no code implementations • 27 Oct 2022 • Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
In this paper we draw intuition from the two extreme learning scenarios -- a single function for all tasks, and a task-specific function that ignores the other tasks dependencies -- to propose a bias-variance trade-off.
no code implementations • 8 Mar 2021 • Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro
In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic.
no code implementations • 24 Feb 2021 • Miguel Calvo-Fullana, Luiz F. O. Chamon, Santiago Paternain
However, to transfer from learning safety to learning safely, there are two hurdles that need to be overcome: (i) it has to be possible to learn the policy without having to re-initialize the system; and (ii) the rollouts of the system need to be in themselves safe.
no code implementations • 23 Feb 2021 • Miguel Calvo-Fullana, Santiago Paternain, Luiz F. O. Chamon, Alejandro Ribeiro
Thus, as we illustrate by an example, while previous methods can fail at finding optimal policies, running the dual dynamics while executing the augmented policy yields an algorithm that provably samples actions from the optimal policy.
no code implementations • 24 Oct 2020 • Juan Cervino, Juan Andres Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro
In this paper we consider a problem known as multi-task learning, consisting of fitting a set of classifier or regression functions intended for solving different tasks.
no code implementations • 12 Feb 2020 • Luiz. F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro
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 processing.
no code implementations • 20 Nov 2019 • Santiago Paternain, Miguel Calvo-Fullana, Luiz. F. O. Chamon, Alejandro Ribeiro
The advantages of the proposed relaxation are threefold.
no code implementations • NeurIPS 2019 • Santiago Paternain, Luiz. F. O. Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro
The later is generally addressed by formulating the conflicting requirements as a constrained RL problem and solved using Primal-Dual methods.