ML Anthology

Ribeiro, Alejandro

55 publications

ICML 2025 A Manifold Perspective on the Statistical Generalization of Graph Neural Networks Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
NeurIPS 2025 Alignment of Large Language Models with Constrained Learning Botong Zhang, Shuo Li, Ignacio Hounie, Osbert Bastani, Dongsheng Ding, Alejandro Ribeiro
NeurIPS 2025 Composition and Alignment of Diffusion Models Using Constrained Learning Shervin Khalafi, Ignacio Hounie, Dongsheng Ding, Alejandro Ribeiro
AAAI 2025 Deterministic Policy Gradient Primal-Dual Methods for Continuous-Space Constrained MDPs Sergio Rozada, Dongsheng Ding, Antonio G. Marques, Alejandro Ribeiro
CoRL 2025 Distilling On-Device Language Models for Robot Planning with Minimal Human Intervention Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar
AISTATS 2025 Feasible Learning Juan Ramirez, Ignacio Hounie, Juan Elenter, Jose Gallego-Posada, Meraj Hashemizadeh, Alejandro Ribeiro, Simon Lacoste-Julien
ICML 2025 GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation Jiashu He, Mingyu Derek Ma, Jinxuan Fan, Dan Roth, Wei Wang, Alejandro Ribeiro
AAAI 2025 Generalization of Graph Neural Networks Is Robust to Model Mismatch Zhiyang Wang, Juan Cerviño, Alejandro Ribeiro
ICLR 2025 Learning Efficient Positional Encodings with Graph Neural Networks Charilaos Kanatsoulis, Evelyn Choi, Stefanie Jegelka, Jure Leskovec, Alejandro Ribeiro
ICLR 2025 LoRanPAC: Low-Rank Random Features and Pre-Trained Models for Bridging Theory and Practice in Continual Learning Liangzu Peng, Juan Elenter, Joshua Agterberg, Alejandro Ribeiro, Rene Vidal
LoG 2025 T-Gae: Transferable Graph Autoencoder for Network Alignment Jiashu He, Charilaos Kanatsoulis, Alejandro Ribeiro
NeurIPS 2024 Constrained Diffusion Models via Dual Training Shervin Khalafi, Dongsheng Ding, Alejandro Ribeiro
ICLR 2024 Counting Graph Substructures with Graph Neural Networks Charilaos Kanatsoulis, Alejandro Ribeiro
ICML 2024 Loss Shaping Constraints for Long-Term Time Series Forecasting Ignacio Hounie, Javier Porras-Valenzuela, Alejandro Ribeiro
ICLR 2024 Near-Optimal Solutions of Constrained Learning Problems Juan Elenter, Luiz F. O. Chamon, Alejandro Ribeiro
ICML 2024 Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro
AISTATS 2024 Resilient Constrained Reinforcement Learning Dongsheng Ding, Zhengyan Huan, Alejandro Ribeiro
ICML 2023 Automatic Data Augmentation via Invariance-Constrained Learning Ignacio Hounie, Luiz F. O. Chamon, Alejandro Ribeiro
NeurIPS 2023 Explainable Brain Age Prediction Using coVariance Neural Networks Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro
LoG 2023 Intrinsically Motivated Graph Exploration Using Network Theories of Human Curiosity Shubhankar Prashant Patankar, Mathieu Ouellet, Juan Cervino, Alejandro Ribeiro, Kieran A. Murphy, Danielle Bassett
NeurIPS 2023 Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs Dongsheng Ding, Chen-Yu Wei, Kaiqing Zhang, Alejandro Ribeiro
ICML 2023 Learning Globally Smooth Functions on Manifolds Juan Cervino, Luiz F. O. Chamon, Benjamin David Haeffele, Rene Vidal, Alejandro Ribeiro
TMLR 2023 Learning Graph Structure from Convolutional Mixtures Max Wasserman, Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro
MLJ 2023 On the Sample Complexity of Actor-Critic Method for Reinforcement Learning with Function Approximation Harshat Kumar, Alec Koppel, Alejandro Ribeiro
NeurIPS 2023 Resilient Constrained Learning Ignacio Hounie, Alejandro Ribeiro, Luiz F. O. Chamon
LoG 2023 Transferable Hypergraph Neural Networks via Spectral Similarity Mikhail Hayhoe, Hans Matthew Riess, Michael M. Zavlanos, Victor Preciado, Alejandro Ribeiro
NeurIPS 2022 A Lagrangian Duality Approach to Active Learning Juan Elenter, Navid Naderializadeh, Alejandro Ribeiro
ICLR 2022 An Agnostic Approach to Federated Learning with Class Imbalance Zebang Shen, Juan Cervino, Hamed Hassani, Alejandro Ribeiro
NeurIPSW 2022 Convolutional Neural Networks on Manifolds: From Graphs and Back Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
COLT 2022 Self-Consistency of the Fokker Planck Equation Zebang Shen, Zhenfu Wang, Satyen Kale, Alejandro Ribeiro, Amin Karbasi, Hamed Hassani
ICLR 2022 Space-Time Graph Neural Networks Samar Hadou, Charilaos I Kanatsoulis, Alejandro Ribeiro
NeurIPS 2022 coVariance Neural Networks Saurabh Sihag, Gonzalo Mateos, Corey McMillan, Alejandro Ribeiro
NeurIPS 2021 Adversarial Robustness with Semi-Infinite Constrained Learning Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani, Alejandro Ribeiro
JMLR 2020 A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning Aryan Mokhtari, Alec Koppel, Martin Takac, Alejandro Ribeiro
ICLR 2020 A Stochastic Trust Region Method for Non-Convex Minimization Zebang Shen, Pan Zhou, Cong Fang, Alejandro Ribeiro
L4DC 2020 Counterfactual Programming for Optimal Control Luiz F. O. Chamon, Santiago Paternain, Alejandro Ribeiro
AISTATS 2020 Efficient Distributed Hessian Free Algorithm for Large-Scale Empirical Risk Minimization via Accumulating Sample Strategy Majid Jahani, Xi He, Chenxin Ma, Aryan Mokhtari, Dheevatsa Mudigere, Alejandro Ribeiro, Martin Takac
NeurIPS 2020 Graphon Neural Networks and the Transferability of Graph Neural Networks Luana Ruiz, Luiz Chamon, Alejandro Ribeiro
NeurIPS 2020 Probably Approximately Correct Constrained Learning Luiz Chamon, Alejandro Ribeiro
NeurIPS 2020 Sinkhorn Barycenter via Functional Gradient Descent Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani
NeurIPS 2020 Sinkhorn Natural Gradient for Generative Models Zebang Shen, Zhenfu Wang, Alejandro Ribeiro, Hamed Hassani
NeurIPS 2019 Constrained Reinforcement Learning Has Zero Duality Gap Santiago Paternain, Luiz Chamon, Miguel Calvo-Fullana, Alejandro Ribeiro
ICLR 2019 Diffusion Scattering Transforms on Graphs Fernando Gama, Alejandro Ribeiro, Joan Bruna
CoRL 2019 Graph Policy Gradients for Large Scale Robot Control Arbaaz Khan, Ekaterina Tolstaya, Alejandro Ribeiro, Vijay Kumar
ICML 2019 Hessian Aided Policy Gradient Zebang Shen, Alejandro Ribeiro, Hamed Hassani, Hui Qian, Chao Mi
CoRL 2019 Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks Ekaterina Tolstaya, Fernando Gama, James Paulos, George Pappas, Vijay Kumar, Alejandro Ribeiro
JMLR 2019 Parsimonious Online Learning with Kernels via Sparse Projections in Function Space Alec Koppel, Garrett Warnell, Ethan Stump, Alejandro Ribeiro
NeurIPS 2019 Stability of Graph Scattering Transforms Fernando Gama, Alejandro Ribeiro, Joan Bruna
AISTATS 2018 Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method Mark Eisen, Aryan Mokhtari, Alejandro Ribeiro
NeurIPS 2017 Approximate Supermodularity Bounds for Experimental Design Luiz Chamon, Alejandro Ribeiro
NeurIPS 2017 First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization Aryan Mokhtari, Alejandro Ribeiro
NeurIPS 2016 Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy Aryan Mokhtari, Hadi Daneshmand, Aurelien Lucchi, Thomas Hofmann, Alejandro Ribeiro
JMLR 2016 DSA: Decentralized Double Stochastic Averaging Gradient Algorithm Aryan Mokhtari, Alejandro Ribeiro
JMLR 2015 Global Convergence of Online Limited Memory BFGS Aryan Mokhtari, Alejandro Ribeiro
ICML 2014 Hierarchical Quasi-Clustering Methods for Asymmetric Networks Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra