Bertrand Charpentier
14 papers · 2019–2024 · 5 conferences · across top CS/AI conferences
Achievements
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(12)
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Conferences
NIPS (5)
ICML (4)
ICLR (3)
IJCAI (1)
JMLR (1)
Top co-authors
Keywords
uncertainty quantification
(3)
graph neural network
(3)
uncertainty estimation
(3)
out-of-distribution detection
(3)
hierarchical clustering
(2)
dirichlet distribution
(2)
graph clustering
(2)
gaussian process
(2)
bayesian learning
(1)
epistemic uncertainty
(1)
adversarial robustness
(1)
graph analysis
(1)
network pruning
(1)
unsupervised learning
(1)
label propagation
(1)
semi-supervised learning
(1)
message passing
(1)
posterior distribution
(1)
kullback-leibler divergence
(1)
adversarial training
(1)
Papers
Expected Probabilistic Hierarchies
NIPS 2024
Uncertainty for Active Learning on Graphs
ICML 2024
Uncertainty Estimation for Molecules: Desiderata and Methods
ICML 2023
Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions
NIPS 2023
Winning the Lottery Ahead of Time: Efficient Early Network Pruning
ICML 2022
Differentiable DAG Sampling
ICLR 2022
End-to-End Learning of Probabilistic Hierarchies on Graphs
ICLR 2022
Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions
ICLR 2022
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
ICML 2021
Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification
NIPS 2021
Scikit-network: Graph Analysis in Python
JMLR 2020
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
NIPS 2020
Tree Sampling Divergence: An Information-Theoretic Metric for Hierarchical Graph Clustering
IJCAI 2019
Uncertainty on Asynchronous Time Event Prediction
NIPS 2019