimization, and other Bayesian machine learning methods.

Before I joined Penn, I was a research scientist at Uber AI Labs. Before this, I was a postdoctoral associate in Operations Research and Information Engineering at Cornell University. I received my Ph.D. in Computer Science from Cornell University, where I was advised by Kilian Weinberger.

Publications

See also my Google scholar page.

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization[Paper]
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
Preprint (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)

Software

Geoff and I founded the GPyTorch project, which aims to implement Gaussian processes in a modular package with strong GPU acceleration. It is deeply embedded in the PyTorch ecosystem, and makes designing complicated models like deep kernel learning both easy to implement and highly efficient. It departs significantly from existing Gaussian process libraries, in that it makes use of modern numerical linear algebra techniques like linear conjugate gradients to perform the fundamental operations required for inference, rather than standard Cholesky based approaches.