Bayesian Optimization with Inequality Constraints. - Jacob R. Gardner; Matt J. Kusner; Zhixiang Eddie Xu; Kilian Q. Weinberger; John P. Cunningham (ICML, 2014)

WOODSTOCC: Extracting Latent Parallelism from a DNA Sequence Aligner on a GPU. - Stephen V. Cole; Jacob R. Gardner; Jeremy D. Buhler (ISPDC, 2014)

Parallel Support Vector Machines in Practice. - Stephen Tyree; Jacob R. Gardner; Kilian Q. Weinberger; Kunal Agrawal 001; John Tran (CoRR, 2014)

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 001 (AAAI, 2015)

Differentially Private Bayesian Optimization. - Matt J. Kusner; Jacob R. Gardner; Roman Garnett; Kilian Q. Weinberger (ICML, 2015)

Bayesian Active Model Selection with an Application to Automated Audiometry. - Jacob R. Gardner; Gustavo Malkomes; Roman Garnett; Kilian Q. Weinberger; Dennis L. Barbour; John P. Cunningham (NIPS, 2015)

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

Deep Manifold Traversal: Changing Labels with Convolutional Features. - Jacob R. Gardner; Matt J. Kusner; Yixuan Li 001; Paul Upchurch; Kilian Q. Weinberger; John E. Hopcroft (CoRR, 2015)

Compressed Support Vector Machines. - Zhixiang Eddie Xu; Jacob R. Gardner; Stephen Tyree; Kilian Q. Weinberger (CoRR, 2015)

Deep Feature Interpolation for Image Content Changes. - Paul Upchurch; Jacob R. Gardner; Kavita Bala; Robert Pless; Noah Snavely; Kilian Q. Weinberger (CoRR, 2016)

Discovering and Exploiting Additive Structure for Bayesian Optimization. - Jacob R. Gardner; Chuan Guo 001; Kilian Q. Weinberger; Roman Garnett; Roger B. Grosse (AISTATS, 2017)

Deep Feature Interpolation for Image Content Changes. - Paul Upchurch; Jacob R. Gardner; Geoff Pleiss; Robert Pless; Noah Snavely; Kavita Bala; Kilian Q. Weinberger (CVPR, 2017)

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

Constant-Time Predictive Distributions for Gaussian Processes. - Geoff Pleiss; Jacob R. Gardner; Kilian Q. Weinberger; Andrew Gordon Wilson (ICML, 2018)

GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. - Jacob R. Gardner; Geoff Pleiss; Kilian Q. Weinberger; David Bindel; Andrew Gordon Wilson (NeurIPS, 2018)

Discovering and Exploiting Structure for Gaussian Processes. - Jacob R. Gardner (2018)

Simple Black-box Adversarial Attacks. - Chuan Guo 001; Jacob R. Gardner; Yurong You; Andrew Gordon Wilson; Kilian Q. Weinberger (ICML, 2019)

Scalable Global Optimization via Local Bayesian Optimization. - David Eriksson; Michael Pearce; Jacob R. Gardner; Ryan Turner; Matthias Poloczek (NeurIPS, 2019)

Exact Gaussian Processes on a Million Data Points. - Ke Alexander Wang; Geoff Pleiss; Jacob R. Gardner; Stephen Tyree; Kilian Q. Weinberger; Andrew Gordon Wilson (NeurIPS, 2019)

Neural Likelihoods for Multi-Output Gaussian Processes. - Martin Jankowiak; Jacob R. Gardner (CoRR, 2019)

Sparse Gaussian Process Regression Beyond Variational Inference. - Martin Jankowiak; Geoff Pleiss; Jacob R. Gardner (CoRR, 2019)

Parametric Gaussian Process Regressors. - Martin Jankowiak; Geoff Pleiss; Jacob R. Gardner (ICML, 2020)

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. - Shali Jiang 001; Daniel R. Jiang; Maximilian Balandat; Brian Karrer; Jacob R. Gardner; Roman Garnett (NeurIPS, 2020)

Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. - Geoff Pleiss; Martin Jankowiak; David Eriksson; Anil Damle; Jacob R. Gardner (NeurIPS, 2020)

Deep Sigma Point Processes. - Martin Jankowiak; Geoff Pleiss; Jacob R. Gardner (UAI, 2020)

Scaling Gaussian Processes with Derivative Information Using Variational Inference. - Misha Padidar; Xinran Zhu; Leo Huang; Jacob R. Gardner; David Bindel (NeurIPS, 2021)

Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. - Jonathan Wenger; Geoff Pleiss; Philipp Hennig; John P. Cunningham; Jacob R. Gardner (CoRR, 2021)

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. - Jonathan Wenger; Geoff Pleiss; Philipp Hennig; John P. Cunningham; Jacob R. Gardner (ICML, 2022)

Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients. - Kyurae Kim; Jisu Oh; Jacob R. Gardner; Adji Bousso Dieng; Hongseok Kim (NeurIPS, 2022)

Local Latent Space Bayesian Optimization over Structured Inputs. - Natalie Maus; Haydn Thomas Jones; Juston Moore; Matt J. Kusner; John Bradshaw; Jacob R. Gardner (NeurIPS, 2022)

Local Bayesian optimization via maximizing probability of descent. - Quan Nguyen; Kaiwen Wu; Jacob R. Gardner; Roman Garnett (NeurIPS, 2022)

wenda_gpu: fast domain adaptation for genomic data. - Ariel A. Hippen; Jake Crawford; Jacob R. Gardner; Casey S. Greene (Bioinform., 2022)

Discovering Many Diverse Solutions with Bayesian Optimization. - Natalie Maus; Kaiwen Wu; David Eriksson; Jacob R. Gardner (AISTATS, 2023)

Extracting or Guessing? Improving Faithfulness of Event Temporal Relation Extraction. - Haoyu Wang 005; Hongming Zhang 009; Yuqian Deng; Jacob R. Gardner; Dan Roth 001; Muhao Chen 001 (EACL, 2023)

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

On the Convergence of Black-Box Variational Inference. - Kyurae Kim; Jisu Oh; Kaiwen Wu; Yi-An Ma; Jacob R. Gardner (NeurIPS, 2023)

The Behavior and Convergence of Local Bayesian Optimization. - Kaiwen Wu; Kyurae Kim; Roman Garnett; Jacob R. Gardner (NeurIPS, 2023)

Variational Gaussian Processes with Decoupled Conditionals. - Xinran Zhu; Kaiwen Wu; Natalie Maus; Jacob R. Gardner; David Bindel (NeurIPS, 2023)

Adversarial Prompting for Black Box Foundation Models. - Natalie Maus; Patrick Chao; Eric Wong 001; Jacob R. Gardner (CoRR, 2023)

Black-Box Variational Inference Converges. - Kyurae Kim; Kaiwen Wu; Jisu Oh; Yi-An Ma; Jacob R. Gardner (CoRR, 2023)

Inverse Protein Folding Using Deep Bayesian Optimization. - Natalie Maus; Yimeng Zeng; Daniel Allen Anderson; Phillip M. Maffettone; Aaron Solomon; Peyton Greenside; Osbert Bastani; Jacob R. Gardner (CoRR, 2023)

Distributional Latent Variable Models with an Application in Active Cognitive Testing. - Robert Kasumba; Dom CP Marticorena; Anja Pahor; Geetha B. Ramani; Imani Goffney; Susanne M. Jaeggi; Aaron R. Seitz; Jacob R. Gardner; Dennis L. Barbour (CoRR, 2023)

Stochastic Approximation with Biased MCMC for Expectation Maximization. - Samuel Gruffaz; Kyurae Kim; Alain Durmus; Jacob R. Gardner (AISTATS, 2024)

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing? - Kyurae Kim; Yi-An Ma; Jacob R. Gardner (AISTATS, 2024)

Large-Scale Gaussian Processes via Alternating Projection. - Kaiwen Wu; Jonathan Wenger; Haydn Thomas Jones; Geoff Pleiss; Jacob R. Gardner (AISTATS, 2024)

Learning Performance-Improving Code Edits. - Alexander Shypula; Aman Madaan; Yimeng Zeng; Uri Alon 002; Jacob R. Gardner; Yiming Yang; Milad Hashemi; Graham Neubig; Parthasarathy Ranganathan; Osbert Bastani; Amir Yazdanbakhsh (ICLR, 2024)

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

Provably Scalable Black-Box Variational Inference with Structured Variational Families. - Joohwan Ko; Kyurae Kim; Woochang Kim; Jacob R. Gardner (ICML, 2024)

Understanding Stochastic Natural Gradient Variational Inference. - Kaiwen Wu; Jacob R. Gardner (ICML, 2024)

Approximation-Aware Bayesian Optimization. - Natalie Maus; Kyurae Kim; David Eriksson; Geoff Pleiss; 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 (NeurIPS, 2024)

Generative Adversarial Model-Based Optimization via Source Critic Regularization. - Michael S. Yao; Yimeng Zeng; Hamsa Bastani; Jacob R. Gardner; James C. Gee; Osbert Bastani (NeurIPS, 2024)

Generative Adversarial Bayesian Optimization for Surrogate Objectives. - Michael S. Yao; Yimeng Zeng; Hamsa Bastani; Jacob R. Gardner; James C. Gee; Osbert Bastani (CoRR, 2024)

Zeroth-Order Fine-Tuning of LLMs with Extreme Sparsity. - Wentao Guo; Jikai Long; Yimeng Zeng; Zirui Liu 001; Xinyu Yang 002; Yide Ran; Jacob R. Gardner; Osbert Bastani; Christopher De Sa; Xiaodong Yu; Beidi Chen; Zhaozhuo Xu (CoRR, 2024)

A Fast, Robust Elliptical Slice Sampling Implementation for Linearly Truncated Multivariate Normal Distributions. - Kaiwen Wu; Jacob R. Gardner (CoRR, 2024)

Improving Structural Diversity of Blackbox LLMs via Chain-of-Specification Prompting. - Halley Young; Yimeng Zeng; Jacob R. Gardner; Osbert Bastani (CoRR, 2024)

Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity. - Wentao Guo; Jikai Long; Yimeng Zeng; Zirui Liu 001; Xinyu Yang 002; Yide Ran; Jacob R. Gardner; Osbert Bastani; Christopher De Sa; Xiaodong Yu; Beidi Chen; Zhaozhuo Xu (ICLR, 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 (CoRR, 2025)

Large Scale Multi-Task Bayesian Optimization with Large Language Models. - 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 (CoRR, 2025)

Tuning Sequential Monte Carlo Samplers via Greedy Incremental Divergence Minimization. - Kyurae Kim; Zuheng Xu; Jacob R. Gardner; Trevor Campbell (CoRR, 2025)

Nearly Dimension-Independent Convergence of Mean-Field Black-Box Variational Inference. - Kyurae Kim; Yi-An Ma; Trevor Campbell; Jacob R. Gardner (CoRR, 2025)

Learned Offline Query Planning via Bayesian Optimization. - Jeffrey Tao; Natalie Maus; Haydn Thomas Jones; Yimeng Zeng; Jacob R. Gardner; Ryan Marcus (Proc. ACM Manag. Data, 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 G. Ives; Yoseph Barash; Cesar de la Fuente-Nunez; Jacob R. Gardner; Mark Yatskar (CoRR, 2025)