Reza Babanezhad

According to our database, Reza Babanezhad authored at least 27 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Noise-adaptive (Accelerated) Stochastic Heavy-Ball Momentum.

Anh Dang, Reza Babanezhad, Sharan Vaswani

CoRR, 2024

2023

Promoting Exploration in Memory-Augmented Adam using Critical Momenta.

Pranshu Malviya, Gonçalo Mordido, Aristide Baratin, Reza Babanezhad Harikandeh, Jerry Huang, Simon Lacoste-Julien, Razvan Pascanu, Sarath Chandar

CoRR, 2023

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees.

Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad Harikandeh, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fast Online Node Labeling for Very Large Graphs.

Baojian Zhou, Yifan Sun, Reza Babanezhad Harikandeh

Proceedings of the International Conference on Machine Learning, 2023

Target-based Surrogates for Stochastic Optimization.

Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux

Proceedings of the International Conference on Machine Learning, 2023

2022

SVRG meets AdaGrad: painless variance reduction.

Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien

Mach. Learn., 2022

Towards Painless Policy Optimization for Constrained MDPs.

Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvári, Doina Precup

CoRR, 2022

Towards painless policy optimization for constrained MDPs.

Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesváari, Doina Precup

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent.

Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad

Proceedings of the International Conference on Machine Learning, 2022

2021

Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent.

Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad

CoRR, 2021

Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport.

Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang

Proceedings of the 38th International Conference on Machine Learning, 2021

An Analysis of the Adaptation Speed of Causal Models.

Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Geometry-Aware Universal Mirror-Prox.

Reza Babanezhad, Simon Lacoste-Julien

CoRR, 2020

To Each Optimizer a Norm, To Each Norm its Generalization.

Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux

CoRR, 2020

M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning.

Issam H. Laradji, Reza Babanezhad

Proceedings of the Domain Adaptation for Visual Understanding, 2020

2019

Manifold Preserving Adversarial Learning.

Ousmane Amadou Dia, Elnaz Barshan, Reza Babanezhad

CoRR, 2019

Reducing the variance in online optimization by transporting past gradients.

Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning.

Issam H. Laradji, Reza Babanezhad

CoRR, 2018

MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds.

Reza Babanezhad, Issam H. Laradji, Alireza Shafaei, Mark Schmidt

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Online variance-reducing optimization.

Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol

Proceedings of the 6th International Conference on Learning Representations, 2018

A Generic Top-N Recommendation Framework for Trading-Off Accuracy, Novelty, and Coverage.

Zainab Zolaktaf, Reza Babanezhad, Rachel Pottinger

Proceedings of the 34th IEEE International Conference on Data Engineering, 2018

2016

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions.

Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

2015

Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence.

Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama

CoRR, 2015

Stop Wasting My Gradients: Practical SVRG.

Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen

CoRR, 2015

StopWasting My Gradients: Practical SVRG.

Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields.

Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2010

Process Patterns for Web Engineering.

Reza Babanezhad, Yusef Mehrdad Bibalan, Raman Ramsin

Proceedings of the 34th Annual IEEE International Computer Software and Applications Conference, 2010