David Eriksson
19 papers · 2017–2026 · 6 venues · across top CS/AI venues
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Achievements
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Research topics
Machine Learning
(18)
Optimization & Theory
(17)
Mathematics & Optimization
(13)
Optimization
(11)
Artificial Intelligence
(9)
Bayesian & Probabilistic Β· Artificial Intelligence
(8)
Bayesian & Probabilistic Β· Machine Learning
(6)
Application Areas
(2)
Core Methods
(2)
Mathematics
(2)
Data Science & Analytics
(1)
Deep Learning
(1)
Reinforcement Learning
(1)
Applications
(1)
Core AI
(1)
Keywords
bayesian optimization
(15)
gaussian process
(8)
acquisition function
(4)
surrogate model
(4)
black-box optimization
(4)
sample efficiency
(3)
hyperparameter optimization
(3)
kernel learning
(2)
constrained optimization
(2)
high-dimensional optimization
(2)
global optimization
(2)
variational inference
(2)
multi-objective optimization
(2)
markov decision process
(1)
gaussian processes
(1)
bayesian inference
(1)
robust regression
(1)
numerical optimization
(1)
sparse optimization
(1)
embedding learning
(1)
Papers
MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment
ACL 2026
Ax: A Platform for Adaptive Experimentation
AutoML 2025
Scalable Gaussian Processes with Latent Kronecker Structure
ICML 2025
Approximation-Aware Bayesian Optimization
NeurIPS 2024
Robust Gaussian Processes via Relevance Pursuit
NeurIPS 2024
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
AISTATS 2023
Discovering Many Diverse Solutions with Bayesian Optimization
AISTATS 2023
Sparse Bayesian optimization
AISTATS 2023
Unexpected Improvements to Expected Improvement for Bayesian Optimization
NeurIPS 2023
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
NeurIPS 2022
Multi-objective Bayesian optimization over high-dimensional search spaces
UAI 2022
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
UAI 2021
High-dimensional Bayesian optimization with sparse axis-aligned subspaces
UAI 2021
Scalable Constrained Bayesian Optimization
AISTATS 2021
Efficient Rollout Strategies for Bayesian Optimization
UAI 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
NeurIPS 2020
Scalable Global Optimization via Local Bayesian Optimization
NeurIPS 2019
Scaling Gaussian Process Regression with Derivatives
NeurIPS 2018
Scalable Log Determinants for Gaussian Process Kernel Learning
NeurIPS 2017