Alejandro Ribeiro

A Manifold Perspective on the Statistical Generalization of Graph Neural Networks ICML 2025 Deterministic Policy Gradient Primal-Dual Methods for Continuous-Space Constrained MDPs AAAI 2025 Distilling On-device Language Models for Robot Planning with Minimal Human Intervention CoRL 2025 Feasible Learning AISTATS 2025 Generalization of Graph Neural Networks Is Robust to Model Mismatch AAAI 2025 GIVE: Structured Reasoning of Large Language Models with Knowledge Graph Inspired Veracity Extrapolation ICML 2025 Learning Efficient Positional Encodings with Graph Neural Networks ICLR 2025 LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning ICLR 2025 Constrained Diffusion Models via Dual Training NeurIPS 2024 Counting Graph Substructures with Graph Neural Networks ICLR 2024 Loss Shaping Constraints for Long-Term Time Series Forecasting ICML 2024 Near-Optimal Solutions of Constrained Learning Problems ICLR 2024 Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks ICML 2024 Resilient Constrained Reinforcement Learning AISTATS 2024 Active Collaborative Localization in Heterogeneous Robot Teams RSS 2023 Automatic Data Augmentation via Invariance-Constrained Learning ICML 2023 Explainable Brain Age Prediction using coVariance Neural Networks NeurIPS 2023 Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs NeurIPS 2023 Learning Globally Smooth Functions on Manifolds ICML 2023 Resilient Constrained Learning NeurIPS 2023 A Lagrangian Duality Approach to Active Learning NeurIPS 2022 An Agnostic Approach to Federated Learning with Class Imbalance ICLR 2022 coVariance Neural Networks NeurIPS 2022 Self-Consistency of the Fokker Planck Equation COLT 2022 Space-Time Graph Neural Networks ICLR 2022 Adversarial Robustness with Semi-Infinite Constrained Learning NeurIPS 2021 A Class of Parallel Doubly Stochastic Algorithms for Large-Scale Learning JMLR 2020 Counterfactual Programming for Optimal Control L4DC 2020 Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy AISTATS 2020 Graphon Neural Networks and the Transferability of Graph Neural Networks NeurIPS 2020 Probably Approximately Correct Constrained Learning NeurIPS 2020 Sinkhorn Barycenter via Functional Gradient Descent NeurIPS 2020 Sinkhorn Natural Gradient for Generative Models NeurIPS 2020 Constrained Reinforcement Learning Has Zero Duality Gap NeurIPS 2019 Diffusion Scattering Transforms on Graphs ICLR 2019 Graph Policy Gradients for Large Scale Robot Control CoRL 2019 Hessian Aided Policy Gradient ICML 2019 Learning Decentralized Controllers for Robot Swarms with Graph Neural Networks CoRL 2019 Parsimonious Online Learning with Kernels via Sparse Projections in Function Space JMLR 2019 Stability of Graph Scattering Transforms NeurIPS 2019 Large Scale Empirical Risk Minimization via Truncated Adaptive Newton Method AISTATS 2018 Approximate Supermodularity Bounds for Experimental Design NeurIPS 2017 First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization NeurIPS 2017 Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy NeurIPS 2016 DSA: Decentralized Double Stochastic Averaging Gradient Algorithm JMLR 2016 Global Convergence of Online Limited Memory BFGS JMLR 2015 Hierarchical Quasi-Clustering Methods for Asymmetric Networks ICML 2014