1 citations · 2 across the 10 of their papers we have counts for
10 papers
One-Step Voice Conversion by Learning kNN Transport in WavLM Space
Anton Selitskiy, David Millard
Voice conversion (VC) systems fall into two families: non-parametric embedding-space methods, which need no trained model but degrade on short target utterances, and spectrogram-ba…
Stability of the Monge Map in Semi-Dual Optimal Transport
Anton Selitskiy, David Millard
This paper shows that the semi-dual formulation of the optimal transport problem has a degenerate saddle-point structure, and that its numerical solution is equivalent to solving a…
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…
Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting
Carlos A. Pereira, Stéphane Gaudreault, Valentin Dallerit +9
Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection, diffusive mixing, thermodynamic…
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…
Geometry-Aware Decentralized Sinkhorn for Wasserstein Barycenters
Ali Baheri, Alireza Vahid
Distributed systems require fusing heterogeneous local probability distributions into a global summary over sparse and unreliable communication networks. Traditional consensus algo…
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…
Deep Learning for Koopman Operator Estimation in Idealized Atmospheric Dynamics
David Millard, Arielle Carr, Stéphane Gaudreault
Deep learning is revolutionizing weather forecasting, with new data-driven models achieving accuracy on par with operational physical models for medium-term predictions. However, t…