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Pascal Germain

23 papers · 2006–2025 · 7 conferences · across top CS/AI conferences

Achievements

🌍 Conference Polyglot (7) πŸ—ΊοΈ Taxonomy Completionist (24) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (7) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸƒ Academic Marathon (19) 🀝 Dynamic Duo (10) πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (4) πŸ“ˆ Trend Setter πŸ—ƒοΈ Keyword Collector (74) πŸš€ Conference Pioneer πŸ’Ž Century Club (23) πŸ”₯ Unstoppable (7)

Conferences

NIPS (7) AISTATS (4) ICML (4) JMLR (3) AAAI (2) UAI (2) INTERSPEECH (1)

Top co-authors

François Laviolette (10) Emilie Morvant (4) Alexandre Lacasse (4) Jean-Francis Roy (3) Benjamin Guedj (3) Mario Marchand (3) Florence Clerc (3) Amaury Habrard (3) Valentina Zantedeschi (2) Mathieu Bazinet (2)

Keywords

generalization bound (10) pac-bayesian theory (6) learning theory (5) pac-bayes bound (4) domain adaptation (4) majority vote (4) pac-bayesian bound (4) risk bound (4) ensemble learning (3) gibbs classifier (3) pac-bayesian analysis (2) wasserstein distance (2) variational inference (2) generative model (2) binary classification (2) ridge regression (1) transductive learning (1) domain generalization (1) convex optimization (1) classification (1)

Papers

Generalization Bounds via Meta-Learned Model Representations: PAC-Bayes and Sample Compression Hypernetworks ICML 2025
Sample Compression Unleashed: New Generalization Bounds for Real Valued Losses AISTATS 2025
Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice INTERSPEECH 2024
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory NIPS 2023
PAC-Bayesian Generalization Bounds for Adversarial Generative Models ICML 2023
Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm JMLR 2023
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data UAI 2023
Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models AAAI 2022
Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound NIPS 2021
Improved PAC-Bayesian Bounds for Linear Regression AAAI 2020
PAC-Bayesian Contrastive Unsupervised Representation Learning UAI 2020
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior AISTATS 2019
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks NIPS 2019
Domain-Adversarial Training of Neural Networks JMLR 2016
A New PAC-Bayesian Perspective on Domain Adaptation ICML 2016
PAC-Bayesian Theory Meets Bayesian Inference NIPS 2016
PAC-Bayesian Bounds based on the RΓ©nyi Divergence AISTATS 2016
Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm JMLR 2015
PAC-Bayesian Theory for Transductive Learning AISTATS 2014
A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers ICML 2013
From PAC-Bayes Bounds to KL Regularization NIPS 2009
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier NIPS 2006
A PAC-Bayes Risk Bound for General Loss Functions NIPS 2006