Cédric Archambeau
24 papers · 1997–2024 · 9 venues · across top CS/AI venues
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Achievements
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🗺️ Taxonomy Completionist (50) 🏃 Academic Marathon · Ultra 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Bronze 🐣 Hot Topic Early Bird
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Deep Specialist
(10)
🏆
Keyword Champion
🧭
Keyword Pioneer
💎
Century Club · Bronze
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Unstoppable · Bronze
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Conference Pioneer
🗃️
Keyword Collector
(107)
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Trend Setter
Top co-authors
Research topics
Machine Learning
(20)
Artificial Intelligence
(14)
Optimization & Theory
(13)
Learning Paradigms
(8)
Deep Learning
(7)
Application Areas
(6)
Bayesian & Probabilistic · Artificial Intelligence
(6)
Bayesian & Probabilistic · Machine Learning
(6)
Learning Types
(5)
Models
(5)
Core AI
(4)
Core Methods
(3)
Mathematics & Optimization
(2)
Architectures
(2)
Optimization
(2)
Keywords
hyperparameter optimization
(7)
bayesian optimization
(6)
multi-task learning
(4)
transfer learning
(4)
gaussian process
(3)
variational inference
(3)
surrogate model
(3)
conformal prediction
(2)
acquisition function
(2)
automatic relevance determination
(2)
early stopping
(2)
sample efficiency
(1)
black-box optimization
(1)
probabilistic modeling
(1)
catastrophic forgetting
(1)
stochastic processes
(1)
approximate inference
(1)
continual learning
(1)
parameter estimation
(1)
bayesian matrix factorization
(1)
Papers
Explaining Probabilistic Models with Distributional Values
ICML 2024
Fortuna: A Library for Uncertainty Quantification in Deep Learning
JMLR 2024
Geographical Erasure in Language Generation
EMNLP 2023
Optimizing Hyperparameters with Conformal Quantile Regression
ICML 2023
PASHA: Efficient HPO and NAS with Progressive Resource Allocation
ICLR 2023
Automatic Termination for Hyperparameter Optimization
AutoML 2022
Memory Efficient Continual Learning with Transformers
NeurIPS 2022
Private Synthetic Data for Multitask Learning and Marginal Queries
NeurIPS 2022
Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research
AutoML 2022
BORE: Bayesian Optimization by Density-Ratio Estimation
ICML 2021
Hyperparameter Transfer Learning with Adaptive Complexity
AISTATS 2021
On the Lack of Robust Interpretability of Neural Text Classifiers
ACL 2021
Towards robust episodic meta-learning
UAI 2021
LEEP: A New Measure to Evaluate Transferability of Learned Representations
ICML 2020
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning
NeurIPS 2019
Scalable Hyperparameter Transfer Learning
NeurIPS 2018
Bayesian Optimization with Tree-structured Dependencies
ICML 2017
Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
ICML 2016
Structured Penalties for Log-Linear Language Models
EMNLP 2013
Robust Bayesian Matrix Factorisation
AISTATS 2011
Sparse Bayesian Multi-Task Learning
NeurIPS 2011
Sparse probabilistic projections
NeurIPS 2008
Variational Inference for Diffusion Processes
NeurIPS 2007
Gaussian Process Approximations of Stochastic Differential Equations
AISTATS 1997