Gilles Stoltz
Scholar

Gilles Stoltz

Google Scholar ID: w0ZsyyEAAAAJ
CNRS / Université Paris-Saclay / HEC Paris
Machine learning
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Citations & Impact
All-time
Citations
2,826
 
H-index
23
 
i10-index
38
 
Publications
20
 
Co-authors
0
 
Publications
6 items
A Direct Approach for Handling Contextual Bandits with Latent State Dynamics
2026
Cited
0
Blackwell's Approachability for Sequential Conformal Inference
2025
Cited
0
Online Matching via Reinforcement Learning: An Expert Policy Orchestration Strategy
2025
Cited
0
Conformal Prediction for Hierarchical Data
arXiv.org · 2024
Cited
0
Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization
arXiv.org · 2024
Cited
0
Symphony of experts: orchestration with adversarial insights in reinforcement learning
arXiv.org · 2023
Cited
2
Resume
Academic Achievements
  • Articles; Other publications; PhD students
Research Experience
  • Senior research scientist, CNRS - Université Paris-Saclay
Background
  • Research interests include machine learning and statistics, with a focus on: robust online learning (aggregation of experts, prediction of individual sequences), stochastic online learning (stochastic bandits, reinforcement learning), application to industrial data (currently: electricity consumption, discount rates for credits, dwell time for commuter trains, etc.), information-theoretic lower bounds of performance.
Miscellany
  • Contact:
  • Gilles (Fortin-)Stoltz
  • Office 3I3
  • Institut de mathématique (building 307)
  • Université Paris-Saclay, 91405 Orsay Cedex
  • Phone: +33 1 39 67 72 60
  • Email: prenom.nom@universite-paris-saclay.fr