Jacob R. Gardner

According to our database, Jacob R. Gardner authored at least 57 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference.

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

CoRR, 2024

Improving Structural Diversity of Blackbox LLMs via Chain-of-Specification Prompting.

Halley Young, Yimeng Zeng, Jacob R. Gardner, Osbert Bastani

CoRR, 2024

A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions.

Kaiwen Wu, Jacob R. Gardner

CoRR, 2024

Approximation-Aware Bayesian Optimization.

Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner

CoRR, 2024

Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity.

Wentao Guo, Jikai Long, Yimeng Zeng, Zirui Liu, Xinyu Yang, Yide Ran, Jacob R. Gardner, Osbert Bastani, Christopher De Sa, Xiaodong Yu, Beidi Chen, Zhaozhuo Xu

CoRR, 2024

Generative Adversarial Bayesian Optimization for Surrogate Objectives.

Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob R. Gardner, James C. Gee, Osbert Bastani

CoRR, 2024

Understanding Stochastic Natural Gradient Variational Inference.

Kaiwen Wu, Jacob R. Gardner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Provably Scalable Black-Box Variational Inference with Structured Variational Families.

Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Demystifying SGD with Doubly Stochastic Gradients.

Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Learning Performance-Improving Code Edits.

Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Large-Scale Gaussian Processes via Alternating Projection.

Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?

Kyurae Kim, Yi-An Ma, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Stochastic Approximation with Biased MCMC for Expectation Maximization.

Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Distributional Latent Variable Models with an Application in Active Cognitive Testing.

Robert Kasumba, Dom CP Marticorena, Anja Pahor, Geetha B. Ramani, Imani Goffney, Susanne M. Jaeggi, Aaron R. Seitz, Jacob R. Gardner, Dennis L. Barbour

CoRR, 2023

Large-Scale Gaussian Processes via Alternating Projection.

Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner

CoRR, 2023

Inverse Protein Folding Using Deep Bayesian Optimization.

Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip M. Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner

CoRR, 2023

Black-Box Variational Inference Converges.

Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner

CoRR, 2023

Adversarial Prompting for Black Box Foundation Models.

Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner

CoRR, 2023

Variational Gaussian Processes with Decoupled Conditionals.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Behavior and Convergence of Local Bayesian Optimization.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

On the Convergence of Black-Box Variational Inference.

Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference.

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

Proceedings of the International Conference on Machine Learning, 2023

Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction.

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

Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, 2023

Discovering Many Diverse Solutions with Bayesian Optimization.

Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

wenda_gpu: fast domain adaptation for genomic data.

Ariel A. Hippen, Jake Crawford, Jacob R. Gardner, Casey S. Greene

Bioinform., November, 2022

Local Latent Space Bayesian Optimization over Structured Inputs.

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

CoRR, 2022

Local Bayesian optimization via maximizing probability of descent.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Local Latent Space Bayesian Optimization over Structured Inputs.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization.

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

Proceedings of the International Conference on Machine Learning, 2022

2021

Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning.

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

CoRR, 2021

Scaling Gaussian Processes with Derivative Information Using Variational Inference.

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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Deep Sigma Point Processes.

Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees.

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Parametric Gaussian Process Regressors.

Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Sparse Gaussian Process Regression Beyond Variational Inference.

Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner

CoRR, 2019

Neural Likelihoods for Multi-Output Gaussian Processes.

Martin Jankowiak, Jacob R. Gardner

CoRR, 2019

Exact Gaussian Processes on a Million Data Points.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Scalable Global Optimization via Local Bayesian Optimization.

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

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Simple Black-box Adversarial Attacks.

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

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Discovering and Exploiting Structure for Gaussian Processes.

Jacob R. Gardner

PhD thesis, 2018

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration.

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

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Constant-Time Predictive Distributions for Gaussian Processes.

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

Proceedings of the 35th International Conference on Machine Learning, 2018

Product Kernel Interpolation for Scalable Gaussian Processes.

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

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Deep Feature Interpolation for Image Content Changes.

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

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Discovering and Exploiting Additive Structure for Bayesian Optimization.

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

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Deep Feature Interpolation for Image Content Changes.

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

CoRR, 2016

2015

Compressed Support Vector Machines.

Zhixiang Eddie Xu, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger

CoRR, 2015

Deep Manifold Traversal: Changing Labels with Convolutional Features.

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

CoRR, 2015

Psychophysical Detection Testing with Bayesian Active Learning.

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

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Bayesian Active Model Selection with an Application to Automated Audiometry.

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

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Differentially Private Bayesian Optimization.

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

Proceedings of the 32nd International Conference on Machine Learning, 2015

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

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

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Parallel Support Vector Machines in Practice.

Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran

CoRR, 2014

WOODSTOCC: Extracting Latent Parallelism from a DNA Sequence Aligner on a GPU.

Stephen V. Cole, Jacob R. Gardner, Jeremy D. Buhler

Proceedings of the IEEE 13th International Symposium on Parallel and Distributed Computing, 2014

Bayesian Optimization with Inequality Constraints.

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

Proceedings of the 31th International Conference on Machine Learning, 2014