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Jacob R. Gardner
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2020 – today
- 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 - [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
- [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
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 - [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] - [i31]Natalie Maus, Patrick Chao, Eric Wong
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
- [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) - [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 - [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
- [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 - [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
- [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 - [i16]Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner:
Deep Sigma Point Processes. CoRR abs/2002.09112 (2020) - [i15]Geoff Pleiss, Martin Jankowiak, David Eriksson
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)
2010 – 2019
- 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 - [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
- [b1]Jacob R. Gardner:
Discovering and Exploiting Structure for Gaussian Processes. Cornell University, USA, 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 - [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) - 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
Deep Feature Interpolation for Image Content Changes. CVPR 2017: 6090-6099 - 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
- [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 - [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
- [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 - [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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