I'm a fifth year Ph.D. student at Penn working with at Alejandro Ribeiro.
I study the theoretical underpinnings of constrained learning and its applications to
signal processing, control, and machine learning.
SELECTED PUBLICATIONS
- S. Paternain, L.F.O. Chamon, M. Calvo-Fullana, and A. Ribeiro, "Constrained reinforcement learning has zero duality gap."
NeurIPS 2019. - L.F.O. Chamon, Y.C. Eldar, and A. Ribeiro, "Functional nonlinear sparse models." Submitted to IEEE TSP.
- M. Peifer, L.F.O. Chamon, S. Paternain, and A. Ribeiro, "Sparse multiresolution representations with adaptive kernels."
Submitted to IEEE TSP. - L.F.O. Chamon, A. Ribeiro, and G.J. Pappas, "Approximate supermodularity of Kalman filter sensor selection."
IEEE TAC (accepted). - L.F.O. Chamon and A. Ribeiro, "Greedy sampling of graph signals." IEEE TSP 2018
NEWS
- February 7th, 2020 — "Approximate supermodularity of Kalman filter sensor selection" was accepted for publication in IEEE TAC (arXiv).
- February 7th, 2020 — Patent "Sparse cascaded-integrator-comb filters" with Analog Devices is out.
- January 29th, 2020 — New preprint "Counterfactual Programming for Optimal Control" available on arXiv.