4 citations · 4 across the 15 of their papers we have counts for
13 papers · 1 filter
A Contraction Framework for Stochastic Operators with Bootstrapping: Application to TD Learning
Ids van der Werf, Sergio Rozada, Antonio G. Marques
Many iterative algorithms rely on bootstrapping. A variable is updated using a second, frozen copy as a target, which is periodically replaced with the updated variable. Majorize-m…
Generative Diffusion Models of Stochastic Graph Signals
Yiğit Berkay Uslu, Samar Hadou, Sergio Rozada +2
Sampling stochastic signals supported on a graph underlies many graph machine learning tasks, including recommender systems, forecasting in financial markets, and wireless network…
Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Shervin Khalafi, Igor Krawczuk, Sergio Rozada +3
Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models. Attention-based architectures such as graph transformers have shown pr…
Learning Policy Representations for Steerable Behavior Synthesis
Beiming Li, Sergio Rozada, Alejandro Ribeiro
Given a Markov decision process (MDP), we seek to learn representations for a range of policies to facilitate behavior steering at test time. As policies of an MDP are uniquely det…
Graph-Aware Diffusion for Signal Generation
Sergio Rozada, Vimal K. B., Andrea Cavallo +3
We study the problem of generating graph signals from unknown distributions defined over given graphs, relevant to domains such as recommender systems or sensor networks. Our appro…
Graph Signal Generative Diffusion Models
Yigit Berkay Uslu, Samar Hadou, Sergio Rozada +2
We introduce U-shaped encoder-decoder graph neural networks (U-GNNs) for stochastic graph signal generation using denoising diffusion processes. The architecture learns node featur…
A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning
Sergio Rozada, Santiago Paternain, Juan Andres Bazerque +1
In pursuit of reinforcement learning systems that could train in physical environments, we investigate multi-task approaches as a means to alleviate the need for massive data acqui…
Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement Learning
Sergio Rozada, Hoi-To Wai, Antonio G. Marques
Reinforcement learning (RL) aims to estimate the action to take given a (time-varying) state, with the goal of maximizing a cumulative reward function. Predominantly, there are two…
Addressing Finite-Horizon MDPs via Low-Rank Tensor Value Approximation
Sergio Rozada, Jose Luis Orejuela, Antonio G. Marques
We study the problem of learning optimal policies in finite-horizon Markov Decision Processes (MDPs) using low-rank reinforcement learning (RL) methods. In finite-horizon MDPs, the…
Tensor Low-rank Approximation of Finite-horizon Value Functions
Sergio Rozada, Antonio G. Marques
The goal of reinforcement learning is estimating a policy that maps states to actions and maximizes the cumulative reward of a Markov Decision Process (MDP). This is oftentimes ach…
Matrix Low-Rank Approximation For Policy Gradient Methods
Sergio Rozada, Antonio G. Marques
Estimating a policy that maps states to actions is a central problem in reinforcement learning. Traditionally, policies are inferred from the so called value functions (VFs), but e…
Matrix Low-Rank Trust Region Policy Optimization
Sergio Rozada, Antonio G. Marques
Most methods in reinforcement learning use a Policy Gradient (PG) approach to learn a parametric stochastic policy that maps states to actions. The standard approach is to implemen…
Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning
Sergio Rozada, Santiago Paternain, Antonio G. Marques
Value-function (VF) approximation is a central problem in Reinforcement Learning (RL). Classical non-parametric VF estimation suffers from the curse of dimensionality. As a result,…