Reza Babanezhad

Publication Activity (10 Years)

Years Active: 2010-2022
Publications (10 Years): 18

Top Topics

Concave Convex Procedure

Conditional Random Fields

Web Page Prediction

Variational Inference

Top Venues

Publications

Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvári, Doina Precup
Towards Painless Policy Optimization for Constrained MDPs. CoRR (2022)
Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad
Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent. ICML (2022)
Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesváari, Doina Precup
Towards painless policy optimization for constrained MDPs. UAI (2022)
Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang
Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport. ICML (2021)
Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad
Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent. CoRR (2021)
Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien
SVRG Meets AdaGrad: Painless Variance Reduction. CoRR (2021)
Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien
An Analysis of the Adaptation Speed of Causal Models. AISTATS (2021)
Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux
To Each Optimizer a Norm, To Each Norm its Generalization. CoRR (2020)
Reza Babanezhad, Simon Lacoste-Julien
Geometry-Aware Universal Mirror-Prox. CoRR (2020)
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
Reducing the variance in online optimization by transporting past gradients. NeurIPS (2019)
Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
Reducing the variance in online optimization by transporting past gradients. CoRR (2019)
Ousmane Amadou Dia, Elnaz Barshan, Reza Babanezhad
Manifold Preserving Adversarial Learning. CoRR (2019)
Issam H. Laradji, Reza Babanezhad
M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning. CoRR (2018)
Zainab Zolaktaf, Reza Babanezhad, Rachel Pottinger
A Generic Top-N Recommendation Framework For Trading-off Accuracy, Novelty, and Coverage. CoRR (2018)
Reza Babanezhad, Issam H. Laradji, Alireza Shafaei, Mark Schmidt
MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds. ECML/PKDD (2) (2018)
Zainab Zolaktaf, Reza Babanezhad, Rachel Pottinger
A Generic Top-N Recommendation Framework for Trading-Off Accuracy, Novelty, and Coverage. ICDE (2018)
Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol
Online variance-reducing optimization. ICLR (Workshop) (2018)
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. UAI (2016)
Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. AISTATS (2015)
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
Stop Wasting My Gradients: Practical SVRG. CoRR (2015)
Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
StopWasting My Gradients: Practical SVRG. NIPS (2015)
Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. CoRR (2015)
Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. CoRR (2015)
Reza Babanezhad, Yusef Mehrdad Bibalan, Raman Ramsin
Process Patterns for Web Engineering. COMPSAC (2010)