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ORCIDJacob R. Gardner
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Books and Theses
- 2018
[b1]Jacob R. Gardner:
Discovering and Exploiting Structure for Gaussian Processes. Cornell University, USA, 2018
Journal Articles
- 2022
- [j1]Ariel A. Hippen, Jake Crawford, Jacob R. Gardner, Casey S. Greene
:
wenda_gpu: fast domain adaptation for genomic data. Bioinform. 38(22): 5129-5130 (2022)
Conference and Workshop Papers
- 2024
- [c36]Kyurae Kim, Yi-An Ma, Jacob R. Gardner:
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS 2024: 235-243 - [c35]Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner:
Stochastic Approximation with Biased MCMC for Expectation Maximization. AISTATS 2024: 2332-2340 - [c34]Kaiwen Wu, Jonathan Wenger, Haydn T. Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. AISTATS 2024: 2620-2628 - [c33]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 - [c32]Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner:
Demystifying SGD with Doubly Stochastic Gradients. ICML 2024 - [c31]Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner:
Provably Scalable Black-Box Variational Inference with Structured Variational Families. ICML 2024 - [c30]Kaiwen Wu, Jacob R. Gardner:
Understanding Stochastic Natural Gradient Variational Inference. ICML 2024 - 2023
- [c29]Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner:
Discovering Many Diverse Solutions with Bayesian Optimization. AISTATS 2023: 1779-1798 - [c28]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: 541-553 - [c27]Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner:
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. ICML 2023: 16853-16876 - [c26]Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner:
On the Convergence of Black-Box Variational Inference. NeurIPS 2023 - [c25]Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner:
The Behavior and Convergence of Local Bayesian Optimization. NeurIPS 2023 - [c24]Xinran Zhu, Kaiwen Wu, Natalie Maus, Jacob R. Gardner, David Bindel:
Variational Gaussian Processes with Decoupled Conditionals. NeurIPS 2023 - 2022
- [c23]Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner:
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML 2022: 23751-23780 - [c22]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 - [c21]Natalie Maus, Haydn Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner:
Local Latent Space Bayesian Optimization over Structured Inputs. NeurIPS 2022 - [c20]Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett:
Local Bayesian optimization via maximizing probability of descent. NeurIPS 2022 - 2021
- [c19]Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel:
Scaling Gaussian Processes with Derivative Information Using Variational Inference. NeurIPS 2021: 6442-6453 - 2020
- [c18]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Parametric Gaussian Process Regressors. ICML 2020: 4702-4712 - [c17]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 - [c16]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 - [c15]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Deep Sigma Point Processes. UAI 2020: 789-798 - 2019
- [c14]Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger:
Simple Black-box Adversarial Attacks. ICML 2019: 2484-2493 - [c13]David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek:
Scalable Global Optimization via Local Bayesian Optimization. NeurIPS 2019: 5497-5508 - [c12]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: 14622-14632 - 2018
- [c11]Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson:
Product Kernel Interpolation for Scalable Gaussian Processes. AISTATS 2018: 1407-1416 - [c10]Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson:
Constant-Time Predictive Distributions for Gaussian Processes. ICML 2018: 4111-4120 - [c9]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: 7587-7597 - 2017
- [c8]Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse:
Discovering and Exploiting Additive Structure for Bayesian Optimization. AISTATS 2017: 1311-1319 - [c7]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: 6090-6099 - 2015
- [c6]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: 3210-3216 - [c5]Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger:
Differentially Private Bayesian Optimization. ICML 2015: 918-927 - [c4]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: 2386-2394 - [c3]Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham:
Psychophysical Detection Testing with Bayesian Active Learning. UAI 2015: 286-295 - 2014
- [c2]Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham:
Bayesian Optimization with Inequality Constraints. ICML 2014: 937-945 - [c1]Stephen V. Cole, Jacob R. Gardner, Jeremy D. Buhler:
WOODSTOCC: Extracting Latent Parallelism from a DNA Sequence Aligner on a GPU. ISPDC 2014: 197-204
Editorship
- 2023
- [e1]Aleksandra Faust, Roman Garnett, Colin White, Frank Hutter, Jacob R. Gardner:
International Conference on Automated Machine Learning, 12-15 November 2023, Hasso Plattner Institute, Potsdam, Germany. Proceedings of Machine Learning Research 224, PMLR 2023 [contents]
Informal and Other Publications
- 2024
- [i40]Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner:
Provably Scalable Black-Box Variational Inference with Structured Variational Families. CoRR abs/2401.10989 (2024) - [i39]Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob R. Gardner, James C. Gee, Osbert Bastani:
Generative Adversarial Bayesian Optimization for Surrogate Objectives. CoRR abs/2402.06532 (2024) - [i38]Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Jacob R. Gardner:
Stochastic Approximation with Biased MCMC for Expectation Maximization. CoRR abs/2402.17870 (2024) - [i37]Kyurae Kim, Joohwan Ko, Yi-An Ma, Jacob R. Gardner:
Demystifying SGD with Doubly Stochastic Gradients. CoRR abs/2406.00920 (2024) - [i36]Kaiwen Wu, Jacob R. Gardner:
Understanding Stochastic Natural Gradient Variational Inference. CoRR abs/2406.01870 (2024) - [i35]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 abs/2406.02913 (2024) - [i34]Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner:
Approximation-Aware Bayesian Optimization. CoRR abs/2406.04308 (2024) - [i33]Kaiwen Wu, Jacob R. Gardner:
A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions. CoRR abs/2407.10449 (2024) - [i32]Halley Young, Yimeng Zeng, Jacob R. Gardner, Osbert Bastani:
Improving Structural Diversity of Blackbox LLMs via Chain-of-Specification Prompting. CoRR abs/2408.06186 (2024) - 2023
- [i31]Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner:
Adversarial Prompting for Black Box Foundation Models. CoRR abs/2302.04237 (2023) - [i30]Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner:
Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. CoRR abs/2303.10472 (2023) - [i29]Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner:
Black-Box Variational Inference Converges. CoRR abs/2305.15349 (2023) - [i28]Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner:
The Behavior and Convergence of Local Bayesian Optimization. CoRR abs/2305.15572 (2023) - [i27]Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip M. Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner:
Inverse Protein Folding Using Deep Bayesian Optimization. CoRR abs/2305.18089 (2023) - [i26]Kyurae Kim, Yi-An Ma, Jacob R. Gardner:
Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? CoRR abs/2307.14642 (2023) - [i25]Kaiwen Wu, Jonathan Wenger, Haydn Jones, Geoff Pleiss, Jacob R. Gardner:
Large-Scale Gaussian Processes via Alternating Projection. CoRR abs/2310.17137 (2023) - [i24]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 abs/2312.09316 (2023) - 2022
- [i23]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 abs/2201.11872 (2022) - [i22]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 abs/2206.06295 (2022) - [i21]Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner, Dan Roth, Muhao Chen:
Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction. CoRR abs/2210.04992 (2022) - [i20]Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner:
Discovering Many Diverse Solutions with Bayesian Optimization. CoRR abs/2210.10953 (2022) - [i19]Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett:
Local Bayesian optimization via maximizing probability of descent. CoRR abs/2210.11662 (2022) - 2021
- [i18]Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner:
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR abs/2107.00243 (2021) - [i17]Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel:
Scaling Gaussian Processes with Derivative Information Using Variational Inference. CoRR abs/2107.04061 (2021) - 2020
- [i16]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Deep Sigma Point Processes. CoRR abs/2002.09112 (2020) - [i15]Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner:
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. CoRR abs/2006.11267 (2020) - [i14]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 abs/2006.15779 (2020) - 2019
- [i13]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 abs/1903.08114 (2019) - [i12]Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger:
Simple Black-box Adversarial Attacks. CoRR abs/1905.07121 (2019) - [i11]Martin Jankowiak, Jacob R. Gardner:
Neural Likelihoods for Multi-Output Gaussian Processes. CoRR abs/1905.13697 (2019) - [i10]David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek:
Scalable Global Optimization via Local Bayesian Optimization. CoRR abs/1910.01739 (2019) - [i9]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Sparse Gaussian Process Regression Beyond Variational Inference. CoRR abs/1910.07123 (2019) - 2018
- [i8]Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson:
Product Kernel Interpolation for Scalable Gaussian Processes. CoRR abs/1802.08903 (2018) - [i7]Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson:
Constant-Time Predictive Distributions for Gaussian Processes. CoRR abs/1803.06058 (2018) - [i6]Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson:
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. CoRR abs/1809.11165 (2018) - 2016
- [i5]Paul Upchurch, Jacob R. Gardner, Kavita Bala, Robert Pless, Noah Snavely, Kilian Q. Weinberger:
Deep Feature Interpolation for Image Content Changes. CoRR abs/1611.05507 (2016) - 2015
- [i4]Zhixiang Eddie Xu, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger:
Compressed Support Vector Machines. CoRR abs/1501.06478 (2015) - [i3]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 abs/1511.06421 (2015) - 2014
- [i2]Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran:
Parallel Support Vector Machines in Practice. CoRR abs/1404.1066 (2014) - [i1]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 abs/1409.1976 (2014)
Coauthor Index
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