Luiz F. O. Chamon

Orcid: 0000-0001-7731-6650

Affiliations:
  • University of Stuttgart, Germany


According to our database1, Luiz F. O. Chamon authored at least 58 papers between 2011 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning With Rewards.
IEEE Trans. Autom. Control., July, 2024

Solving Differential Equations with Constrained Learning.
CoRR, 2024

Near-Optimal Solutions of Constrained Learning Problems.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Constrained Learning With Non-Convex Losses.
IEEE Trans. Inf. Theory, March, 2023

Safe Policies for Reinforcement Learning via Primal-Dual Methods.
IEEE Trans. Autom. Control., March, 2023

Transferability Properties of Graph Neural Networks.
IEEE Trans. Signal Process., 2023

Distributed Universal Adaptive Networks.
IEEE Trans. Signal Process., 2023

Resilient Constrained Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Automatic Data Augmentation via Invariance-Constrained Learning.
Proceedings of the International Conference on Machine Learning, 2023

Learning Globally Smooth Functions on Manifolds.
Proceedings of the International Conference on Machine Learning, 2023

2022
Approximately Supermodular Scheduling Subject to Matroid Constraints.
IEEE Trans. Autom. Control., 2022

Probabilistically Robust Learning: Balancing Average and Worst-case Performance.
Proceedings of the International Conference on Machine Learning, 2022

2021
Graphon Signal Processing.
IEEE Trans. Signal Process., 2021

Approximate Supermodularity of Kalman Filter Sensor Selection.
IEEE Trans. Autom. Control., 2021

Adversarial Robustness with Semi-Infinite Constrained Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Towards Safe Continuing Task Reinforcement Learning.
Proceedings of the 2021 American Control Conference, 2021

Source Seeking in Unknown Environments with Convex Obstacles.
Proceedings of the 2021 American Control Conference, 2021

Transferable Graph Neural Networks on Large-Scale Stochastic Graphs.
Proceedings of the 55th Asilomar Conference on Signals, Systems, and Computers, 2021

2020
Sparse Multiresolution Representations With Adaptive Kernels.
IEEE Trans. Signal Process., 2020

Functional Nonlinear Sparse Models.
IEEE Trans. Signal Process., 2020

Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning.
CoRR, 2020

Graphon Neural Networks and the Transferability of Graph Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Probably Approximately Correct Constrained Learning.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Counterfactual Programming for Optimal Control.
Proceedings of the 2nd Annual Conference on Learning for Dynamics and Control, 2020

The Graphon Fourier Transform.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Better Safe Than Sorry: Risk-Aware Nonlinear Bayesian Estimation.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

The Empirical Duality Gap of Constrained Statistical Learning.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Graphon Filters: Signal Processing in Very Large Graphs.
Proceedings of the 28th European Signal Processing Conference, 2020

Risk-Constrained Linear-Quadratic Regulators.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Resilient Control: Compromising to Adapt.
Proceedings of the 59th IEEE Conference on Decision and Control, 2020

2019
Learning Optimal Resource Allocations in Wireless Systems.
IEEE Trans. Signal Process., 2019

Risk-Aware MMSE Estimation.
CoRR, 2019

Constrained Reinforcement Learning Has Zero Duality Gap.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Sparse Learning of Parsimonious Reproducing Kernel Hilbert Space Models.
Proceedings of the IEEE International Conference on Acoustics, 2019

Dual Domain Learning of Optimal Resource Allocations in Wireless Systems.
Proceedings of the IEEE International Conference on Acoustics, 2019

Sparse Recovery over Nonlinear Dictionaries.
Proceedings of the IEEE International Conference on Acoustics, 2019

Learning Safe Policies via Primal-Dual Methods.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Matroid-Constrained Approximately Supermodular Optimization for Near-Optimal Actuator Scheduling.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Model Predictive Selection: A Receding Horizon Scheme for Actuator Selection.
Proceedings of the 2019 American Control Conference, 2019

Learning Gaussian Processes with Bayesian Posterior Optimization.
Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018
Greedy Sampling of Graph Signals.
IEEE Trans. Signal Process., 2018

007: Democratically Finding the Cause of Packet Drops.
Proceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation, 2018

Strong Duality of Sparse Functional Optimization.
Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

Locally Adaptive Kernel Estimation Using Sparse Functional Programming.
Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

Online Deep Learning in Wireless Communication Systems.
Proceedings of the 52nd Asilomar Conference on Signals, Systems, and Computers, 2018

2017
Closing the Network Diagnostics Gap with Vigil.
Proceedings of the Posters and Demos Proceedings of the Conference of the ACM Special Interest Group on Data Communication, 2017

Approximate Supermodularity Bounds for Experimental Design.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Universal bounds for the sampling of graph signals.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

Finite-precision effects on graph filters.
Proceedings of the 2017 IEEE Global Conference on Signal and Information Processing, 2017

The mean square error in Kalman filtering sensor selection is approximately supermodular.
Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

2016
Combinations of Adaptive Filters with Coefficients Feedback.
CoRR, 2016

Near-optimality of greedy set selection in the sampling of graph signals.
Proceedings of the 2016 IEEE Global Conference on Signal and Information Processing, 2016

2014
There's plenty of room at the bottom: Incremental combinations of sign-error LMS filters.
Proceedings of the IEEE International Conference on Acoustics, 2014

Towards spatially universal adaptive diffusion networks.
Proceedings of the 2014 IEEE Global Conference on Signal and Information Processing, 2014

2013
Transient performance of an incremental combination of LMS filters.
Proceedings of the 21st European Signal Processing Conference, 2013

2012
Combination of adaptive filters with coefficients feedback.
Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

A data reusage algorithm based on incremental combination of LMS filters.
Proceedings of the Conference Record of the Forty Sixth Asilomar Conference on Signals, 2012

2011
Combination of adaptive filters for relative navigation.
Proceedings of the 19th European Signal Processing Conference, 2011


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