Gardner, Jacob R.

22 publications

NeurIPS 2025 A Dataset for Distilling Knowledge Priors from Literature for Therapeutic Design Haydn Thomas Jones, Natalie Maus, Josh magnus Ludan, Maggie Ziyu Huan, Jiaming Liang, Marcelo Der Torossian Torres, Jiatao Liang, Zachary Ives, Yoseph Barash, Cesar de la Fuente-Nunez, Jacob R. Gardner, Mark Yatskar

NeurIPS 2025 Covering Multiple Objectives with a Small Set of Solutions Using Bayesian Optimization Natalie Maus, Kyurae Kim, Yimeng Zeng, Haydn Thomas Jones, Fangping Wan, Marcelo Der Torossian Torres, Cesar de la Fuente-Nunez, Jacob R. Gardner

NeurIPS 2025 Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference Kyurae Kim, Yian Ma, Trevor Campbell, Jacob R. Gardner

ICML 2025 Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell

ICLR 2025 Zeroth-Order Fine-Tuning of LLMs with Transferable Static 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

NeurIPSW 2024 A Fast, Robust Elliptical Slice Sampling Method for Truncated Multivariate Normal Distributions Kaiwen Wu, Jacob R. Gardner

ICLRW 2024 Antibody Design with Constrained Bayesian Optimization Yimeng Zeng, Hunter Elliott, Phillip Maffettone, Peyton Greenside, Osbert Bastani, Jacob R. Gardner

NeurIPS 2024 Approximation-Aware Bayesian Optimization Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner

NeurIPS 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

ICML 2024 Demystifying SGD with Doubly Stochastic Gradients Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner

ICLR 2024 Learning Performance-Improving Code Edits Alexander G Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh

ICML 2024 Provably Scalable Black-Box Variational Inference with Structured Variational Families Joohwan Ko, Kyurae Kim, Woo Chang Kim, Jacob R. Gardner

ICML 2024 Understanding Stochastic Natural Gradient Variational Inference Kaiwen Wu, Jacob R. Gardner

ICMLW 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

ICMLW 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

ICMLW 2023 Black Box Adversarial Prompting for Foundation Models Natalie Maus, Patrick Chao, Eric Wong, Jacob R. Gardner

AAAI 2023 Learning to Select Pivotal Samples for Meta Re-Weighting Yinjun Wu, Adam Stein, Jacob R. Gardner, Mayur Naik

ICML 2023 Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner

AISTATS 2018 Product Kernel Interpolation for Scalable Gaussian Processes Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson

AISTATS 2017 Discovering and Exploiting Additive Structure for Bayesian Optimization Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse

AAAI 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

UAI 2015 Psychophysical Detection Testing with Bayesian Active Learning Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham