Santiago Paternain

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 73

Top Topics

Reproducing Kernel

Markov Decision Process

Reinforcement Learning

Top Venues

IEEE Trans. Autom. Control.

Publications

Miguel Calvo-Fullana, Santiago Paternain, Luiz F. O. Chamon, Alejandro Ribeiro
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning With Rewards. IEEE Trans. Autom. Control. 69 (7) (2024)
Honglu He, Chen-lung Lu, Glenn Saunders, Pinghai Yang, Jeffrey Schoonover, John D. Wason, Santiago Paternain, Agung Julius, John T. Wen
Fast and Accurate Relative Motion Tracking for Two Industrial Robots. CoRR (2024)
Arindam Chowdhury, Santiago Paternain, Gunjan Verma, Ananthram Swami, Santiago Segarra
Learning Non-myopic Power Allocation in Constrained Scenarios. CoRR (2024)
Glory Justin, Santiago Paternain
Real-Time Small-Signal Security Assessment Using Graph Neural Networks. CoRR (2024)
Weiqin Chen, Mark S. Squillante, Chai Wah Wu, Santiago Paternain
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms. CoRR (2024)
Anmol Dwivedi, Santiago Paternain, Ali Tajer
Blackout Mitigation via Physics-guided RL. CoRR (2024)
Han Shen, Santiago Paternain, Gaowen Liu, Ramana Kompella, Tianyi Chen
A Method for Bilevel Optimization with Convex Lower-Level Problem. ICASSP (2024)
Weiqin Chen, James Onyejizu, Long Vu, Lan Hoang, Dharmashankar Subramanian, Koushik Kar, Sandipan Mishra, Santiago Paternain
Adaptive Primal-Dual Method for Safe Reinforcement Learning. AAMAS (2024)
Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana, Santiago Paternain, Juan Andrés Bazerque
Multi-agent assignment via state augmented reinforcement learning. L4DC (2024)
Sergio Rozada, Santiago Paternain, Antonio G. Marques
Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning. IEEE Trans. Signal Process. 72 (2024)
Weiqin Chen, Santiago Paternain
Generalized constraint for probabilistic safe reinforcement learning. L4DC (2024)
Weiqin Chen, James Onyejizu, Long Vu, Lan Hoang, Dharmashankar Subramanian, Koushik Kar, Sandipan Mishra, Santiago Paternain
Adaptive Primal-Dual Method for Safe Reinforcement Learning. CoRR (2024)
Leopoldo Agorio, Sean Van Alen, Miguel Calvo-Fullana, Santiago Paternain, Juan Andrés Bazerque
Multi-agent assignment via state augmented reinforcement learning. CoRR (2024)
Arindam Chowdhury, Santiago Paternain, Gunjan Verma, Ananthram Swami, Santiago Segarra
Learning Non-myopic Power Allocation in Constrained Scenarios. ACSSC (2023)
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro
Constrained Learning With Non-Convex Losses. IEEE Trans. Inf. Theory 69 (3) (2023)
Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain
Probabilistic Constraint for Safety-Critical Reinforcement Learning. CoRR (2023)
Damsara Jayarathne, Santiago Paternain, Sandipan Mishra
Safe residual reinforcement learning for helicopter aerial refueling. AIM (2023)
Santiago Paternain, Miguel Calvo-Fullana, Luiz F. O. Chamon, Alejandro Ribeiro
Safe Policies for Reinforcement Learning via Primal-Dual Methods. IEEE Trans. Autom. Control. 68 (3) (2023)
Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain
Policy Gradients for Probabilistic Constrained Reinforcement Learning. CISS (2023)
Weiqin Chen, Dharmashankar Subramanian, Santiago Paternain
Policy Gradients for Probabilistic Constrained Reinforcement Learning. CoRR (2022)
Harshat Kumar, Santiago Paternain, Alejandro Ribeiro
Navigation of a quadratic potential with ellipsoidal obstacles. Autom. 146 (2022)
Santiago Paternain, Juan Andrés Bazerque, Alejandro Ribeiro
Policy Gradient for Continuing Tasks in Discounted Markov Decision Processes. IEEE Trans. Autom. Control. 67 (9) (2022)
Clark Zhang, Santiago Paternain, Alejandro Ribeiro
Sufficiently Accurate Model Learning for Planning. CoRR (2021)
Bruno Augusto Angélico, Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro, George J. Pappas
Source Seeking in Unknown Environments with Convex Obstacles. ACC (2021)
Miguel Calvo-Fullana, Luiz F. O. Chamon, Santiago Paternain
Towards Safe Continuing Task Reinforcement Learning. CoRR (2021)
Miguel Calvo-Fullana, Santiago Paternain, Luiz F. O. Chamon, Alejandro Ribeiro
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards. CoRR (2021)
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro
Constrained Learning with Non-Convex Losses. CoRR (2021)
Miguel Calvo-Fullana, Luiz F. O. Chamon, Santiago Paternain
Towards Safe Continuing Task Reinforcement Learning. ACC (2021)
Santiago Paternain, Juan Andrés Bazerque, Austin Small, Alejandro Ribeiro
Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces. IEEE Trans. Autom. Control. 66 (8) (2021)
Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Counterfactual Programming for Optimal Control. L4DC (2020)
Santiago Paternain, Alejandro Ribeiro
Stochastic Artificial Potentials for Online Safe Navigation. IEEE Trans. Autom. Control. 65 (5) (2020)
Harshat Kumar, Santiago Paternain, Alejandro Ribeiro
Navigation of a Quadratic Potential with Star Obstacles. ACC (2020)
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro
The empirical duality gap of constrained statistical learning. CoRR (2020)
Clark Zhang, Arbaaz Khan, Santiago Paternain, Alejandro Ribeiro
Sufficiently Accurate Model Learning. ICRA (2020)
Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro
The Empirical Duality Gap of Constrained Statistical Learning. ICASSP (2020)
Santiago Paternain, Soomin Lee, Michael M. Zavlanos, Alejandro Ribeiro
Distributed Constrained Online Learning. IEEE Trans. Signal Process. 68 (2020)
Luiz F. O. Chamon, Alexandre Amice, Santiago Paternain, Alejandro Ribeiro
Resilient Control: Compromising to Adapt. CDC (2020)
Andrea Simonetto, Emiliano Dall'Anese, Santiago Paternain, Geert Leus, Georgios B. Giannakis
Time-Varying Convex Optimization: Time-Structured Algorithms and Applications. Proc. IEEE 108 (11) (2020)
Santiago Paternain, Juan Andrés Bazerque, Alejandro Ribeiro
Policy Gradient for Continuing Tasks in Non-stationary Markov Decision Processes. CoRR (2020)
Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning. CoRR (2020)
Andrea Simonetto, Emiliano Dall'Anese, Santiago Paternain, Geert Leus, Georgios B. Giannakis
Time-Varying Convex Optimization: Time-Structured Algorithms and Applications. CoRR (2020)
Maria Peifer, Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Sparse Multiresolution Representations With Adaptive Kernels. IEEE Trans. Signal Process. 68 (2020)
Santiago Paternain, Miguel Calvo-Fullana, Luiz F. O. Chamon, Alejandro Ribeiro
Safe Policies for Reinforcement Learning via Primal-Dual Methods. CoRR (2019)
Santiago Paternain, Manfred Morari, Alejandro Ribeiro
A Prediction-Correction Algorithm for Real-Time Model Predictive Control. CoRR (2019)
Santiago Paternain, Luiz F. O. Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro
Constrained Reinforcement Learning Has Zero Duality Gap. CoRR (2019)
Maria Peifer, Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Sparse Learning of Parsimonious Reproducing Kernel Hilbert Space Models. ICASSP (2019)
Santiago Paternain, Luiz F. O. Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro
Constrained Reinforcement Learning Has Zero Duality Gap. NeurIPS (2019)
Clark Zhang, Arbaaz Khan, Santiago Paternain, Vijay Kumar, Alejandro Ribeiro
Learning Task Agnostic Sufficiently Accurate Models. CoRR (2019)
Bruno Augusto Angélico, Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro, George J. Pappas
Source Seeking in Unknown Environments with Convex Obstacles. CoRR (2019)
Santiago Paternain, Manfred Morari, Alejandro Ribeiro
Real-Time Model Predictive Control Based on Prediction-Correction Algorithms. CDC (2019)
Santiago Paternain, Miguel Calvo-Fullana, Luiz F. O. Chamon, Alejandro Ribeiro
Learning Safe Policies via Primal-Dual Methods. CDC (2019)
Santiago Paternain, Mahyar Fazlyab, Victor M. Preciado, Alejandro Ribeiro
A Prediction-Correction Primal-Dual Algorithm for Distributed Optimization. ACC (2019)
Santiago Paternain, Juan Andrés Bazerque, Austin Small, Alejandro Ribeiro
Policy Improvement Directions for Reinforcement Learning in Reproducing Kernel Hilbert Spaces. CDC (2019)
Harshat Kumar, Santiago Paternain, Alejandro Ribeiro
Navigation of a Quadratic Potential with Ellipsoidal Obstacles. CDC (2019)
Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Learning Gaussian Processes with Bayesian Posterior Optimization. ACSSC (2019)
Maria Peifer, Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Sparse multiresolution representations with adaptive kernels. CoRR (2019)
Santiago Paternain, Soomin Lee, Michael M. Zavlanos, Alejandro Ribeiro
Constrained Online Learning in Networks with Sublinear Regret and Fit. CDC (2019)
Santiago Paternain, Aryan Mokhtari, Alejandro Ribeiro
A Newton-Based Method for Nonconvex Optimization with Fast Evasion of Saddle Points. SIAM J. Optim. 29 (1) (2019)
Santiago Paternain, Manfred Morari, Alejandro Ribeiro
A Prediction-Correction Method for Model Predictive Control. ACC (2018)
Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro
Decentralized Online Learning With Kernels. IEEE Trans. Signal Process. 66 (12) (2018)
Mahyar Fazlyab, Santiago Paternain, Victor M. Preciado, Alejandro Ribeiro
Prediction-Correction Interior-Point Method for Time-Varying Convex Optimization. IEEE Trans. Autom. Control. 63 (7) (2018)
Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro
Decentralized Online Nonparametric Learning. ACSSC (2018)
Mahyar Fazlyab, Santiago Paternain, Alejandro Ribeiro, Victor M. Preciado
Distributed Smooth and Strongly Convex Optimization with Inexact Dual Methods. ACC (2018)
Maria Peifer, Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
Locally Adaptive Kernel Estimation Using Sparse Functional Programming. ACSSC (2018)
Santiago Paternain, Juan Andrés Bazerque, Austin Small, Alejandro Ribeiro
Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces. CoRR (2018)
Santiago Paternain, Daniel E. Koditschek, Alejandro Ribeiro
Navigation Functions for Convex Potentials in a Space With Convex Obstacles. IEEE Trans. Autom. Control. 63 (9) (2018)
Santiago Paternain, Aryan Mokhtari, Alejandro Ribeiro
A Newton Method for Faster Navigation in Cluttered Environments. CDC (2018)
Santiago Paternain, Juan Andrés Bazerque, Austin Small, Alejandro Ribeiro
Learning Policies for Markov Decision Processes in Continuous Spaces. CDC (2018)
Santiago Paternain, Alejandro Ribeiro
Safe online navigation of convex potentials in spaces with convex obstacles. CDC (2017)
Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro
Decentralized Online Learning with Kernels. CoRR (2017)
Alec Koppel, Santiago Paternain, Cédric Richard, Alejandro Ribeiro
Decentralized efficient nonparametric stochastic optimization. GlobalSIP (2017)
Santiago Paternain, Alejandro Ribeiro
Online Learning of Feasible Strategies in Unknown Environments. IEEE Trans. Autom. Control. 62 (6) (2017)
Mahyar Fazlyab, Santiago Paternain, Victor M. Preciado, Alejandro Ribeiro
Interior point method for dynamic constrained optimization in continuous time. ACC (2016)
Santiago Paternain, Alejandro Ribeiro
Online learning of optimal strategies in unknown environments. CDC (2015)
Santiago Paternain, Alejandro Ribeiro
Online learning of feasible strategies in unknown environments. ACC (2015)
Matías Tailanián, Santiago Paternain, Rodrigo Rosa, Rafael M. Canetti
Design and implementation of sensor data fusion for an autonomous quadrotor. I2MTC (2014)
Santiago Paternain, Matías Tailanián, Rafael M. Canetti
Calibration of an inertial measurement unit. ICAR (2013)