Jacob R. Gardner
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
26
Venues
8
Active years
2014–2025
Best venue rank
A*
Where they publish
Papers
26 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICLR | 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 |
| 2025 | ICML | Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. | Kyurae Kim, Zuheng Xu, Jacob R. Gardner, Trevor Campbell |
| 2024 | AISTATS | Stochastic Approximation with Biased MCMC for Expectation Maximization. | Samuel Gruffaz, Kyurae Kim, Alain Durmus, Jacob R. Gardner |
| 2024 | AISTATS | Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? | Kyurae Kim, Yi-An Ma, Jacob R. Gardner |
| 2024 | AISTATS | Large-Scale Gaussian Processes via Alternating Projection. | Kaiwen Wu, Jonathan Wenger, Haydn Thomas Jones, Geoff Pleiss, Jacob R. Gardner |
| 2024 | ICLR | Learning Performance-Improving Code Edits. | Alexander Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh |
| 2024 | ICML | Demystifying SGD with Doubly Stochastic Gradients. | Kyurae Kim, Joohwan Ko, Yian Ma, Jacob R. Gardner |
| 2024 | ICML | Provably Scalable Black-Box Variational Inference with Structured Variational Families. | Joohwan Ko, Kyurae Kim, Woochang Kim, Jacob R. Gardner |
| 2024 | ICML | Understanding Stochastic Natural Gradient Variational Inference. | Kaiwen Wu, Jacob R. Gardner |
| 2023 | AAAI | Learning to Select Pivotal Samples for Meta Re-weighting. | Yinjun Wu, Adam Stein, Jacob R. Gardner, Mayur Naik |
| 2023 | AISTATS | Discovering Many Diverse Solutions with Bayesian Optimization. | Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner |
| 2023 | EACL | Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction. | Haoyu Wang, Hongming Zhang, Yuqian Deng, Jacob R. Gardner, Dan Roth, Muhao Chen |
| 2023 | ICML | Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference. | Kyurae Kim, Kaiwen Wu, Jisu Oh, Jacob R. Gardner |
| 2022 | ICML | Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. | Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner |
| 2020 | ICML | Parametric Gaussian Process Regressors. | Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner |
| 2020 | UAI | Deep Sigma Point Processes. | Martin Jankowiak, Geoff Pleiss, Jacob R. Gardner |
| 2019 | ICML | Simple Black-box Adversarial Attacks. | Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger |
| 2018 | AISTATS | Product Kernel Interpolation for Scalable Gaussian Processes. | Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson |
| 2018 | ICML | Constant-Time Predictive Distributions for Gaussian Processes. | Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson |
| 2017 | AISTATS | Discovering and Exploiting Additive Structure for Bayesian Optimization. | Jacob R. Gardner, Chuan Guo, Kilian Q. Weinberger, Roman Garnett, Roger B. Grosse |
| 2017 | CVPR | Deep Feature Interpolation for Image Content Changes. | Paul Upchurch, Jacob R. Gardner, Geoff Pleiss, Robert Pless, Noah Snavely, Kavita Bala, Kilian Q. Weinberger |
| 2015 | AAAI | 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 |
| 2015 | ICML | Differentially Private Bayesian Optimization. | Matt J. Kusner, Jacob R. Gardner, Roman Garnett, Kilian Q. Weinberger |
| 2015 | UAI | Psychophysical Detection Testing with Bayesian Active Learning. | Jacob R. Gardner, Xinyu Song, Kilian Q. Weinberger, Dennis L. Barbour, John P. Cunningham |
| 2014 | ICML | Bayesian Optimization with Inequality Constraints. | Jacob R. Gardner, Matt J. Kusner, Zhixiang Eddie Xu, Kilian Q. Weinberger, John P. Cunningham |
| 2014 | ISPDC | WOODSTOCC: Extracting Latent Parallelism from a DNA Sequence Aligner on a GPU. | Stephen V. Cole, Jacob R. Gardner, Jeremy D. Buhler |