15 citations · 15 across the 4 of their papers we have counts for
5 papers · 1 filter
Multi-task Bias-Variance Trade-off Through Functional Constraints
Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana +1
Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is t…
Assured RL: Reinforcement Learning with Almost Sure Constraints
Agustin Castellano, Juan Bazerque, Enrique Mallada
We consider the problem of finding optimal policies for a Markov Decision Process with almost sure constraints on state transitions and action triplets. We define value and action-…
Policy Gradient for Continuing Tasks in Non-stationary Markov Decision Processes
Santiago Paternain, Juan Andres Bazerque, Alejandro Ribeiro
Reinforcement learning considers the problem of finding policies that maximize an expected cumulative reward in a Markov decision process with unknown transition probabilities. In…
Learning to be safe, in finite time
Agustin Castellano, Juan Bazerque, Enrique Mallada
This paper aims to put forward the concept that learning to take safe actions in unknown environments, even with probability one guarantees, can be achieved without the need for an…
Nonparametric Basis Pursuit via Sparse Kernel-based Learning
Juan Andres Bazerque, Georgios B. Giannakis
Signal processing tasks as fundamental as sampling, reconstruction, minimum mean-square error interpolation and prediction can be viewed under the prism of reproducing kernel Hilbe…