Noémie Jaquier
10 papers · 2018–2026 · 8 venues · across top CS/AI venues
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Machine Learning
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Bayesian & Probabilistic · Machine Learning
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Robotics
(4)
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Optimization & Theory
(4)
Deep Learning
(3)
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Keywords
gaussian process
(6)
riemannian manifold
(4)
bayesian optimization
(4)
matern kernel
(2)
robot learning
(1)
positive definite matrices
(1)
gaussian processes
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latent representation
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geometry-aware learning
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riemannian geometry
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information geometry
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robot manipulation
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high-dimensional optimization
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learning from demonstration
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probability simplex
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multi-output regression
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generative model
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latent variable model
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uncertainty quantification
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manifold learning
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Papers
Information Theoretic Bayesian Optimization over the Probability Simplex
UAI 2026
A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics
ICLR 2025
Riemann$^2$: Learning Riemannian Submanifolds from Riemannian Data
AISTATS 2025
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
JMLR 2025
Bringing Motion Taxonomies to Continuous Domains via GPLVM on Hyperbolic manifolds
ICML 2024
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
CoRL 2021
High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds
NeurIPS 2020
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
CoRL 2019
Learning from demonstration with model-based Gaussian process
CoRL 2019
Geometry-aware Tracking of Manipulability Ellipsoids
RSS 2018