7 papers · 1 filter
Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting
Carlos A. Pereira, Stéphane Gaudreault, Valentin Dallerit +9
Recent machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms-advection (long-range transport), diffusion-l…
Federated Distributional Reinforcement Learning with Distributional Critic Regularization
David Millard, Cecilia Alm, Rashid Ali +2
Federated reinforcement learning typically aggregates value functions or policies by parameter averaging, which emphasizes expected return and can obscure statistical multimodality…
Can Optimal Transport Improve Federated Inverse Reinforcement Learning?
David Millard, Ali Baheri
In robotics and multi-agent systems, fleets of autonomous agents often operate in subtly different environments while pursuing a common high-level objective. Directly pooling their…
Split Conformal Prediction in the Function Space with Neural Operators
David Millard, Lars Lindemann, Ali Baheri
Uncertainty quantification for neural operators remains an open problem in the infinite-dimensional setting due to the lack of finite-sample coverage guarantees over functional out…
DEF: Diffusion-augmented Ensemble Forecasting
David Millard, Arielle Carr, Stéphane Gaudreault +1
We present DEF (\textbf{\ul{D}}iffusion-augmented \textbf{\ul{E}}nsemble \textbf{\ul{F}}orecasting), a novel approach for generating initial condition perturbations. Modern approac…
PEARL: Preconditioner Enhancement through Actor-critic Reinforcement Learning
David Millard, Arielle Carr, Stéphane Gaudreault +1
We present PEARL (Preconditioner Enhancement through Actor-critic Reinforcement Learning), a novel approach to learning matrix preconditioners. Existing preconditioners such as Jac…