1 citations · 1 across the 6 of their papers we have counts for
6 papers
Proximal DCA for Fréchet Regression on Riemannian Manifolds with Bounded Curvature
Yamin Zhou, César A. Uribe
Fréchet regression generalizes linear regression to metric-space-valued responses by defining fitted values as minimizers of weighted Fréchet functionals. Since these weights may h…
Training Dynamics and Induced Preconditioning of ReLU Policy Gradient for Scalar LQR
Jhojan A. Rodriguez-Gil, César A. Uribe
Policy gradient is not invariant to reparameterization; the training dynamics of the weights of a neural controller differ from those of the induced gains. We make that difference…
Fréchet Regression on the Bures-Wasserstein Manifold
Duc Toan Nguyen, César A. Uribe
Fréchet regression, or conditional Barycenters, is a flexible framework for modeling relationships between covariates (usually Euclidean) and response variables on general metric s…
Intrinsic Decentralized Stochastic Riemannian Optimization on Manifolds with Bounded Sectional Curvature
Duc Toan Nguyen, César A. Uribe
Decentralized optimization on Riemannian manifolds is foundational for many modern machine learning and signal processing applications in which data are non-Euclidean and generated…
A Moreau Envelope Approach for LQR Meta-Policy Estimation
Ashwin Aravind, Mohammad Taha Toghani, César A. Uribe
We study the problem of policy estimation for the Linear Quadratic Regulator (LQR) in discrete-time linear time-invariant uncertain dynamical systems. We propose a Moreau Envelope-…
Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations
Delaram Pirhayatifard, Mohammad Taha Toghani, Guha Balakrishnan +1
In this work, we address the challenge of multi-task image generation with limited data for denoising diffusion probabilistic models (DDPM), a class of generative models that produ…