no code implementations • NeurIPS 2017 • Luiz. F. O. Chamon, Alejandro Ribeiro
This work provides performance guarantees for the greedy solution of experimental design problems.
no code implementations • 10 Aug 2016 • Luiz. F. O. Chamon, Cassio G. Lopes
Parallel combinations of adaptive filters have been effectively used to improve the performance of adaptive algorithms and address well-known trade-offs, such as convergence rate vs. steady-state error.
no code implementations • 5 Apr 2017 • Luiz. F. O. Chamon, Alejandro Ribeiro
In contrast to traditional signal processing, the irregularity of the signal domain makes selecting a sampling set non-trivial and hard to analyze.
no code implementations • 21 Jul 2018 • Mark Eisen, Clark Zhang, Luiz. F. O. Chamon, Daniel D. Lee, Alejandro Ribeiro
This paper considers the design of optimal resource allocation policies in wireless communication systems which are generically modeled as a functional optimization problem with stochastic constraints.
no code implementations • 1 Nov 2018 • Luiz. F. O. Chamon, Yonina C. Eldar, Alejandro Ribeiro
Even if they are, recovering sparse solutions using convex relaxations requires assumptions that may be hard to meet in practice.
no code implementations • 7 May 2019 • Maria Peifer, Luiz. F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
To address the complexity issues, we then write the function estimation problem as a sparse functional program that explicitly minimizes the support of the representation leading to low complexity solutions.
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.
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 • 6 Dec 2019 • Dionysios S. Kalogerias, Luiz. F. O. Chamon, George J. Pappas, Alejandro Ribeiro
Despite the simplicity and intuitive interpretation of Minimum Mean Squared Error (MMSE) estimators, their effectiveness in certain scenarios is questionable.
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 • NeurIPS 2020 • Luana Ruiz, Luiz. F. O. Chamon, Alejandro Ribeiro
These graph convolutions combine information from adjacent nodes using coefficients that are shared across all nodes.
no code implementations • NeurIPS 2020 • Luiz. F. O. Chamon, Alejandro Ribeiro
To overcome this issue, we prove that under mild conditions the empirical dual problem of constrained learning is also a PAC constrained learner that now leads to a practical constrained learning algorithm based solely on solving unconstrained problems.