Alejandro Ribeiro

47 papers · 2014–2025 · 10 conferences · across top CS/AI conferences

Achievements

+13 more ↓

🧭 Keyword Pioneer 🗺️ Taxonomy Completionist (19) 🌈 Renaissance Researcher (6) 🌉 Interdisciplinary Bridge 🐣 Hot Topic Early Bird

🌈 Renaissance Researcher (6) 🌍 Conference Polyglot (10) 🗺️ Taxonomy Completionist (19) 🔬 Deep Specialist (13) 🏆 Grand Slam 👑 Triple Crown 🏆 Keyword Champion 🗃️ Keyword Collector (161) ⚡ Prolific Year (7) 🚀 Conference Pioneer 💎 Century Club (47) 🔥 Unstoppable (12) 📈 Trend Setter

Conferences

NIPS (16) ICML (8) ICLR (7) AISTATS (4) JMLR (4) CORL (3) AAAI (2) COLT (1) L4DC (1) RSS (1)

Top co-authors

Aryan Mokhtari (7) Hamed Hassani (6) Zebang Shen (5) Luiz Chamon (5) Ignacio Hounie (5) Luiz F. O. Chamon (5) Juan Elenter (4) Vijay Kumar (4) Dongsheng Ding (4) Juan Cerviño (4)

Keywords

empirical risk minimization (5) graph neural network (5) policy gradient (4) stochastic optimization (3) distributed learning (3) stochastic gradient descent (3) adaptive sample size (3) reinforcement learning (3) lagrangian duality (2) sinkhorn divergence (2) constrained learning (2) online learning (2) graph convolution (2) policy optimization (2) optimal control (2) stability analysis (2) hessian approximation (2) constrained reinforcement learning (2) optimal transport (2) constrained optimization (2)

Papers

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