Pascal Germain
24 papers · 2006–2026 · 7 venues · across top CS/AI venues
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Achievements
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🐣 Hot Topic Early Bird 🏃 Academic Marathon · Ultra 🌍 Conference Polyglot · Silver 🗺️ Taxonomy Completionist (28) 🤝 Dynamic Duo (10)
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Dynamic Duo
(10)
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Deep Specialist
(16)
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Keyword Champion
(4)
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Keyword Pioneer
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Conference Pioneer
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Keyword Collector
(92)
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Unstoppable · Bronze
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Century Club · Bronze
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Research topics
Machine Learning
(23)
Optimization & Theory
(21)
Core Methods
(6)
Deep Learning
(5)
Learning Types · Machine Learning
(5)
Artificial Intelligence
(4)
Application Areas
(4)
Bayesian & Probabilistic · Machine Learning
(3)
Learning Types · Deep Learning
(3)
Bayesian & Probabilistic · Artificial Intelligence
(2)
Models
(2)
Speech & Audio
(1)
Architectures
(1)
Core AI
(1)
Learning Paradigms
(1)
Keywords
generalization bound
(12)
pac-bayesian theory
(6)
learning theory
(5)
domain adaptation
(4)
pac-bayesian bound
(4)
risk bound
(4)
majority vote
(4)
pac-bayes bound
(4)
ensemble learning
(3)
gibbs classifier
(3)
variational inference
(2)
generative model
(2)
pac-bayesian analysis
(2)
pac-bayes theory
(2)
wasserstein distance
(2)
sample compression
(2)
binary classification
(2)
statistical learning theory
(1)
convex optimization
(1)
feature learning
(1)
Papers
Bound to Disagree: Generalization Bounds via Certifiable Surrogates
UAI 2026
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
Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm
JMLR 2023
PAC-Bayesian Generalization Bounds for Adversarial Generative Models
ICML 2023
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data
UAI 2023
Statistical Guarantees for Variational Autoencoders using PAC-Bayesian Theory
NeurIPS 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
NeurIPS 2021
Improved PAC-Bayesian Bounds for Linear Regression
AAAI 2020
PAC-Bayesian Contrastive Unsupervised Representation Learning
UAI 2020
Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks
NeurIPS 2019
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
AISTATS 2019
A New PAC-Bayesian Perspective on Domain Adaptation
ICML 2016
Domain-Adversarial Training of Neural Networks
JMLR 2016
PAC-Bayesian Bounds based on the Rényi Divergence
AISTATS 2016
PAC-Bayesian Theory Meets Bayesian Inference
NeurIPS 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
NeurIPS 2009
A PAC-Bayes Risk Bound for General Loss Functions
NeurIPS 2006
PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier
NeurIPS 2006