Vincent Fortuin
18 papers · 2019–2025 · 7 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+8 more ↓ Show less ↑
🗺️ Taxonomy Completionist (34) 🏃 Academic Marathon · Sprint 🌍 Conference Polyglot · Silver 🌉 Interdisciplinary Bridge · Silver 🐣 Hot Topic Early Bird
👥
Mega-Team · Squad
🔬
Deep Specialist
(14)
⚡
Prolific Year · Productive
🔥
Unstoppable · Bronze
❓
The Questioner
💎
Century Club · Bronze
📈
Trend Setter
🗃️
Keyword Collector
(80)
Top co-authors
Research topics
Machine Learning
(17)
Optimization & Theory · Machine Learning
(14)
Deep Learning
(12)
Artificial Intelligence
(9)
Bayesian & Probabilistic · Artificial Intelligence
(5)
Models
(5)
Architectures
(4)
Core AI
(4)
Core Methods
(4)
Learning Types
(4)
Application Areas
(3)
Optimization & Theory · Deep Learning
(3)
Bayesian & Probabilistic · Machine Learning
(2)
Learning Paradigms
(2)
Interdisciplinary
(1)
Keywords
bayesian neural network
(4)
marginal likelihood
(4)
bayesian inference
(4)
laplace approximation
(4)
variational autoencoder
(3)
gaussian process
(3)
uncertainty estimation
(3)
uncertainty quantification
(3)
probabilistic modeling
(2)
bayesian deep learning
(2)
data augmentation
(2)
inductive bia
(2)
deep ensemble
(2)
hyperparameter optimization
(1)
feature selection
(1)
representation learning
(1)
natural language processing
(1)
neural network pruning
(1)
time series analysis
(1)
in-context learning
(1)
Papers
Can Transformers Learn Full Bayesian Inference in Context?
ICML 2025
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning
NeurIPS 2024
Improving Neural Additive Models with Bayesian Principles
ICML 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
ICML 2024
Shaving Weights with Occam's Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood
NeurIPS 2024
Understanding Pathologies of Deep Heteroskedastic Regression
UAI 2024
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice
JMLR 2023
Bayesian Neural Network Priors Revisited
ICLR 2022
Data augmentation in Bayesian neural networks and the cold posterior effect
UAI 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
NeurIPS 2022
Probing as Quantifying Inductive Bias
ACL 2022
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
ICML 2021
Repulsive Deep Ensembles are Bayesian
NeurIPS 2021
Scalable Gaussian Process Variational Autoencoders
AISTATS 2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
ICML 2021
Conservative Uncertainty Estimation By Fitting Prior Networks
ICLR 2020
GP-VAE: Deep Probabilistic Time Series Imputation
AISTATS 2020
SOM-VAE: Interpretable Discrete Representation Learning on Time Series
ICLR 2019