Effects of short-term synaptic plasticity in a local microcircuit on cell firing. - David Eriksson; Erik Fransén; Yuri Zilberter; Anders Lansner (Neurocomputing, 2003)

Extracting and exposing predictive cortical columns for selective attention. - David Eriksson (Neurocomputing, 2004)

2HDMC - two-Higgs-doublet model calculator. - David Eriksson; Johan Rathsman; Oscar Stål (Comput. Phys. Commun., 2010)

Erratum for "2HDMC - two-Higgs-doublet model calculator" [Comput. Phys. Commun. 181 (2010) 189]. - David Eriksson; Johan Rathsman; Oscar Stål (Comput. Phys. Commun., 2010)

Coordinating new product development with supply chain management. - Per Hilletofth; David Eriksson (Ind. Manag. Data Syst., 2010)

A Linear Model of Phase-Dependent Power Correlations in Neuronal Oscillations. - David Eriksson; Raul Vicente; Kerstin Erika Schmidt (Frontiers Comput. Neurosci., 2011)

Matching supply chain strategy with business strategy and the results of a mismatch. - David Eriksson; Carl Philip T. Hedenstierna (Int. J. Manuf. Res., 2012)

Two sides of a token: coordinating demand and supply at furniture wholesaler. - Per Hilletofth; David Eriksson; Olli-Pekka Hilmola (Int. J. Manuf. Res., 2012)

Creating value through wholesaler and retailer interface. - David Eriksson; Per Hilletofth; Olli-Pekka Hilmola (Ind. Manag. Data Syst., 2013)

Approximate Distance Queries for Path-planning in Massive Point Clouds. - David Eriksson; Evan Shellshear (ICINCO (2), 2014)

Fast exact shortest distance queries for massive point clouds. - David Eriksson; Evan Shellshear (Graph. Model., 2016)

Scalable Log Determinants for Gaussian Process Kernel Learning. - Kun Dong; David Eriksson; Hannes Nickisch; David Bindel; Andrew Gordon Wilson (NIPS, 2017)

Critical operations capabilities for competitive manufacturing: a systematic review. - Cinzia Sansone; Per Hilletofth; David Eriksson (Ind. Manag. Data Syst., 2017)

Overview of 3GPP Release 14 Enhanced NB-IoT. - Andreas Hoglund; Xingqin Lin; Olof Liberg; Ali Behravan; Emre A. Yavuz; Martin Van Der Zee; Yutao Sui; Tuomas Tirronen; Antti Ratilainen; David Eriksson (IEEE Netw., 2017)

Selective interhemispheric circuits account for a cardinal bias in spontaneous activity within early visual areas. - Tiago Siebert Altavini; Sergio Andres Conde-Ocazionez; David Eriksson; Thomas Wunderle; Kerstin Erika Schmidt (NeuroImage, 2017)

Scaling Gaussian Process Regression with Derivatives. - David Eriksson; Kun Dong; Eric Hans Lee; David Bindel; Andrew Gordon Wilson (NeurIPS, 2018)

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

pySOT and POAP: An event-driven asynchronous framework for surrogate optimization. - David Eriksson; David Bindel; Christine A. Shoemaker (CoRR, 2019)

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)

Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020. - Ryan Turner; David Eriksson; Michael McCourt; Juha Kiili; Eero Laaksonen; Zhen Xu 007; Isabelle Guyon (NeurIPS (Competition and Demos), 2020)

Efficient Rollout Strategies for Bayesian Optimization. - Eric Hans Lee; David Eriksson; David Bindel; Bolong Cheng; Mike Mccourt (UAI, 2020)

Scalable Constrained Bayesian Optimization. - David Eriksson; Matthias Poloczek (AISTATS, 2021)

High-dimensional Bayesian optimization with sparse axis-aligned subspaces. - David Eriksson; Martin Jankowiak (UAI, 2021)

A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. - Eric Hans Lee; David Eriksson; Valerio Perrone; Matthias W. Seeger (UAI, 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 Aly; Ganesh Venkatesh; Maximilian Balandat (CoRR, 2021)

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

Multi-objective Bayesian optimization over high-dimensional search spaces. - Samuel Daulton; David Eriksson; Maximilian Balandat; Eytan Bakshy (UAI, 2022)

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)

Sparse Bayesian optimization. - Sulin Liu; Qing Feng; David Eriksson; Benjamin Letham; Eytan Bakshy (AISTATS, 2023)

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

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

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

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

Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces. - Aryan Deshwal; Sait Cakmak; Yuhou Xia; David Eriksson (CoRR, 2024)

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