Reza Babanezhad

34 papers A* 4A 4B 1Journal 21Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Towards Parameter-Free Temporal Difference Learning. CoRR Yunxiang Li, Mark Schmidt, Reza Babanezhad, Sharan Vaswani
2025	J	`jnl`	Armijo Line-search Makes (Stochastic) Gradient Descent Go Fast. CoRR Sharan Vaswani, Reza Babanezhad
2025	J	`jnl`	Revisiting Actor-Critic Methods in Discrete Action Off-Policy Reinforcement Learning. CoRR Reza Asad, Reza Babanezhad, Sharan Vaswani
2024	J	`jnl`	Fast Convergence of Softmax Policy Mirror Ascent. CoRR Reza Asad, Reza Babanezhad, Issam H. Laradji, Nicolas Le Roux, Sharan Vaswani
2024	J	`jnl`	Noise-adaptive (Accelerated) Stochastic Heavy-Ball Momentum. CoRR Anh Quang Dang, Reza Babanezhad, Sharan Vaswani
2023	J	`jnl`	Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. CoRR Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad, Nicolas Le Roux
2023	J	`jnl`	Fast Online Node Labeling for Very Large Graphs. CoRR Baojian Zhou, Yifan Sun, Reza Babanezhad
2023	J	`jnl`	Target-based Surrogates for Stochastic Optimization. CoRR Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux
2022	J	`jnl`	SVRG meets AdaGrad: painless variance reduction. Mach. Learn. Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien
2022	A*	`conf`	Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent. ICML Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad
2022	J	`jnl`	Towards Painless Policy Optimization for Constrained MDPs. CoRR Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvári, Doina Precup
2022	A	`conf`	Towards painless policy optimization for constrained MDPs. UAI Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesváari, Doina Precup
2021	A	`conf`	An Analysis of the Adaptation Speed of Causal Models. AISTATS Rémi Le Priol, Reza Babanezhad, Yoshua Bengio, Simon Lacoste-Julien
2021	A*	`conf`	Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport. ICML Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang
2021	J	`jnl`	SVRG Meets AdaGrad: Painless Variance Reduction. CoRR Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien
2021	J	`jnl`	Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent. CoRR Sharan Vaswani, Benjamin Dubois-Taine, Reza Babanezhad
2020	J	`jnl`	Geometry-Aware Universal Mirror-Prox. CoRR Reza Babanezhad, Simon Lacoste-Julien
2020	—	`ch.`	M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning. Domain Adaptation for Visual Understanding Issam H. Laradji, Reza Babanezhad
2020	J	`jnl`	To Each Optimizer a Norm, To Each Norm its Generalization. CoRR Sharan Vaswani, Reza Babanezhad, Jose Gallego, Aaron Mishkin, Simon Lacoste-Julien, Nicolas Le Roux
2019	J	`jnl`	Manifold Preserving Adversarial Learning. CoRR Ousmane Amadou Dia, Elnaz Barshan, Reza Babanezhad
2019	A*	`conf`	Reducing the variance in online optimization by transporting past gradients. NeurIPS Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
2019	J	`jnl`	Reducing the variance in online optimization by transporting past gradients. CoRR Sébastien M. R. Arnold, Pierre-Antoine Manzagol, Reza Babanezhad, Ioannis Mitliagkas, Nicolas Le Roux
2018	J	`jnl`	A Generic Top-N Recommendation Framework For Trading-off Accuracy, Novelty, and Coverage. CoRR Zainab Zolaktaf, Reza Babanezhad, Rachel Pottinger
2018	A*	`conf`	A Generic Top-N Recommendation Framework for Trading-Off Accuracy, Novelty, and Coverage. ICDE Zainab Zolaktaf, Reza Babanezhad, Rachel Pottinger
2018	J	`jnl`	M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning. CoRR Issam H. Laradji, Reza Babanezhad
2018	—	`conf`	MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds. ECML/PKDD (2) Reza Babanezhad, Issam H. Laradji, Alireza Shafaei, Mark Schmidt
2018	—	`conf`	Online variance-reducing optimization. ICLR (Workshop) Nicolas Le Roux, Reza Babanezhad, Pierre-Antoine Manzagol
2016	A	`conf`	Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. UAI Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
2015	J	`jnl`	Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. CoRR Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, Masashi Sugiyama
2015	A	`conf`	Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. AISTATS Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
2015	J	`jnl`	Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. CoRR Mark Schmidt, Reza Babanezhad, Mohamed Osama Ahmed, Aaron Defazio, Ann Clifton, Anoop Sarkar
2015	J	`jnl`	Stop Wasting My Gradients: Practical SVRG. CoRR Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
2015	—	`conf`	StopWasting My Gradients: Practical SVRG. NIPS Reza Babanezhad, Mohamed Osama Ahmed, Alim Virani, Mark Schmidt, Jakub Konecný, Scott Sallinen
2010	B	`conf`	Process Patterns for Web Engineering. COMPSAC Reza Babanezhad, Yusef Mehrdad Bibalan, Raman Ramsin