Jacob R. Gardner

104 papers A* 31A 9C 1Journal 58Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Adversarial Query Synthesis via Bayesian Optimization. CoRR Jeffrey Tao, Yimeng Zeng, Haydn Thomas Jones, Natalie Maus, Osbert Bastani, Jacob R. Gardner, Ryan Marcus
2026	J	`jnl`	Knowledge Gradient for Preference Learning. CoRR Kaiwen Wu, Jacob R. Gardner
2026	J	`jnl`	Purely Agentic Black-Box Optimization for Biological Design. CoRR Natalie Maus, Yimeng Zeng, Haydn Thomas Jones, Yining Huang, Gaurav Ng Goel, Alden Rose, Kyurae Kim, Hyun-Su Lee, Marcelo Der Torossian Torres, Fangping Wan, Cesar de la Fuente-Nunez, Mark Yatskar, Osbert Bastani, Jacob R. Gardner
2026	J	`jnl`	Stochastic Gradient Variational Inference with Price's Gradient Estimator from Bures-Wasserstein to Parameter Space. CoRR Kyurae Kim, Qiang Fu, Yi-An Ma, Jacob R. Gardner, Trevor Campbell
2025	J	`jnl`	A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design. CoRR Haydn Thomas Jones, Natalie Maus, Josh Magnus Ludan, Maggie Ziyu Huan, Jiaming Liang, Marcelo Der Torossian Torres, Jiatao Liang, Zachary G. Ives, Yoseph Barash, Cesar de la Fuente-Nunez, Jacob R. Gardner, Mark Yatskar
2025	J	`jnl`	Automated High-Level Code Optimization for Warehouse Performance. IEEE Micro Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh
2025	J	`jnl`	Bayesian Distributional Models of Executive Functioning. CoRR Robert Kasumba, Zeyu Lu, Dom CP Marticorena, Mingyang Zhong, Paul Beggs, Anja Pahor, Geetha B. Ramani, Imani Goffney, Susanne M. Jaeggi, Aaron R. Seitz, Jacob R. Gardner, Dennis L. Barbour
2025	J	`jnl`	Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization. CoRR Natalie Maus, Kyurae Kim, Yimeng Zeng, Haydn Thomas Jones, Fangping Wan, Marcelo Der Torossian Torres, Cesar de la Fuente-Nunez, Jacob R. Gardner
2025	J	`jnl`	Distributional Latent Variable Models With an Application in Active Cognitive Testing. IEEE Trans. Cogn. Dev. Syst. Robert Kasumba, Dom CP Marticorena, Anja Pahor, Geetha B. Ramani, Imani Goffney, Susanne M. Jaeggi, Aaron R. Seitz, Jacob R. Gardner, Dennis L. Barbour
2025	J	`jnl`	Large Scale Multi-Task Bayesian Optimization with Large Language Models. CoRR Yimeng Zeng, Natalie Maus, Haydn Thomas Jones, Jeffrey Tao, Fangping Wan, Marcelo Der Torossian Torres, Cesar de la Fuente-Nunez, Ryan Marcus, Osbert Bastani, Jacob R. Gardner
2025	J	`jnl`	Learned Offline Query Planning via Bayesian Optimization. CoRR Jeffrey Tao, Natalie Maus, Haydn Thomas Jones, Yimeng Zeng, Jacob R. Gardner, Ryan Marcus
2025	J	`jnl`	Learned Offline Query Planning via Bayesian Optimization. Proc. ACM Manag. Data Jeffrey Tao, Natalie Maus, Haydn Thomas Jones, Yimeng Zeng, Jacob R. Gardner, Ryan Marcus
2025	J	`jnl`	Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference. CoRR Kyurae Kim, Yi-An Ma, Trevor Campbell, Jacob R. Gardner
2025	—	`ed.`	Proceedings of the International Conference on Automated Machine Learning (AutoML 2025), 8-11 September 2025, Cornell Tech, New York, NY, USA AutoML Leman Akoglu, Carola Doerr, Jan N. van Rijn, Roman Garnett, Jacob R. Gardner
2025	A*	`conf`	Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. ICML Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell
2025	J	`jnl`	Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. CoRR Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell
2025	J	`jnl`	We Still Don't Understand High-Dimensional Bayesian Optimization. CoRR Colin Doumont, Donney Fan, Natalie Maus, Jacob R. Gardner, Henry B. Moss, Geoff Pleiss
2025	A*	`conf`	Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity. ICLR 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
2024	J	`jnl`	A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions. CoRR Kaiwen Wu, Jacob R. Gardner
2024	A*	`conf`	Approximation-Aware Bayesian Optimization. NeurIPS Natalie Maus, Kyurae Kim, David Eriksson, Geoff Pleiss, John P. Cunningham, Jacob R. Gardner
2024	J	`jnl`	Approximation-Aware Bayesian Optimization. CoRR Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
2024	A*	`conf`	Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. NeurIPS Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
2024	J	`jnl`	Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. CoRR Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob R. Gardner, Geoff Pleiss, John P. Cunningham
2024	A*	`conf`	Demystifying SGD with Doubly Stochastic Gradients. ICML Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner
2024	J	`jnl`	Demystifying SGD with Doubly Stochastic Gradients. CoRR Kyurae Kim, Joohwan Ko, Yi-An Ma, Jacob R. Gardner
2024	J	`jnl`	Generative Adversarial Bayesian Optimization for Surrogate Objectives. CoRR Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob R. Gardner, James C. Gee, Osbert Bastani
2024	A*	`conf`	Generative Adversarial Model-Based Optimization via Source Critic Regularization. NeurIPS Michael S. Yao, Yimeng Zeng, Hamsa Bastani, Jacob R. Gardner, James C. Gee, Osbert Bastani
2024	J	`jnl`	Improving Structural Diversity of Blackbox LLMs via Chain-of-Specification Prompting. CoRR Halley Young, Yimeng Zeng, Jacob R. Gardner, Osbert Bastani
2024	—	`ed.`	International Conference on Automated Machine Learning, 9-12 September 2024, Sorbonne Université, Paris, France. AutoML Katharina Eggensperger, Roman Garnett, Joaquin Vanschoren, Marius Lindauer, Jacob R. Gardner
2024	A	`conf`	Large-Scale Gaussian Processes via Alternating Projection. AISTATS Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner
2024	A*	`conf`	Learning Performance-Improving Code Edits. ICLR Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh
2024	A	`conf`	Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? AISTATS Kyurae Kim, Yi-An Ma, Jacob R. Gardner
2024	A*	`conf`	Provably Scalable Black-Box Variational Inference with Structured Variational Families. ICML Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner
2024	J	`jnl`	Provably Scalable Black-Box Variational Inference with Structured Variational Families. CoRR Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner
2024	A	`conf`	Stochastic Approximation with Biased MCMC for Expectation Maximization. AISTATS Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner
2024	J	`jnl`	Stochastic Approximation with Biased MCMC for Expectation Maximization. CoRR Samuel Gruffaz, Kyurae Kim, Alain Oliviero Durmus, Jacob R. Gardner
2024	A*	`conf`	Understanding Stochastic Natural Gradient Variational Inference. ICML Kaiwen Wu, Jacob R. Gardner
2024	J	`jnl`	Understanding Stochastic Natural Gradient Variational Inference. CoRR Kaiwen Wu, Jacob R. Gardner
2024	J	`jnl`	Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity. CoRR 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
2023	J	`jnl`	Adversarial Prompting for Black Box Foundation Models. CoRR Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner
2023	J	`jnl`	Black-Box Variational Inference Converges. CoRR Kyurae Kim, Kaiwen Wu, Jisu Oh, Yi-An Ma, Jacob R. Gardner
2023	A	`conf`	Discovering Many Diverse Solutions with Bayesian Optimization. AISTATS Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner
2023	J	`jnl`	Distributional Latent Variable Models with an Application in Active Cognitive Testing. CoRR Robert Kasumba, Dom CP Marticorena, Anja Pahor, Geetha B. Ramani, Imani Goffney, Susanne M. Jaeggi, Aaron R. Seitz, Jacob R. Gardner, Dennis L. Barbour
2023	A	`conf`	Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction. EACL Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner, Dan Roth, Muhao Chen
2023	—	`ed.`	International Conference on Automated Machine Learning, 12-15 November 2023, Hasso Plattner Institute, Potsdam, Germany. AutoML Aleksandra Faust, Roman Garnett, Colin White, Frank Hutter, Jacob R. Gardner
2023	J	`jnl`	Inverse Protein Folding Using Deep Bayesian Optimization. CoRR Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip M. Maffettone, Aaron Solomon, Peyton Greenside, Osbert Bastani, Jacob R. Gardner
2023	J	`jnl`	Large-Scale Gaussian Processes via Alternating Projection. CoRR Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner
2023	A*	`conf`	Learning to Select Pivotal Samples for Meta Re-weighting. AAAI Yinjun Wu, Adam Stein, Jacob R. Gardner, Mayur Naik
2023	J	`jnl`	Learning to Select Pivotal Samples for Meta Re-weighting. CoRR Yinjun Wu, Adam Stein, Jacob R. Gardner, Mayur Naik
2023	J	`jnl`	Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? CoRR Kyurae Kim, Yi-An Ma, Jacob R. Gardner
2023	A*	`conf`	On the Convergence of Black-Box Variational Inference. NeurIPS Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma, Jacob R. Gardner
2023	A*	`conf`	Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. ICML Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner
2023	J	`jnl`	Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. CoRR Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner
2023	A*	`conf`	The Behavior and Convergence of Local Bayesian Optimization. NeurIPS Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner
2023	J	`jnl`	The Behavior and Convergence of Local Bayesian Optimization. CoRR Kaiwen Wu, Kyurae Kim, Roman Garnett, Jacob R. Gardner
2023	A*	`conf`	Variational Gaussian Processes with Decoupled Conditionals. NeurIPS Xinran Zhu, Kaiwen Wu, Natalie Maus, Jacob R. Gardner, David Bindel
2022	J	`jnl`	Discovering Many Diverse Solutions with Bayesian Optimization. CoRR Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner
2022	J	`jnl`	Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction. CoRR Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner, Dan Roth, Muhao Chen
2022	A*	`conf`	Local Bayesian optimization via maximizing probability of descent. NeurIPS Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett
2022	J	`jnl`	Local Bayesian optimization via maximizing probability of descent. CoRR Quan Nguyen, Kaiwen Wu, Jacob R. Gardner, Roman Garnett
2022	A*	`conf`	Local Latent Space Bayesian Optimization over Structured Inputs. NeurIPS Natalie Maus, Haydn Thomas Jones, Juston Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
2022	J	`jnl`	Local Latent Space Bayesian Optimization over Structured Inputs. CoRR Natalie Maus, Haydn Thomas Jones, Juston S. Moore, Matt J. Kusner, John Bradshaw, Jacob R. Gardner
2022	A*	`conf`	Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. NeurIPS Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim
2022	J	`jnl`	Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. CoRR Kyurae Kim, Jisu Oh, Jacob R. Gardner, Adji Bousso Dieng, Hongseok Kim
2022	A*	`conf`	Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
2022	J	`jnl`	wenda_gpu: fast domain adaptation for genomic data. Bioinform. Ariel A. Hippen, Jake Crawford, Jacob R. Gardner, Casey S. Greene
2021	J	`jnl`	Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
2021	A*	`conf`	Scaling Gaussian Processes with Derivative Information Using Variational Inference. NeurIPS Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel
2021	J	`jnl`	Scaling Gaussian Processes with Derivative Information Using Variational Inference. CoRR Misha Padidar, Xinran Zhu, Leo Huang, Jacob R. Gardner, David Bindel
2020	A	`conf`	Deep Sigma Point Processes. UAI Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2020	J	`jnl`	Deep Sigma Point Processes. CoRR Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2020	A*	`conf`	Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. NeurIPS Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
2020	J	`jnl`	Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. CoRR Shali Jiang, Daniel R. Jiang, Maximilian Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett
2020	A*	`conf`	Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. NeurIPS Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
2020	J	`jnl`	Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. CoRR Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
2020	A*	`conf`	Parametric Gaussian Process Regressors. ICML Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2019	A*	`conf`	Exact Gaussian Processes on a Million Data Points. NeurIPS Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
2019	J	`jnl`	Exact Gaussian Processes on a Million Data Points. CoRR Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson
2019	J	`jnl`	Neural Likelihoods for Multi-Output Gaussian Processes. CoRR Martin Jankowiak, Jacob R. Gardner
2019	A*	`conf`	Scalable Global Optimization via Local Bayesian Optimization. NeurIPS David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek
2019	J	`jnl`	Scalable Global Optimization via Local Bayesian Optimization. CoRR David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek
2019	A*	`conf`	Simple Black-box Adversarial Attacks. ICML Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
2019	J	`jnl`	Simple Black-box Adversarial Attacks. CoRR Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger
2019	J	`jnl`	Sparse Gaussian Process Regression Beyond Variational Inference. CoRR Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner
2018	A*	`conf`	Constant-Time Predictive Distributions for Gaussian Processes. ICML Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
2018	J	`jnl`	Constant-Time Predictive Distributions for Gaussian Processes. CoRR Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson
2018	—	`—`	Discovering and Exploiting Structure for Gaussian Processes. Jacob R. Gardner
2018	A*	`conf`	GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. NeurIPS Jacob R. Gardner, Geoff Pleiss, Kilian Q. Weinberger, David Bindel, Andrew Gordon Wilson
2018	J	`jnl`	GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. CoRR Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson
2018	A	`conf`	Product Kernel Interpolation for Scalable Gaussian Processes. AISTATS Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
2018	J	`jnl`	Product Kernel Interpolation for Scalable Gaussian Processes. CoRR Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson
2017	A*	`conf`	Deep Feature Interpolation for Image Content Changes. CVPR Paul Upchurch, Jacob R. Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Q. Weinberger
2017	A	`conf`	Discovering and Exploiting Additive Structure for Bayesian Optimization. AISTATS Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse
2016	J	`jnl`	Deep Feature Interpolation for Image Content Changes. CoRR Paul Upchurch, Jacob R. Gardner, Kavita Bala, Robert Pless, Noah Snavely, Kilian Q. Weinberger
2015	A*	`conf`	A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing. AAAI Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen
2015	—	`conf`	Bayesian Active Model Selection with an Application to Automated Audiometry. NIPS Jacob R. Gardner, Gustavo Malkomes, Roman Garnett, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
2015	J	`jnl`	Compressed Support Vector Machines. CoRR Zhixiang Eddie Xu, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger
2015	J	`jnl`	Deep Manifold Traversal: Changing Labels with Convolutional Features. CoRR Jacob R. Gardner, Matt J. Kusner, Yixuan Li, Paul Upchurch, Kilian Q. Weinberger, John E. Hopcroft
2015	A*	`conf`	Differentially Private Bayesian Optimization. ICML Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger
2015	A	`conf`	Psychophysical Detection Testing with Bayesian Active Learning. UAI Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham
2014	J	`jnl`	A Reduction of the Elastic Net to Support Vector Machines with an Application to GPU Computing. CoRR Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen
2014	A*	`conf`	Bayesian Optimization with Inequality Constraints. ICML Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham
2014	J	`jnl`	Parallel Support Vector Machines in Practice. CoRR Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran
2014	C	`conf`	WOODSTOCC: Extracting Latent Parallelism from a DNA Sequence Aligner on a GPU. ISPDC Stephen V. Cole, Jacob R. Gardner, Jeremy D. Buhler