Bakshy, Eytan

30 publications

AutoML 2025 Ax: A Platform for Adaptive Experimentation Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, Mia Garrard, Samuel Daulton, Zhiyuan Jerry Lin, Sebastian Ament, Bernard Beckerman, Eric Onofrey, Paschal Igusti, Cristian Lara, Benjamin Letham, Cesar Cardoso, Shiyun Sunny Shen, Andy Chenyuan Lin, Matthew Grange, Elena Kashtelyan, David Eriksson, Maximilian Balandat, Eytan Bakshy

NeurIPS 2025 Informed Initialization for Bayesian Optimization and Active Learning Carl Hvarfner, David Eriksson, Eytan Bakshy, Maximilian Balandat

ICML 2025 Scalable Gaussian Processes with Latent Kronecker Structure Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy

ICLRW 2025 Α-PFN: In-Context Learning Entropy Search Tom Julian Viering, Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Carl Hvarfner, Frank Hutter, Eytan Bakshy

NeurIPS 2024 Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes Syrine Belakaria, Benjamin Letham, Janardhan Rao Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy

NeurIPSW 2024 Bayesian Optimization of High-Dimensional Outputs with Human Feedback Qing Feng, Zhiyuan Jerry Lin, Yujia Zhang, Benjamin Letham, Jelena Markovic-Voronov, Ryan-Rhys Griffiths, Peter I. Frazier, Eytan Bakshy

ICML 2024 Joint Composite Latent Space Bayesian Optimization Natalie Maus, Zhiyuan Jerry Lin, Maximilian Balandat, Eytan Bakshy

UAI 2024 Response Time Improves Gaussian Process Models for Perception and Preferences Michael Shvartsman, Benjamin Letham, Eytan Bakshy, Stephen Keeley

NeurIPS 2024 Robust Gaussian Processes via Relevance Pursuit Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy

NeurIPSW 2024 Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, Eytan Bakshy

AISTATS 2023 Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-Based Embeddings Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson

ICML 2023 Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information Sam Daulton, Maximilian Balandat, Eytan Bakshy

AISTATS 2023 Sparse Bayesian Optimization Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy

NeurIPS 2023 Unexpected Improvements to Expected Improvement for Bayesian Optimization Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy

AISTATS 2023 qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy, Peter Frazier

AISTATS 2022 Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman

AISTATS 2022 Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes Zhiyuan Jerry Lin, Raul Astudillo, Peter Frazier, Eytan Bakshy

NeurIPS 2022 Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A Osborne, Eytan Bakshy

UAI 2022 Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy

NeurIPSW 2022 One-Shot Optimal Design for Gaussian Process Analysis of Randomized Experiments Jelena Markovic-Voronov, Qing Feng, Eytan Bakshy

ICML 2022 Robust Multi-Objective Bayesian Optimization Under Input Noise Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy

NeurIPS 2021 Bayesian Optimization with High-Dimensional Outputs Wesley J Maddox, Maximilian Balandat, Andrew G Wilson, Eytan Bakshy

NeurIPS 2021 Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs Raul Astudillo, Daniel Jiang, Maximilian Balandat, Eytan Bakshy, Peter Frazier

NeurIPS 2021 Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement Samuel Daulton, Maximilian Balandat, Eytan Bakshy

NeurIPS 2020 BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat, Brian Karrer, Daniel Jiang, Samuel Daulton, Ben Letham, Andrew G Wilson, Eytan Bakshy

NeurIPS 2020 Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization Samuel Daulton, Maximilian Balandat, Eytan Bakshy

NeurIPS 2020 High-Dimensional Contextual Policy Search with Unknown Context Rewards Using Bayesian Optimization Qing Feng, Ben Letham, Hongzi Mao, Eytan Bakshy

NeurIPS 2020 Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization Ben Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy

JMLR 2019 Bayesian Optimization for Policy Search via Online-Offline Experimentation Benjamin Letham, Eytan Bakshy

ICMLW 2019 Real-World Video Adaptation with Reinforcement Learning Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy