Jacob R. Gardner

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 61

Top Topics

Support Vector Machine

Latent Variable Models

Variational Inference

Gaussian Process

Top Venues

Publications

Kaiwen Wu, Jacob R. Gardner
Understanding Stochastic Natural Gradient Variational Inference. CoRR (2024)
Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner
Stochastic Approximation with Biased MCMC for Expectation Maximization. AISTATS (2024)
Kaiwen Wu, Jacob R. Gardner
A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions. CoRR (2024)
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
Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity. CoRR (2024)
Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner
Large-Scale Gaussian Processes via Alternating Projection. AISTATS (2024)
Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner
Provably Scalable Black-Box Variational Inference with Structured Variational Families. CoRR (2024)
Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob R. Gardner, James C. Gee, Osbert Bastani
Generative Adversarial Bayesian Optimization for Surrogate Objectives. CoRR (2024)
Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh
Learning Performance-Improving Code Edits. ICLR (2024)
Kyurae Kim, Yi-An Ma, Jacob R. Gardner
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS (2024)
Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Jacob R. Gardner
Stochastic Approximation with Biased MCMC for Expectation Maximization. CoRR (2024)
Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
Approximation-Aware Bayesian Optimization. CoRR (2024)
Kyurae Kim, Joohwan Ko, Yi-An Ma, Jacob R. Gardner
Demystifying SGD with Doubly Stochastic Gradients. CoRR (2024)
Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner
Inverse Protein Folding Using Deep Bayesian Optimization. CoRR (2023)
Kyurae Kim, Yian Ma, Jacob R. Gardner
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? CoRR (2023)
Robert Kasumba, Dom CP Marticorena, Anja Pahor, Geetha B. Ramani, Imani Goffney, Susanne M. Jaeggi, Aaron R. Seitz, Jacob R. Gardner, Dennis L. Barbour
Distributional Latent Variable Models with an Application in Active Cognitive Testing. CoRR (2023)
Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner
Adversarial Prompting for Black Box Foundation Models. CoRR (2023)
Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner, Dan Roth, Muhao Chen
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction. EACL (2023)
Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. CoRR (2023)
Kyurae Kim, Kaiwen Wu, Jisu Oh, Yian Ma, Jacob R. Gardner
Black-Box Variational Inference Converges. CoRR (2023)
Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner
Large-Scale Gaussian Processes via Alternating Projection. CoRR (2023)
Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. ICML (2023)
Kyurae Kim, Jisu Oh, Kaiwen Wu, Yian Ma, Jacob R. Gardner
On the Convergence of Black-Box Variational Inference. NeurIPS (2023)
Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner
Discovering Many Diverse Solutions with Bayesian Optimization. AISTATS (2023)
Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner
The Behavior and Convergence of Local Bayesian Optimization. CoRR (2023)
Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner
Discovering Many Diverse Solutions with Bayesian Optimization. CoRR (2022)
Natalie Maus, Haydn T. Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
Local Latent Space Bayesian Optimization over Structured Inputs. CoRR (2022)
Ariel A. Hippen, Jake Crawford, Jacob R. Gardner, Casey S. Greene
wenda_gpu: fast domain adaptation for genomic data. Bioinform. 38 (22) (2022)
Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett
Local Bayesian optimization via maximizing probability of descent. NeurIPS (2022)
Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett
Local Bayesian optimization via maximizing probability of descent. CoRR (2022)
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML (2022)
Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner, Dan Roth, Muhao Chen
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction. CoRR (2022)
Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. NeurIPS (2022)
Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. CoRR (2022)
Natalie Maus, Haydn Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
Local Latent Space Bayesian Optimization over Structured Inputs. NeurIPS (2022)
Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel
Scaling Gaussian Processes with Derivative Information Using Variational Inference. CoRR (2021)
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR (2021)
Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel
Scaling Gaussian Processes with Derivative Information Using Variational Inference. NeurIPS (2021)
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
Deep Sigma Point Processes. UAI (2020)
Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. CoRR (2020)
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. CoRR (2020)
Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. NeurIPS (2020)
Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. NeurIPS (2020)
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
Deep Sigma Point Processes. CoRR (2020)
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
Parametric Gaussian Process Regressors. ICML (2020)
Martin Jankowiak, Jacob R. Gardner
Neural Likelihoods for Multi-Output Gaussian Processes. CoRR (2019)
Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
Sparse Gaussian Process Regression Beyond Variational Inference. CoRR (2019)
Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
Simple Black-box Adversarial Attacks. CoRR (2019)
Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
Simple Black-box Adversarial Attacks. ICML (2019)
Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
Exact Gaussian Processes on a Million Data Points. NeurIPS (2019)
David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek
Scalable Global Optimization via Local Bayesian Optimization. CoRR (2019)
Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
Exact Gaussian Processes on a Million Data Points. CoRR (2019)
David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek
Scalable Global Optimization via Local Bayesian Optimization. NeurIPS (2019)
Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
Constant-Time Predictive Distributions for Gaussian Processes. ICML (2018)
Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. CoRR (2018)
Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew Gordon Wilson
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. NeurIPS (2018)
Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
Product Kernel Interpolation for Scalable Gaussian Processes. CoRR (2018)
Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
Constant-Time Predictive Distributions for Gaussian Processes. CoRR (2018)
Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
Product Kernel Interpolation for Scalable Gaussian Processes. AISTATS (2018)
Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse
Discovering and Exploiting Additive Structure for Bayesian Optimization. AISTATS (2017)
Paul Upchurch, Jacob R. Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Q. Weinberger
Deep Feature Interpolation for Image Content Changes. CVPR (2017)
Paul Upchurch, Jacob R. Gardner, Kavita Bala, Robert Pless, Noah Snavely, Kilian Q. Weinberger
Deep Feature Interpolation for Image Content Changes. CoRR (2016)
Jacob R. Gardner, Matt J. Kusner, Yixuan Li, Paul Upchurch, Kilian Q. Weinberger, John E. Hopcroft
Deep Manifold Traversal: Changing Labels with Convolutional Features. CoRR (2015)
Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger
Differentially Private Bayesian Optimization. ICML (2015)
Zhixiang Eddie Xu, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger
Compressed Support Vector Machines. CoRR (2015)
Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing. AAAI (2015)
Jacob R. Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
Bayesian Active Model Selection with an Application to Automated Audiometry. NIPS (2015)
Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
Psychophysical Detection Testing with Bayesian Active Learning. UAI (2015)
Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham
Bayesian Optimization with Inequality Constraints. ICML (2014)
Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran
Parallel Support Vector Machines in Practice. CoRR (2014)
Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen
A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing. CoRR (2014)
Stephen V. Cole, Jacob R. Gardner, Jeremy D. Buhler
WOODSTOCC: Extracting Latent Parallelism from a DNA Sequence Aligner on a GPU. ISPDC (2014)