60 citations · 153 across the 26 of their papers we have counts for
6 papers · 1 filter
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning
Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Prediction credibility measures, in the form of confidence intervals or probability distributions, are fundamental in statistics and machine learning to characterize model robustne…
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…
Time-Varying Convex Optimization: Time-Structured Algorithms and Applications
Andrea Simonetto, Emiliano Dall'Anese, Santiago Paternain +2
Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms g…
Resilient Control: Compromising to Adapt
Luiz F. O. Chamon, Alexandre Amice, Santiago Paternain +1
In optimal control problems, disturbances are typically dealt with using robust solutions, such as H-infinity or tube model predictive control, that plan control actions feasible f…
The empirical duality gap of constrained statistical learning
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana +1
This paper is concerned with the study of constrained statistical learning problems, the unconstrained version of which are at the core of virtually all of modern information proce…
Counterfactual Programming for Optimal Control
Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
In recent years, considerable work has been done to tackle the issue of designing control laws based on observations to allow unknown dynamical systems to perform pre-specified tas…