Calibration of an inertial measurement unit. - Santiago Paternain; Matías Tailanián; Rafael M. Canetti (ICAR, 2013)

Design and implementation of sensor data fusion for an autonomous quadrotor. - Matías Tailanián; Santiago Paternain; Rodrigo Rosa; Rafael M. Canetti (I2MTC, 2014)

Online learning of optimal strategies in unknown environments. - Santiago Paternain; Alejandro Ribeiro (CDC, 2015)

Interior point method for dynamic constrained optimization in continuous time. - Mahyar Fazlyab; Santiago Paternain; Victor M. Preciado; Alejandro Ribeiro (ACC, 2016)

Safe online navigation of convex potentials in spaces with convex obstacles. - Santiago Paternain; Alejandro Ribeiro (CDC, 2017)

Decentralized efficient nonparametric stochastic optimization. - Alec Koppel; Santiago Paternain; Cédric Richard; Alejandro Ribeiro (GlobalSIP, 2017)

Online Learning of Feasible Strategies in Unknown Environments. - Santiago Paternain; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2017)

Decentralized Online Nonparametric Learning. - Alec Koppel; Santiago Paternain; Cédric Richard; Alejandro Ribeiro (ACSSC, 2018)

Locally Adaptive Kernel Estimation Using Sparse Functional Programming. - Maria Peifer; Luiz F. O. Chamon; Santiago Paternain; Alejandro Ribeiro (ACSSC, 2018)

Distributed Smooth and Strongly Convex Optimization with Inexact Dual Methods. - Mahyar Fazlyab; Santiago Paternain; Alejandro Ribeiro; Victor M. Preciado (ACC, 2018)

A Prediction-Correction Method for Model Predictive Control. - Santiago Paternain; Manfred Morari; Alejandro Ribeiro (ACC, 2018)

Learning Policies for Markov Decision Processes in Continuous Spaces. - Santiago Paternain; Juan Andrés Bazerque; Austin Small; Alejandro Ribeiro (CDC, 2018)

A Newton Method for Faster Navigation in Cluttered Environments. - Santiago Paternain; Aryan Mokhtari; Alejandro Ribeiro (CDC, 2018)

Prediction-Correction Interior-Point Method for Time-Varying Convex Optimization. - Mahyar Fazlyab; Santiago Paternain; Victor M. Preciado; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2018)

Navigation Functions for Convex Potentials in a Space With Convex Obstacles. - Santiago Paternain; Daniel E. Koditschek; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2018)

Decentralized Online Learning With Kernels. - Alec Koppel; Santiago Paternain; Cédric Richard; Alejandro Ribeiro (IEEE Trans. Signal Process., 2018)

Learning Gaussian Processes with Bayesian Posterior Optimization. - Luiz F. O. Chamon; Santiago Paternain; Alejandro Ribeiro (ACSSC, 2019)

A Prediction-Correction Primal-Dual Algorithm for Distributed Optimization. - Santiago Paternain; Mahyar Fazlyab; Victor M. Preciado; Alejandro Ribeiro (ACC, 2019)

Policy Improvement Directions for Reinforcement Learning in Reproducing Kernel Hilbert Spaces. - Santiago Paternain; Juan Andrés Bazerque; Austin Small; Alejandro Ribeiro (CDC, 2019)

Learning Safe Policies via Primal-Dual Methods. - Santiago Paternain; Miguel Calvo-Fullana; Luiz F. O. Chamon; Alejandro Ribeiro (CDC, 2019)

Constrained Online Learning in Networks with Sublinear Regret and Fit. - Santiago Paternain; Soomin Lee; Michael M. Zavlanos; Alejandro Ribeiro (CDC, 2019)

Real-Time Model Predictive Control Based on Prediction-Correction Algorithms. - Santiago Paternain; Manfred Morari; Alejandro Ribeiro (CDC, 2019)

Sparse Learning of Parsimonious Reproducing Kernel Hilbert Space Models. - Maria Peifer; Luiz F. O. Chamon; Santiago Paternain; Alejandro Ribeiro (ICASSP, 2019)

Constrained Reinforcement Learning Has Zero Duality Gap. - Santiago Paternain; Luiz F. O. Chamon; Miguel Calvo-Fullana; Alejandro Ribeiro (NeurIPS, 2019)

Learning Task Agnostic Sufficiently Accurate Models. - Clark Zhang; Arbaaz Khan; Santiago Paternain; Vijay Kumar 001; Alejandro Ribeiro (CoRR, 2019)

A Prediction-Correction Algorithm for Real-Time Model Predictive Control. - Santiago Paternain; Manfred Morari; Alejandro Ribeiro (CoRR, 2019)

A Newton-Based Method for Nonconvex Optimization with Fast Evasion of Saddle Points. - Santiago Paternain; Aryan Mokhtari; Alejandro Ribeiro (SIAM J. Optim., 2019)

Navigation of a Quadratic Potential with Star Obstacles. - Harshat Kumar; Santiago Paternain; Alejandro Ribeiro (ACC, 2020)

Resilient Control: Compromising to Adapt. - Luiz F. O. Chamon; Alexandre Amice; Santiago Paternain; Alejandro Ribeiro (CDC, 2020)

The Empirical Duality Gap of Constrained Statistical Learning. - Luiz F. O. Chamon; Santiago Paternain; Miguel Calvo-Fullana; Alejandro Ribeiro (ICASSP, 2020)

Sufficiently Accurate Model Learning. - Clark Zhang; Arbaaz Khan; Santiago Paternain; Alejandro Ribeiro (ICRA, 2020)

Counterfactual Programming for Optimal Control. - Luiz F. O. Chamon; Santiago Paternain; Alejandro Ribeiro (L4DC, 2020)

Policy Gradient for Continuing Tasks in Non-stationary Markov Decision Processes. - Santiago Paternain; Juan Andrés Bazerque; Alejandro Ribeiro (CoRR, 2020)

Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning. - Luiz F. O. Chamon; Santiago Paternain; Alejandro Ribeiro (CoRR, 2020)

Time-Varying Convex Optimization: Time-Structured Algorithms and Applications. - Andrea Simonetto; Emiliano Dall'Anese; Santiago Paternain; Geert Leus; Georgios B. Giannakis (Proc. IEEE, 2020)

Stochastic Artificial Potentials for Online Safe Navigation. - Santiago Paternain; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2020)

Distributed Constrained Online Learning. - Santiago Paternain; Soomin Lee; Michael M. Zavlanos; Alejandro Ribeiro (IEEE Trans. Signal Process., 2020)

Sparse Multiresolution Representations With Adaptive Kernels. - Maria Peifer; Luiz F. O. Chamon; Santiago Paternain; Alejandro Ribeiro (IEEE Trans. Signal Process., 2020)

Source Seeking in Unknown Environments with Convex Obstacles. - Bruno Augusto Angélico; Luiz F. O. Chamon; Santiago Paternain; Alejandro Ribeiro; George J. Pappas (ACC, 2021)

Towards Safe Continuing Task Reinforcement Learning. - Miguel Calvo-Fullana; Luiz F. O. Chamon; Santiago Paternain (ACC, 2021)

Sufficiently Accurate Model Learning for Planning. - Clark Zhang; Santiago Paternain; Alejandro Ribeiro (CoRR, 2021)

Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces. - Santiago Paternain; Juan Andrés Bazerque; Austin Small; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2021)

Navigation of a quadratic potential with ellipsoidal obstacles. - Harshat Kumar; Santiago Paternain; Alejandro Ribeiro (Autom., 2022)

Policy Gradient for Continuing Tasks in Discounted Markov Decision Processes. - Santiago Paternain; Juan Andrés Bazerque; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2022)

Learning Non-myopic Power Allocation in Constrained Scenarios. - Arindam Chowdhury; Santiago Paternain; Gunjan Verma; Ananthram Swami; Santiago Segarra (ACSSC, 2023)

Safe residual reinforcement learning for helicopter aerial refueling. - Damsara Jayarathne; Santiago Paternain; Sandipan Mishra (AIM, 2023)

Policy Gradients for Probabilistic Constrained Reinforcement Learning. - Weiqin Chen 003; Dharmashankar Subramanian; Santiago Paternain (CISS, 2023)

Safe Policies for Reinforcement Learning via Primal-Dual Methods. - Santiago Paternain; Miguel Calvo-Fullana; Luiz F. O. Chamon; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2023)

Constrained Learning With Non-Convex Losses. - Luiz F. O. Chamon; Santiago Paternain; Miguel Calvo-Fullana; Alejandro Ribeiro (IEEE Trans. Inf. Theory, 2023)

A Tensor Low-Rank Approximation for Value Functions in Multi-Task Reinforcement Learning. - Sergio Rozada; Santiago Paternain; Juan Andrés Bazerque; Antonio G. Marques (IEEECONF, 2024)

Joint Trajectory Optimization for Redundant Manipulators with Constant Path Speed. - Jonathan Fried; Santiago Paternain (ACC, 2024)

Allocation of Control Authority Between Dynamic Inversion and Reinforcement Learning for Autonomous Helicopter Aerial Refueling. - Damsara Jayarathne; Santiago Paternain; Sandipan Mishra (ACC, 2024)

Adaptive Primal-Dual Method for Safe Reinforcement Learning. - Weiqin Chen 003; James Onyejizu; Long Vu; Lan Hoang; Dharmashankar Subramanian; Koushik Kar; Sandipan Mishra; Santiago Paternain (AAMAS, 2024)

A Method for Bilevel Optimization with Convex Lower-Level Problem. - Han Shen; Santiago Paternain; Gaowen Liu; Ramana Kompella; Tianyi Chen (ICASSP, 2024)

Generalized constraint for probabilistic safe reinforcement learning. - Weiqin Chen 003; Santiago Paternain (L4DC, 2024)

Multi-agent assignment via state augmented reinforcement learning. - Leopoldo Agorio; Sean Van Alen; Miguel Calvo-Fullana; Santiago Paternain; Juan Andrés Bazerque (L4DC, 2024)

Blackout Mitigation via Physics-guided RL. - Anmol Dwivedi; Santiago Paternain; Ali Tajer (CoRR, 2024)

Fast and Accurate Relative Motion Tracking for Two Industrial Robots. - Honglu He; Chen-Lung Lu; Glenn Saunders; Pinghai Yang; Jeffrey Schoonover; John D. Wason; Santiago Paternain; Agung Julius; John T. Wen (CoRR, 2024)

Real-Time Small-Signal Security Assessment Using Graph Neural Networks. - Glory Justin; Santiago Paternain (CoRR, 2024)

A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms. - Weiqin Chen 003; Mark S. Squillante; Chai Wah Wu; Santiago Paternain (CoRR, 2024)

Domain Adaptation for Offline Reinforcement Learning with Limited Samples. - Weiqin Chen 003; Sandipan Mishra; Santiago Paternain (CoRR, 2024)

Data-driven Under Frequency Load Shedding Using Reinforcement Learning. - Glory Justin; Santiago Paternain (CoRR, 2024)

SAD: State-Action Distillation for In-Context Reinforcement Learning under Random Policies. - Weiqin Chen 003; Santiago Paternain (CoRR, 2024)

RL for Mitigating Cascading Failures: Targeted Exploration via Sensitivity Factors. - Anmol Dwivedi; Ali Tajer; Santiago Paternain; Nurali Virani (CoRR, 2024)

A Bi-Level Optimization Approach to Joint Trajectory Optimization for Redundant Manipulators. - Jonathan Fried; Santiago Paternain (CoRR, 2024)

Tensor and Matrix Low-Rank Value-Function Approximation in Reinforcement Learning. - Sergio Rozada; Santiago Paternain; Antonio G. Marques (IEEE Trans. Signal Process., 2024)

State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning With Rewards. - Miguel Calvo-Fullana; Santiago Paternain; Luiz F. O. Chamon; Alejandro Ribeiro (IEEE Trans. Autom. Control., 2024)

Probabilistic Constraint for Safety-Critical Reinforcement Learning. - Weiqin Chen 003; Dharmashankar Subramanian; Santiago Paternain (IEEE Trans. Autom. Control., 2024)

Fast and Accurate Relative Motion Tracking for Dual Industrial Robots. - Honglu He; Chen-Lung Lu; Glenn Saunders; John D. Wason; Pinghai Yang; Jeffrey Schoonover; Leo Ajdelsztajn; Santiago Paternain; Agung Julius; John T. Wen (IEEE Robotics Autom. Lett., 2024)

Autonomous helicopter aerial refueling: controller design and performance guarantees. - Damsara Jayarathne; Santiago Paternain; Sandipan Mishra (ACC, 2025)

Transfer Learning for a Class of Cascade Dynamical Systems. - Shima Rabiei; Sandipan Mishra; Santiago Paternain (ACC, 2025)

User Association in Wireless Networks with Distributed GNN-Based Reinforcement Learning. - Martín Randall; Santiago Paternain; Pedro Casas; Federico Larroca; Pablo Belzarena (NTMS, 2025)

A Bi-Level Optimization Method for Redundant Dual-Arm Minimum Time Problems. - Jonathan Fried; Santiago Paternain (IEEE Control. Syst. Lett., 2025)

Random Policy Enables In-Context Reinforcement Learning within Trust Horizons. - Weiqin Chen 003; Santiago Paternain (Trans. Mach. Learn. Res., 2025)

DISC: Dynamic Decomposition Improves LLM Inference Scaling. - Jonathan Light; Wei Cheng 002; Yue Wu; Masafumi Oyamada; Mengdi Wang; Santiago Paternain; Haifeng Chen (CoRR, 2025)

Cooperative Multi-Agent Assignment over Stochastic Graphs via Constrained Reinforcement Learning. - Leopoldo Agorio; Sean Van Alen; Santiago Paternain; Miguel Calvo-Fullana; Juan Andrés Bazerque (CoRR, 2025)

The Lagrangian Method for Solving Constrained Markov Games. - Soham Das 005; Santiago Paternain; Luiz F. O. Chamon; Ceyhun Eksin (CoRR, 2025)

Learning Closed-Loop Parametric Nash Equilibria of Multi-Agent Collaborative Field Coverage. - Jushan Chen; Santiago Paternain (CoRR, 2025)

Filtering Learning Histories Enhances In-Context Reinforcement Learning. - Weiqin Chen 003; Xinjie Zhang; Dharmashankar Subramanian; Santiago Paternain (CoRR, 2025)