David Eriksson

59 papers A* 8A 8Journal 39Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability. CoRR Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2026	J	`jnl`	Empirical Gaussian Processes. CoRR Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao, David Eriksson, Maximilian Balandat, Eytan Bakshy
2026	J	`jnl`	MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment. CoRR Hanxian Huang, Igor Fedorov, Andrey Gromov, Bernard Beckerman, Naveen Suda, David Eriksson, Maximilian Balandat, Rylan Conway, Patrick Huber, Chinnadhurai Sankar, Ayushi Dalmia, Zechun Liu, Lemeng Wu, Tarek Elgamal, Adithya Sagar, Vikas Chandra, Raghuraman Krishnamoorthi
2025	—	`conf`	Ax: A Platform for Adaptive Experimentation. AutoML 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
2025	J	`jnl`	Informed Initialization for Bayesian Optimization and Active Learning. CoRR Carl Hvarfner, David Eriksson, Eytan Bakshy, Maximilian Balandat
2025	A*	`conf`	Scalable Gaussian Processes with Latent Kronecker Structure. ICML Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2025	J	`jnl`	Scalable Gaussian Processes with Latent Kronecker Structure. CoRR Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2024	A*	`conf`	Approximation-Aware Bayesian Optimization. NeurIPS Natalie Maus, Kyurae Kim, David Eriksson, Geoff Pleiss, John P. Cunningham, Jacob R. Gardner
2024	J	`jnl`	Approximation-Aware Bayesian Optimization. CoRR Natalie Maus, Kyurae Kim, Geoff Pleiss, David Eriksson, John P. Cunningham, Jacob R. Gardner
2024	A*	`conf`	Robust Gaussian Processes via Relevance Pursuit. NeurIPS Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy
2024	J	`jnl`	Robust Gaussian Processes via Relevance Pursuit. CoRR Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy
2024	J	`jnl`	Sample-Efficient Bayesian Optimization with Transfer Learning for Heterogeneous Search Spaces. CoRR Aryan Deshwal, Sait Cakmak, Yuhou Xia, David Eriksson
2023	A	`conf`	Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. AISTATS Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson
2023	J	`jnl`	Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. CoRR Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson
2023	A	`conf`	Discovering Many Diverse Solutions with Bayesian Optimization. AISTATS Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner
2023	A	`conf`	Sparse Bayesian optimization. AISTATS Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy
2023	A*	`conf`	Unexpected Improvements to Expected Improvement for Bayesian Optimization. NeurIPS Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2023	J	`jnl`	Unexpected Improvements to Expected Improvement for Bayesian Optimization. CoRR Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2022	A*	`conf`	Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. NeurIPS Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
2022	J	`jnl`	Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. CoRR Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
2022	J	`jnl`	Discovering Many Diverse Solutions with Bayesian Optimization. CoRR Natalie Maus, Kaiwen Wu, David Eriksson, Jacob R. Gardner
2022	A	`conf`	Multi-objective Bayesian optimization over high-dimensional search spaces. UAI Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2022	J	`jnl`	Sparse Bayesian Optimization. CoRR Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy
2021	A	`conf`	A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. UAI Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger
2021	J	`jnl`	A Nonmyopic Approach to Cost-Constrained Bayesian Optimization. CoRR Eric Hans Lee, David Eriksson, Valerio Perrone, Matthias W. Seeger
2021	J	`jnl`	Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020. CoRR Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, Isabelle Guyon
2021	J	`jnl`	High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces. CoRR David Eriksson, Martin Jankowiak
2021	A	`conf`	High-dimensional Bayesian optimization with sparse axis-aligned subspaces. UAI David Eriksson, Martin Jankowiak
2021	J	`jnl`	Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization. CoRR David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Maximilian Balandat
2021	J	`jnl`	Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces. CoRR Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2021	A	`conf`	Scalable Constrained Bayesian Optimization. AISTATS David Eriksson, Matthias Poloczek
2020	—	`conf`	Bayesian Optimization is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020. NeurIPS (Competition and Demos) Ryan Turner, David Eriksson, Michael McCourt, Juha Kiili, Eero Laaksonen, Zhen Xu, Isabelle Guyon
2020	A	`conf`	Efficient Rollout Strategies for Bayesian Optimization. UAI Eric Hans Lee, David Eriksson, David Bindel, Bolong Cheng, Mike Mccourt
2020	J	`jnl`	Efficient Rollout Strategies for Bayesian Optimization. CoRR Eric Hans Lee, David Eriksson, Bolong Cheng, Michael McCourt, David Bindel
2020	A*	`conf`	Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. NeurIPS Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
2020	J	`jnl`	Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization. CoRR Geoff Pleiss, Martin Jankowiak, David Eriksson, Anil Damle, Jacob R. Gardner
2020	J	`jnl`	Scalable Constrained Bayesian Optimization. CoRR David Eriksson, Matthias Poloczek
2019	A*	`conf`	Scalable Global Optimization via Local Bayesian Optimization. NeurIPS David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek
2019	J	`jnl`	Scalable Global Optimization via Local Bayesian Optimization. CoRR David Eriksson, Michael Pearce, Jacob R. Gardner, Ryan Turner, Matthias Poloczek
2019	J	`jnl`	pySOT and POAP: An event-driven asynchronous framework for surrogate optimization. CoRR David Eriksson, David Bindel, Christine A. Shoemaker
2018	A*	`conf`	Scaling Gaussian Process Regression with Derivatives. NeurIPS David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson
2018	J	`jnl`	Scaling Gaussian Process Regression with Derivatives. CoRR David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson
2017	J	`jnl`	Critical operations capabilities for competitive manufacturing: a systematic review. Ind. Manag. Data Syst. Cinzia Sansone, Per Hilletofth, David Eriksson
2017	J	`jnl`	Overview of 3GPP Release 14 Enhanced NB-IoT. IEEE Netw. Andreas Hoglund, Xingqin Lin, Olof Liberg, Ali Behravan, Emre A. Yavuz, Martin Van Der Zee, Yutao Sui, Tuomas Tirronen, Antti Ratilainen, David Eriksson
2017	—	`conf`	Scalable Log Determinants for Gaussian Process Kernel Learning. NIPS Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson
2017	J	`jnl`	Scalable Log Determinants for Gaussian Process Kernel Learning. CoRR Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson
2017	J	`jnl`	Selective interhemispheric circuits account for a cardinal bias in spontaneous activity within early visual areas. NeuroImage Tiago Siebert Altavini, Sergio Andres Conde-Ocazionez, David Eriksson, Thomas Wunderle, Kerstin Erika Schmidt
2016	J	`jnl`	Fast exact shortest distance queries for massive point clouds. Graph. Model. David Eriksson, Evan Shellshear
2014	—	`conf`	Approximate Distance Queries for Path-planning in Massive Point Clouds. ICINCO (2) David Eriksson, Evan Shellshear
2013	J	`jnl`	Creating value through wholesaler and retailer interface. Ind. Manag. Data Syst. David Eriksson, Per Hilletofth, Olli-Pekka Hilmola
2012	J	`jnl`	Matching supply chain strategy with business strategy and the results of a mismatch. Int. J. Manuf. Res. David Eriksson, Carl Philip T. Hedenstierna
2012	J	`jnl`	Two sides of a token: coordinating demand and supply at furniture wholesaler. Int. J. Manuf. Res. Per Hilletofth, David Eriksson, Olli-Pekka Hilmola
2011	J	`jnl`	A Linear Model of Phase-Dependent Power Correlations in Neuronal Oscillations. Frontiers Comput. Neurosci. David Eriksson, Raul Vicente, Kerstin Erika Schmidt
2010	J	`jnl`	2HDMC - two-Higgs-doublet model calculator. Comput. Phys. Commun. David Eriksson, Johan Rathsman, Oscar Stål
2010	J	`jnl`	2HDMC - two-Higgs-doublet model calculator. Comput. Phys. Commun. David Eriksson, Johan Rathsman, Oscar Stål
2010	J	`jnl`	Coordinating new product development with supply chain management. Ind. Manag. Data Syst. Per Hilletofth, David Eriksson
2010	J	`jnl`	Erratum for "2HDMC - two-Higgs-doublet model calculator" [Comput. Phys. Commun. 181 (2010) 189]. Comput. Phys. Commun. David Eriksson, Johan Rathsman, Oscar Stål
2004	J	`jnl`	Extracting and exposing predictive cortical columns for selective attention. Neurocomputing David Eriksson
2003	J	`jnl`	Effects of short-term synaptic plasticity in a local microcircuit on cell firing. Neurocomputing David Eriksson, Erik Fransén, Yuri Zilberter, Anders Lansner