David Eriksson
19 papers · 2017–2026 · 6 conferences · across top CS/AI conferences
Achievements
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🌉 Interdisciplinary Bridge 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🌍 Conference Polyglot (5) 🏃 Academic Marathon (8)
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Academic Marathon
(8)
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Hot Topic Early Bird
👥
Mega-Team
(21)
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(13)
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(56)
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(18)
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(9)
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Trend Setter
Conferences
NIPS (8)
AISTATS (4)
UAI (4)
ACL (1)
AUTOML (1)
ICML (1)
Top co-authors
Keywords
bayesian optimization
(15)
gaussian process
(8)
acquisition function
(4)
hyperparameter optimization
(3)
black-box optimization
(3)
surrogate model
(3)
global optimization
(2)
kernel learning
(2)
constrained optimization
(2)
variational inference
(2)
sample efficiency
(2)
multi-objective optimization
(2)
high-dimensional optimization
(2)
markov decision process
(1)
bayesian inference
(1)
robust regression
(1)
numerical optimization
(1)
sparse optimization
(1)
outlier robustness
(1)
expected improvement
(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
NIPS 2024
Robust Gaussian Processes via Relevance Pursuit
NIPS 2024
Discovering Many Diverse Solutions with Bayesian Optimization
AISTATS 2023
Unexpected Improvements to Expected Improvement for Bayesian Optimization
NIPS 2023
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings
AISTATS 2023
Sparse Bayesian optimization
AISTATS 2023
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization
NIPS 2022
Multi-objective Bayesian optimization over high-dimensional search spaces
UAI 2022
Scalable Constrained Bayesian Optimization
AISTATS 2021
High-dimensional Bayesian optimization with sparse axis-aligned subspaces
UAI 2021
A Nonmyopic Approach to Cost-Constrained Bayesian Optimization
UAI 2021
Efficient Rollout Strategies for Bayesian Optimization
UAI 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
NIPS 2020
Scalable Global Optimization via Local Bayesian Optimization
NIPS 2019
Scaling Gaussian Process Regression with Derivatives
NIPS 2018
Scalable Log Determinants for Gaussian Process Kernel Learning
NIPS 2017