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2020 – today
- 2023
[j141]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): 1321-1336 (2023)- [j140]Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro:
Constrained Learning With Non-Convex Losses. IEEE Trans. Inf. Theory 69(3): 1739-1760 (2023) - [j139]Juan Cerviño, Luana Ruiz, Alejandro Ribeiro:
Learning by Transference: Training Graph Neural Networks on Growing Graphs. IEEE Trans. Signal Process. 71: 233-247 (2023) - [j138]Landon Butler, Alejandro Parada-Mayorga, Alejandro Ribeiro:
Convolutional Learning on Multigraphs. IEEE Trans. Signal Process. 71: 933-946 (2023) - [j137]Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro:
Learning Resilient Radio Resource Management Policies With Graph Neural Networks. IEEE Trans. Signal Process. 71: 995-1009 (2023) - [i187]Elijah S. Lee, Lifeng Zhou, Alejandro Ribeiro, Vijay Kumar:
Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense. CoRR abs/2301.09689 (2023) - [i186]Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo, Alejandro Ribeiro:
Tangent Bundle Convolutional Learning: from Manifolds to Cellular Sheaves and Back. CoRR abs/2303.11323 (2023) - [i185]Saurabh Sihag, Gonzalo Mateos, Corey T. McMillan, Alejandro Ribeiro:
Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features. CoRR abs/2305.01807 (2023) - 2022
- [j136]Harshat Kumar, Santiago Paternain, Alejandro Ribeiro:
Navigation of a quadratic potential with ellipsoidal obstacles. Autom. 146: 110643 (2022) - [j135]Paul Brown, Mark Eisen, Santiago Segarra, Alejandro Ribeiro, Gabriel Egan:
How the Word Adjacency Network (WAN) works. Digit. Scholarsh. Humanit. 37(2): 321-335 (2022) - [j134]Elvin Isufi, Fernando Gama, Alejandro Ribeiro:
EdgeNets: Edge Varying Graph Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 44(11): 7457-7473 (2022) - [j133]Daniel Mox, Vijay Kumar, Alejandro Ribeiro:
Learning Connectivity-Maximizing Network Configurations. IEEE Robotics Autom. Lett. 7(2): 5552-5559 (2022) - [j132]Zhan Gao, Fernando Gama, Alejandro Ribeiro:
Spherical convolutional neural networks: Stability to perturbations in SO(3). Signal Process. 196: 108529 (2022) - [j131]Vinícius Lima, Mark Eisen, Konstantinos Gatsis, Alejandro Ribeiro:
Model-Free design of control systems over wireless fading channels. Signal Process. 197: 108540 (2022) - [j130]Luiz F. O. Chamon, Alexandre Amice, Alejandro Ribeiro:
Approximately Supermodular Scheduling Subject to Matroid Constraints. IEEE Trans. Autom. Control. 67(3): 1384-1396 (2022) - [j129]Santiago Paternain, Juan Andrés Bazerque, Alejandro Ribeiro:
Policy Gradient for Continuing Tasks in Discounted Markov Decision Processes. IEEE Trans. Autom. Control. 67(9): 4467-4482 (2022) - [j128]Zhan Gao, Mark Eisen, Alejandro Ribeiro:
Resource Allocation via Model-Free Deep Learning in Free Space Optical Communications. IEEE Trans. Commun. 70(2): 920-934 (2022) - [j127]Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler:
Scalable Perception-Action-Communication Loops With Convolutional and Graph Neural Networks. IEEE Trans. Signal Inf. Process. over Networks 8: 12-24 (2022) - [j126]Zhiyang Wang, Mark Eisen, Alejandro Ribeiro:
Learning Decentralized Wireless Resource Allocations With Graph Neural Networks. IEEE Trans. Signal Process. 70: 1850-1863 (2022) - [j125]Fernando Gama, Qingbiao Li, Ekaterina I. Tolstaya, Amanda Prorok, Alejandro Ribeiro:
Synthesizing Decentralized Controllers With Graph Neural Networks and Imitation Learning. IEEE Trans. Signal Process. 70: 1932-1946 (2022) - [j124]Zhan Gao, Alec Koppel, Alejandro Ribeiro:
Balancing Rates and Variance via Adaptive Batch-Size for Stochastic Optimization Problems. IEEE Trans. Signal Process. 70: 3693-3708 (2022) - [j123]Zhan Gao, Fernando Gama, Alejandro Ribeiro:
Wide and Deep Graph Neural Network With Distributed Online Learning. IEEE Trans. Signal Process. 70: 3862-3877 (2022) - [j122]Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro:
State-Augmented Learnable Algorithms for Resource Management in Wireless Networks. IEEE Trans. Signal Process. 70: 5898-5912 (2022) - [c278]Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro:
Convolutional Neural Networks on Manifolds: From Graphs and Back. IEEECONF 2022: 356-360 - [c277]Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani:
Self-Consistency of the Fokker Planck Equation. COLT 2022: 817-841 - [c276]Juan Cerviño, Luana Ruiz, Alejandro Ribeiro:
Training Stable Graph Neural Networks Through Constrained Learning. ICASSP 2022: 4223-4227 - [c275]Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro:
Adaptive Wireless Power Allocation with Graph Neural Networks. ICASSP 2022: 5213-5217 - [c274]Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro:
Stability of Neural Networks on Manifolds to Relative Perturbations. ICASSP 2022: 5473-5477 - [c273]Zhiyang Wang, Luana Ruiz, Mark Eisen, Alejandro Ribeiro:
Stable and Transferable Wireless Resource Allocation Policies Via Manifold Neural Networks. ICASSP 2022: 8912-8916 - [c272]Samar Hadou, Charilaos I. Kanatsoulis, Alejandro Ribeiro:
Space-Time Graph Neural Networks. ICLR 2022 - [c271]Zebang Shen, Juan Cerviño, Hamed Hassani, Alejandro Ribeiro:
An Agnostic Approach to Federated Learning with Class Imbalance. ICLR 2022 - [c270]Walker Gosrich, Siddharth Mayya, Rebecca Li, James Paulos, Mark Yim, Alejandro Ribeiro, Vijay Kumar:
Coverage Control in Multi-Robot Systems via Graph Neural Networks. ICRA 2022: 8787-8793 - [c269]Juan Elenter, Navid Naderializadeh, Alejandro Ribeiro:
A Lagrangian Duality Approach to Active Learning. NeurIPS 2022 - [c268]Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro:
coVariance Neural Networks. NeurIPS 2022 - [c267]Lifeng Zhou, Vishnu D. Sharma, Qingbiao Li, Amanda Prorok, Alejandro Ribeiro, Pratap Tokekar, Vijay Kumar:
Graph Neural Networks for Decentralized Multi-Robot Target Tracking. SSRR 2022: 195-202 - [i184]Juan Elenter, Navid Naderializadeh, Alejandro Ribeiro:
A Lagrangian Duality Approach to Active Learning. CoRR abs/2202.04108 (2022) - [i183]Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro:
Learning Resilient Radio Resource Management Policies with Graph Neural Networks. CoRR abs/2203.11012 (2022) - [i182]Max Wasserman, Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro:
Learning Graph Structure from Convolutional Mixtures. CoRR abs/2205.09575 (2022) - [i181]Charilaos I. Kanatsoulis, Alejandro Ribeiro:
Graph Neural Networks Are More Powerful Than we Think. CoRR abs/2205.09801 (2022) - [i180]Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro:
coVariance Neural Networks. CoRR abs/2205.15856 (2022) - [i179]Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani:
Self-Consistency of the Fokker-Planck Equation. CoRR abs/2206.00860 (2022) - [i178]Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro:
State-Augmented Learnable Algorithms for Resource Management in Wireless Networks. CoRR abs/2207.02242 (2022) - [i177]Alejandro Parada-Mayorga, Zhiyang Wang, Fernando Gama, Alejandro Ribeiro:
Stability of Aggregation Graph Neural Networks. CoRR abs/2207.03678 (2022) - [i176]Landon Butler, Alejandro Parada-Mayorga, Alejandro Ribeiro:
Convolutional Learning on Multigraphs. CoRR abs/2209.11354 (2022) - [i175]Ignacio Hounie, Luiz F. O. Chamon, Alejandro Ribeiro:
Automatic Data Augmentation via Invariance-Constrained Learning. CoRR abs/2209.15031 (2022) - [i174]Juan Cerviño, Navid Naderializadeh, Alejandro Ribeiro:
Federated Representation Learning via Maximal Coding Rate Reduction. CoRR abs/2210.00299 (2022) - [i173]Juan Cerviño, Luiz F. O. Chamon, Benjamin D. Haeffele, René Vidal, Alejandro Ribeiro:
Learning Globally Smooth Functions on Manifolds. CoRR abs/2210.00301 (2022) - [i172]Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro:
Convolutional Neural Networks on Manifolds: From Graphs and Back. CoRR abs/2210.00376 (2022) - [i171]Damian Owerko, Fernando Gama, Alejandro Ribeiro:
Unsupervised Optimal Power Flow Using Graph Neural Networks. CoRR abs/2210.09277 (2022) - [i170]Claudio Battiloro, Zhiyang Wang, Hans Riess, Paolo Di Lorenzo, Alejandro Ribeiro:
Tangent Bundle Filters and Neural Networks: from Manifolds to Cellular Sheaves and Back. CoRR abs/2210.15058 (2022) - [i169]Damian Owerko, Charilaos I. Kanatsoulis, Alejandro Ribeiro, Donald J. Bucci Jr., Jennifer Bondarchuk:
Deep Convolutional Neural Networks for Multi-Target Tracking: A Transfer Learning Approach. CoRR abs/2210.15539 (2022) - [i168]Juan Cerviño, Luana Ruiz, Alejandro Ribeiro:
Training Graph Neural Networks on Growing Stochastic Graphs. CoRR abs/2210.15567 (2022) - [i167]Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro:
Multi-task Bias-Variance Trade-off Through Functional Constraints. CoRR abs/2210.15573 (2022) - [i166]Ignacio Hounie, Juan Elenter, Alejandro Ribeiro:
Neural Networks with Quantization Constraints. CoRR abs/2210.15623 (2022) - [i165]Samar Hadou, Charilaos I. Kanatsoulis, Alejandro Ribeiro:
Space-Time Graph Neural Networks with Stochastic Graph Perturbations. CoRR abs/2210.16270 (2022) - [i164]Landon Butler, Alejandro Parada-Mayorga, Alejandro Ribeiro:
Learning with Multigraph Convolutional Filters. CoRR abs/2210.16272 (2022) - [i163]Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro:
Predicting Brain Age using Transferable coVariance Neural Networks. CoRR abs/2210.16363 (2022) - [i162]Yigit Berkay Uslu, Navid Naderializadeh, Mark Eisen, Alejandro Ribeiro:
A State-Augmented Approach for Learning Optimal Resource Management Decisions in Wireless Networks. CoRR abs/2210.16412 (2022) - [i161]Elijah S. Lee, Lifeng Zhou, Alejandro Ribeiro, Vijay Kumar:
Learning Decentralized Strategies for a Perimeter Defense Game with Graph Neural Networks. CoRR abs/2211.01757 (2022) - [i160]Mikhail Hayhoe, Hans Riess, Victor M. Preciado, Alejandro Ribeiro:
Stable and Transferable Hyper-Graph Neural Networks. CoRR abs/2211.06513 (2022) - [i159]Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro:
Convolutional Filtering on Sampled Manifolds. CoRR abs/2211.11058 (2022) - [i158]Alejandro Parada-Mayorga, Zhiyang Wang, Alejandro Ribeiro:
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs. CoRR abs/2212.08171 (2022) - 2021
- [j121]Miguel Calvo-Fullana, Alexander Pyattaev, Daniel Mox, Sergey Andreev, Alejandro Ribeiro:
Communications and Robotics Simulation in UAVs: A Case Study on Aerial Synthetic Aperture Antennas. IEEE Commun. Mag. 59(1): 22-27 (2021) - [j120]Luana Ruiz, Fernando Gama, Alejandro Ribeiro:
Graph Neural Networks: Architectures, Stability, and Transferability. Proc. IEEE 109(5): 660-682 (2021) - [j119]Miguel Calvo-Fullana, Daniel Mox, Alexander Pyattaev, Jonathan Fink, Vijay Kumar, Alejandro Ribeiro:
ROS-NetSim: A Framework for the Integration of Robotic and Network Simulators. IEEE Robotics Autom. Lett. 6(2): 1120-1127 (2021) - [j118]Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro:
Large Scale Distributed Collaborative Unlabeled Motion Planning With Graph Policy Gradients. IEEE Robotics Autom. Lett. 6(3): 5340-5347 (2021) - [j117]Zhan Gao, Elvin Isufi, Alejandro Ribeiro:
Stability of graph convolutional neural networks to stochastic perturbations. Signal Process. 188: 108216 (2021) - [j116]Luiz F. O. Chamon, George J. Pappas, Alejandro Ribeiro:
Approximate Supermodularity of Kalman Filter Sensor Selection. IEEE Trans. Autom. Control. 66(1): 49-63 (2021) - [j115]Alec Koppel, Garrett Warnell, Ethan Stump, Peter Stone, Alejandro Ribeiro:
Policy Evaluation in Continuous MDPs With Efficient Kernelized Gradient Temporal Difference. IEEE Trans. Autom. Control. 66(4): 1856-1863 (2021) - [j114]Santiago Paternain, Juan Andrés Bazerque, Austin Small, Alejandro Ribeiro:
Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces. IEEE Trans. Autom. Control. 66(8): 3429-3444 (2021) - [j113]Alejandro Parada-Mayorga, Alejandro Ribeiro:
Algebraic Neural Networks: Stability to Deformations. IEEE Trans. Signal Process. 69: 3351-3366 (2021) - [j112]Zhan Gao, Elvin Isufi, Alejandro Ribeiro:
Stochastic Graph Neural Networks. IEEE Trans. Signal Process. 69: 4428-4443 (2021) - [j111]Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro:
Graphon Signal Processing. IEEE Trans. Signal Process. 69: 4961-4976 (2021) - [j110]Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro:
Multi-Task Reinforcement Learning in Reproducing Kernel Hilbert Spaces via Cross-Learning. IEEE Trans. Signal Process. 69: 5947-5962 (2021) - [c266]Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro:
Transferable Graph Neural Networks on Large-Scale Stochastic Graphs. ACSCC 2021: 1563-1567 - [c265]Bruno Augusto Angélico, Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro, George J. Pappas:
Source Seeking in Unknown Environments with Convex Obstacles. ACC 2021: 5055-5061 - [c264]Juan Cerviño, Juan Andrés Bazerque, Miguel Calvo-Fullana, Alejandro Ribeiro:
Multi-task Supervised Learning via Cross-learning. EUSIPCO 2021: 1381-1385 - [c263]Zhan Gao, Fernando Gama, Alejandro Ribeiro:
Stability of Spherical Convolutional Neural Networks to Rotation Diffeomorphisms. EUSIPCO 2021: 1451-1455 - [c262]Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro:
Stability of Neural Networks on Riemannian Manifolds. EUSIPCO 2021: 1845-1849 - [c261]Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler:
VGAI: End-to-End Learning of Vision-Based Decentralized Controllers for Robot Swarms. ICASSP 2021: 4900-4904 - [c260]Alejandro Parada-Mayorga, Alejandro Ribeiro:
Stability of Algebraic Neural Networks to Small Perturbations. ICASSP 2021: 5205-5209 - [c259]Zhan Gao, Elvin Isufi, Alejandro Ribeiro:
Variance-Constrained Learning for Stochastic Graph Neural Networks. ICASSP 2021: 5245-5249 - [c258]Luana Ruiz, Zhiyang Wang, Alejandro Ribeiro:
Graphon and Graph Neural Network Stability. ICASSP 2021: 5255-5259 - [c257]Fernando Gama, Ekaterina I. Tolstaya, Alejandro Ribeiro:
Graph Neural Networks for Decentralized Controllers. ICASSP 2021: 5260-5264 - [c256]Luana Ruiz, Fernando Gama, Alejandro Ribeiro, Elvin Isufi:
Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks. ICASSP 2021: 5265-5269 - [c255]Zhan Gao, Alejandro Ribeiro, Fernando Gama:
Wide and Deep Graph Neural Networks with Distributed Online Learning. ICASSP 2021: 5270-5274 - [c254]Zhiyang Wang, Mark Eisen, Alejandro Ribeiro:
Unsupervised Learning for Asynchronous Resource Allocation In Ad-Hoc Wireless Networks. ICASSP 2021: 8143-8147 - [c253]Samuel Pfrommer, Alejandro Ribeiro, Fernando Gama:
Discriminability of Single-Layer Graph Neural Networks. ICASSP 2021: 8508-8512 - [c252]Ekaterina I. Tolstaya, Landon Butler, Daniel Mox, James Paulos, Vijay Kumar, Alejandro Ribeiro:
Learning Connectivity for Data Distribution in Robot Teams. IROS 2021: 413-420 - [c251]Ekaterina I. Tolstaya, James Paulos, Vijay Kumar, Alejandro Ribeiro:
Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks. IROS 2021: 8944-8950 - [c250]Harshat Kumar, Dionysios S. Kalogerias, George J. Pappas, Alejandro Ribeiro:
Actor-only Deterministic Policy Gradient via Zeroth-order Gradient Oracles in Action Space. ISIT 2021: 1676-1681 - [c249]Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro:
Adversarial Robustness with Semi-Infinite Constrained Learning. NeurIPS 2021: 6198-6215 - [i157]Miguel Calvo-Fullana, Daniel Mox, Alexander Pyattaev, Jonathan Fink, Vijay Kumar, Alejandro Ribeiro:
ROS-NetSim: A Framework for the Integration of Robotic and Network Simulators. CoRR abs/2101.10113 (2021) - [i156]Clark Zhang, Santiago Paternain, Alejandro Ribeiro:
Sufficiently Accurate Model Learning for Planning. CoRR abs/2102.06099 (2021) - [i155]Arbaaz Khan, Vijay Kumar, Alejandro Ribeiro:
Large Scale Distributed Collaborative Unlabeled Motion Planning with Graph Policy Gradients. CoRR abs/2102.06284 (2021) - [i154]Miguel Calvo-Fullana, Santiago Paternain, Luiz F. O. Chamon, Alejandro Ribeiro:
State Augmented Constrained Reinforcement Learning: Overcoming the Limitations of Learning with Rewards. CoRR abs/2102.11941 (2021) - [i153]Ekaterina I. Tolstaya, Landon Butler, Daniel Mox, James Paulos, Vijay Kumar, Alejandro Ribeiro:
Learning Connectivity for Data Distribution in Robot Teams. CoRR abs/2103.05091 (2021) - [i152]Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro:
Constrained Learning with Non-Convex Losses. CoRR abs/2103.05134 (2021) - [i151]Ekaterina I. Tolstaya, Ethan Stump, Alec Koppel, Alejandro Ribeiro:
Composable Learning with Sparse Kernel Representations. CoRR abs/2103.14474 (2021) - [i150]Lifeng Zhou, Vishnu D. Sharma, Qingbiao Li, Amanda Prorok, Alejandro Ribeiro, Vijay Kumar:
Graph Neural Networks for Decentralized Multi-Robot Submodular Action Selection. CoRR abs/2105.08601 (2021) - [i149]Zhan Gao, Subhrajit Bhattacharya, Leiming Zhang, Rick S. Blum, Alejandro Ribeiro, Brian M. Sadler:
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping. CoRR abs/2106.02892 (2021) - [i148]Juan Cerviño, Luana Ruiz, Alejandro Ribeiro:
Increase and Conquer: Training Graph Neural Networks on Growing Graphs. CoRR abs/2106.03693 (2021) - [i147]Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro:
Stability of Manifold Neural Networks to Deformations. CoRR abs/2106.03725 (2021) - [i146]Zhan Gao, Elvin Isufi, Alejandro Ribeiro:
Stability of Graph Convolutional Neural Networks to Stochastic Perturbations. CoRR abs/2106.10526 (2021) - [i145]Ting-Kuei Hu, Fernando Gama, Tianlong Chen, Wenqing Zheng, Zhangyang Wang, Alejandro Ribeiro, Brian M. Sadler:
Scalable Perception-Action-Communication Loops with Convolutional and Graph Neural Networks. CoRR abs/2106.13358 (2021) - [i144]Zhan Gao, Fernando Gama, Alejandro Ribeiro:
Wide and Deep Graph Neural Network with Distributed Online Learning. CoRR abs/2107.09203 (2021) - [i143]Alejandro Parada-Mayorga, Alejandro Ribeiro:
Convolutional Filtering and Neural Networks with Non Commutative Algebras. CoRR abs/2108.09923 (2021) - [i142]Walker Gosrich, Siddharth Mayya, Rebecca Li, James Paulos, Mark Yim, Alejandro Ribeiro, Vijay Kumar:
Coverage Control in Multi-Robot Systems via Graph Neural Networks. CoRR abs/2109.15278 (2021) - [i141]Samar Hadou, Charilaos I. Kanatsoulis, Alejandro Ribeiro:
Space-Time Graph Neural Networks. CoRR abs/2110.02880 (2021) - [i140]Juan Cerviño, Luana Ruiz, Alejandro Ribeiro:
Training Stable Graph Neural Networks Through Constrained Learning. CoRR abs/2110.03576 (2021) - [i139]Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro:
Stability of Neural Networks on Manifolds to Relative Perturbations. CoRR abs/2110.04702 (2021) - [i138]Alexander Robey, Luiz F. O. Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro:
Adversarial Robustness with Semi-Infinite Constrained Learning. CoRR abs/2110.15767 (2021) - [i137]Luana Ruiz, Luiz F. O. Chamon, Alejandro Ribeiro:
Transferability Properties of Graph Neural Networks. CoRR abs/2112.04629 (2021) - [i136]Anastasios Tsiamis, Dionysios S. Kalogerias, Alejandro Ribeiro, George J. Pappas:
Linear Quadratic Control with Risk Constraints. CoRR abs/2112.07564 (2021) - [i135]Daniel Mox, Vijay Kumar, Alejandro Ribeiro:
Learning Connectivity-Maximizing Network Configurations. CoRR abs/2112.07663 (2021) - 2020
- [j109]Aryan Mokhtari, Alec Koppel, Martin Takác, Alejandro Ribeiro:
A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning. J. Mach. Learn. Res. 21: 120:1-120:51 (2020) - [j108]Aryan Mokhtari, Alejandro Ribeiro:
Stochastic Quasi-Newton Methods. Proc. IEEE 108(11): 1906-1922 (2020) - [j107]Fernando Gama, Antonio G. Marques, Gonzalo Mateos, Alejandro Ribeiro:
Rethinking sketching as sampling: A graph signal processing approach. Signal Process. 169: 107404 (2020) - [j106]