Eriksson, David

20 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

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

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

AISTATS 2023 Discovering Many Diverse Solutions with Bayesian Optimization Natalie Maus, Kaiwen Wu, David Eriksson, Jacob Gardner

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

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

AISTATS 2021 Scalable Constrained Bayesian Optimization David Eriksson, Matthias Poloczek

UAI 2021 A Nonmyopic Approach to Cost-Constrained Bayesian Optimization Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias Seeger

UAI 2021 High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces David Eriksson, Martin Jankowiak

ICMLW 2021 Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed A Aly, Ganesh Venkatesh, Maximilian Balandat

UAI 2020 Efficient Rollout Strategies for Bayesian Optimization Eric Lee, David Eriksson, David Bindel, Bolong Cheng, Mike Mccourt

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

NeurIPS 2019 Scalable Global Optimization via Local Bayesian Optimization David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, Matthias Poloczek

NeurIPS 2018 Scaling Gaussian Process Regression with Derivatives David Eriksson, Kun Dong, Eric Lee, David Bindel, Andrew G Wilson

NeurIPS 2017 Scalable Log Determinants for Gaussian Process Kernel Learning Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew G Wilson