Process Patterns for Web Engineering. - Reza Babanezhad 001; Yusef Mehrdad Bibalan; Raman Ramsin (COMPSAC, 2010)

Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. - Mark Schmidt 001; Reza Babanezhad 001; Mohamed Osama Ahmed; Aaron Defazio; Ann Clifton; Anoop Sarkar (AISTATS, 2015)

StopWasting My Gradients: Practical SVRG. - Reza Babanezhad 001; Mohamed Osama Ahmed; Alim Virani; Mark Schmidt 001; Jakub Konecný; Scott Sallinen (NIPS, 2015)

Stop Wasting My Gradients: Practical SVRG. - Reza Babanezhad 001; Mohamed Osama Ahmed; Alim Virani; Mark Schmidt 001; Jakub Konecný; Scott Sallinen (CoRR, 2015)

Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence. - Mohammad Emtiyaz Khan; Reza Babanezhad 001; Wu Lin; Mark Schmidt 001; Masashi Sugiyama (CoRR, 2015)

Faster Stochastic Variational Inference using Proximal-Gradient Methods with General Divergence Functions. - Mohammad Emtiyaz Khan; Reza Babanezhad 001; Wu Lin; Mark Schmidt 001; Masashi Sugiyama (UAI, 2016)

A Generic Top-N Recommendation Framework for Trading-Off Accuracy, Novelty, and Coverage. - Zainab Zolaktaf; Reza Babanezhad 001; Rachel Pottinger (ICDE, 2018)

Online variance-reducing optimization. - Nicolas Le Roux; Reza Babanezhad 001; Pierre-Antoine Manzagol (ICLR (Workshop), 2018)

MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds. - Reza Babanezhad 001; Issam H. Laradji; Alireza Shafaei; Mark Schmidt 001 (ECML/PKDD (2), 2018)

M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning. - Issam H. Laradji; Reza Babanezhad 001 (CoRR, 2018)

Reducing the variance in online optimization by transporting past gradients. - Sébastien M. R. Arnold; Pierre-Antoine Manzagol; Reza Babanezhad 001; Ioannis Mitliagkas; Nicolas Le Roux (NeurIPS, 2019)

Manifold Preserving Adversarial Learning. - Ousmane Amadou Dia; Elnaz Barshan; Reza Babanezhad 001 (CoRR, 2019)

M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning. - Issam H. Laradji; Reza Babanezhad 001 (Domain Adaptation for Visual Understanding, 2020)

To Each Optimizer a Norm, To Each Norm its Generalization. - Sharan Vaswani; Reza Babanezhad 001; Jose Gallego; Aaron Mishkin; Simon Lacoste-Julien; Nicolas Le Roux (CoRR, 2020)

Geometry-Aware Universal Mirror-Prox. - Reza Babanezhad 001; Simon Lacoste-Julien (CoRR, 2020)

An Analysis of the Adaptation Speed of Causal Models. - Rémi Le Priol; Reza Babanezhad 001; Yoshua Bengio; Simon Lacoste-Julien (AISTATS, 2021)

Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport. - Lewis Liu; Yufeng Zhang 007; Zhuoran Yang; Reza Babanezhad 001; Zhaoran Wang 001 (ICML, 2021)

Towards Noise-adaptive, Problem-adaptive Stochastic Gradient Descent. - Sharan Vaswani; Benjamin Dubois-Taine; Reza Babanezhad 001 (CoRR, 2021)

Towards Noise-adaptive, Problem-adaptive (Accelerated) Stochastic Gradient Descent. - Sharan Vaswani; Benjamin Dubois-Taine; Reza Babanezhad 001 (ICML, 2022)

Towards painless policy optimization for constrained MDPs. - Arushi Jain; Sharan Vaswani; Reza Babanezhad 001; Csaba Szepesváari; Doina Precup (UAI, 2022)

Towards Painless Policy Optimization for Constrained MDPs. - Arushi Jain; Sharan Vaswani; Reza Babanezhad 001; Csaba Szepesvári; Doina Precup (CoRR, 2022)

SVRG meets AdaGrad: painless variance reduction. - Benjamin Dubois-Taine; Sharan Vaswani; Reza Babanezhad 001; Mark Schmidt 001; Simon Lacoste-Julien (Mach. Learn., 2022)

Target-based Surrogates for Stochastic Optimization. - Jonathan Wilder Lavington; Sharan Vaswani; Reza Babanezhad Harikandeh; Mark Schmidt 001; Nicolas Le Roux (ICML, 2023)

Fast Online Node Labeling for Very Large Graphs. - Baojian Zhou; Yifan Sun; Reza Babanezhad Harikandeh (ICML, 2023)

Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. - Sharan Vaswani; Amirreza Kazemi; Reza Babanezhad Harikandeh; Nicolas Le Roux (NeurIPS, 2023)

Iterative Methods via Locally Evolving Set Process. - Baojian Zhou; Yifan Sun; Reza Babanezhad Harikandeh; Xingzhi Guo; Deqing Yang; Yanghua Xiao (NeurIPS, 2024)

Noise-adaptive (Accelerated) Stochastic Heavy-Ball Momentum. - Anh Quang Dang; Reza Babanezhad 001; Sharan Vaswani (CoRR, 2024)

Torque-Aware Momentum. - Pranshu Malviya; Gonçalo Mordido; Aristide Baratin; Reza Babanezhad Harikandeh; Gintare Karolina Dziugaite; Razvan Pascanu; Sarath Chandar (CoRR, 2024)

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 (Trans. Mach. Learn. Res., 2024)

Fast Convergence of Softmax Policy Mirror Ascent. - Reza Asad; Reza Babanezhad Harikandeh; Issam H. Laradji; Nicolas Le Roux; Sharan Vaswani (AISTATS, 2025)

(Accelerated) Noise-adaptive Stochastic Heavy-Ball Momentum. - Anh Quang Dang; Reza Babanezhad Harikandeh; Sharan Vaswani (Trans. Mach. Learn. Res., 2025)

Armijo Line-search Makes (Stochastic) Gradient Descent Go Fast. - Sharan Vaswani; Reza Babanezhad 001 (CoRR, 2025)

Revisiting Actor-Critic Methods in Discrete Action Off-Policy Reinforcement Learning. - Reza Asad; Reza Babanezhad 001; Sharan Vaswani (CoRR, 2025)