Pascal Germain
23 papers · 2006–2025 · 7 conferences · across top CS/AI conferences
Achievements
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π Conference Polyglot (7) π Interdisciplinary Bridge π£ Hot Topic Early Bird π§ Keyword Pioneer π Academic Marathon (19)
π£
Hot Topic Early Bird
π
Academic Marathon
(19)
π€
Dynamic Duo
(10)
π¬
Deep Specialist
(13)
π
Keyword Champion
(4)
π
Trend Setter
ποΈ
Keyword Collector
(74)
π
Conference Pioneer
π
Century Club
(23)
π₯
Unstoppable
(7)
Conferences
NIPS (7)
AISTATS (4)
ICML (4)
JMLR (3)
AAAI (2)
UAI (2)
INTERSPEECH (1)
Top co-authors
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