Reza Babanezhad

Affiliations:
  • SAIT AI Lab, Montreal, Canada
  • University of British Columbia, Department of Computer Science, Vancouver, Canada


According to our database1, Reza Babanezhad authored at least 27 papers between 2010 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Promoting Exploration in Memory-Augmented Adam using Critical Momenta.
Trans. Mach. Learn. Res., 2024

Noise-adaptive (Accelerated) Stochastic Heavy-Ball Momentum.
CoRR, 2024

2023
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees.
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.
Proceedings of the International Conference on Machine Learning, 2023

Target-based Surrogates for Stochastic Optimization.
Proceedings of the International Conference on Machine Learning, 2023

2022
SVRG meets AdaGrad: painless variance reduction.
Mach. Learn., 2022

Towards Painless Policy Optimization for Constrained MDPs.
CoRR, 2022

Towards painless policy optimization for constrained MDPs.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent.
Proceedings of the International Conference on Machine Learning, 2022

2021
Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent.
CoRR, 2021

Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport.
Proceedings of the 38th International Conference on Machine Learning, 2021

An Analysis of the Adaptation Speed of Causal Models.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020
Geometry-Aware Universal Mirror-Prox.
CoRR, 2020

To Each Optimizer a Norm, To Each Norm its Generalization.
CoRR, 2020

M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning.
Proceedings of the Domain Adaptation for Visual Understanding, 2020

2019
Manifold Preserving Adversarial Learning.
CoRR, 2019

Reducing the variance in online optimization by transporting past gradients.
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.
CoRR, 2018

MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Online variance-reducing optimization.
Proceedings of the 6th International Conference on Learning Representations, 2018

A Generic Top-N Recommendation Framework for Trading-Off Accuracy, Novelty, and Coverage.
Proceedings of the 34th IEEE International Conference on Data Engineering, 2018

2016
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions.
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.
CoRR, 2015

Stop Wasting My Gradients: Practical SVRG.
CoRR, 2015

StopWasting My Gradients: Practical SVRG.
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.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2010
Process Patterns for Web Engineering.
Proceedings of the 34th Annual IEEE International Computer Software and Applications Conference, 2010


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