← Authors

Alejandro Ribeiro

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

Achievements

πŸƒ Academic Marathon (11) πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (10) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (6) 🌍 Conference Polyglot (10) πŸ—ΊοΈ Taxonomy Completionist (19) πŸ”¬ Deep Specialist (13) πŸ† Grand Slam πŸ‘‘ Triple Crown πŸ† Keyword Champion πŸ—ƒοΈ Keyword Collector (161) ⚑ Prolific Year (7) πŸš€ Conference Pioneer πŸ’Ž Century Club (47) πŸ”₯ Unstoppable (12) πŸ“ˆ 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 CerviΓ±o (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