Cédric Archambeau

24 papers · 2007–2024 · 10 conferences · across top CS/AI conferences

Achievements

+9 more ↓

🐣 Hot Topic Early Bird 🧭 Keyword Pioneer 🗺️ Taxonomy Completionist (15) 🌉 Interdisciplinary Bridge 🌍 Conference Polyglot (10)

🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge 🏆 Keyword Champion 🗃️ Keyword Collector (93) 📈 Trend Setter 💎 Century Club (24) 🔥 Unstoppable (9) ⚡ Prolific Year (5)

Conferences

NIPS (7) ICML (6) AISTATS (2) AUTOML (2) EMNLP (2) ACL (1) ICLR (1) IJCNLP (1) JMLR (1) UAI (1)

Top co-authors

Matthias Seeger (7) Aaron Klein (5) Michele Donini (5) Rodolphe Jenatton (4) Valerio Perrone (4) Beyza Ermis (3) Martin Wistuba (3) Matthias W Seeger (3) Giovanni Zappella (3) Guillaume Bouchard (2)

Keywords

hyperparameter optimization (6) bayesian optimization (6) transfer learning (4) multi-task learning (3) variational inference (3) surrogate model (3) gaussian process (3) conformal prediction (2) acquisition function (2) automatic relevance determination (2) sample efficiency (1) black-box optimization (1) probabilistic modeling (1) catastrophic forgetting (1) stochastic processes (1) approximate inference (1) uncertainty quantification (1) continual learning (1) bayesian matrix factorization (1) few-shot learning (1)

Papers

Explaining Probabilistic Models with Distributional Values ICML 2024 Fortuna: A Library for Uncertainty Quantification in Deep Learning JMLR 2024 PASHA: Efficient HPO and NAS with Progressive Resource Allocation ICLR 2023 Geographical Erasure in Language Generation EMNLP 2023 Optimizing Hyperparameters with Conformal Quantile Regression ICML 2023 Memory Efficient Continual Learning with Transformers NIPS 2022 Private Synthetic Data for Multitask Learning and Marginal Queries NIPS 2022 Automatic Termination for Hyperparameter Optimization AUTOML 2022 Syne Tune: A Library for Large Scale Hyperparameter Tuning and Reproducible Research AUTOML 2022 BORE: Bayesian Optimization by Density-Ratio Estimation ICML 2021 Towards robust episodic meta-learning UAI 2021 On the Lack of Robust Interpretability of Neural Text Classifiers ACL 2021 On the Lack of Robust Interpretability of Neural Text Classifiers IJCNLP 2021 Hyperparameter Transfer Learning with Adaptive Complexity AISTATS 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 NIPS 2019 Scalable Hyperparameter Transfer Learning NIPS 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 NIPS 2011 Sparse probabilistic projections NIPS 2008 Variational Inference for Diffusion Processes NIPS 2007