Sharan Vaswani

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 42

Top Topics

Influence Maximization

Sample Complexity Bounds

Bandit Problems

Top Venues

Publications

Michael Lu, Matin Aghaei, Anant Raj, Sharan Vaswani
Towards Principled, Practical Policy Gradient for Bandits and Tabular MDPs. CoRR (2024)
Anh Dang, Reza Babanezhad, Sharan Vaswani
Noise-adaptive (Accelerated) Stochastic Heavy-Ball Momentum. CoRR (2024)
Saurabh Mishra, Anant Raj, Sharan Vaswani
From Inverse Optimization to Feasibility to ERM. CoRR (2024)
Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad Harikandeh, Nicolas Le Roux
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. NeurIPS (2023)
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux
Target-based Surrogates for Stochastic Optimization. CoRR (2023)
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux
Target-based Surrogates for Stochastic Optimization. ICML (2023)
Sharan Vaswani, Amirreza Kazemi, Reza Babanezhad, Nicolas Le Roux
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees. CoRR (2023)
Sharan Vaswani, Lin F. Yang, Csaba Szepesvári
Near-Optimal Sample Complexity Bounds for Constrained MDPs. CoRR (2022)
Arushi Jain, Sharan Vaswani, Reza Babanezhad, Csaba Szepesvári, Doina Precup
Towards Painless Policy Optimization for Constrained MDPs. CoRR (2022)
Benjamin Dubois-Taine, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Simon Lacoste-Julien
SVRG meets AdaGrad: painless variance reduction. Mach. Learn. 111 (12) (2022)
Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Müller, Shivam Garg, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux
A general class of surrogate functions for stable and efficient reinforcement learning. AISTATS (2022)
Sharan Vaswani, Lin Yang, Csaba Szepesvári
Near-Optimal Sample Complexity Bounds for Constrained MDPs. NeurIPS (2022)
Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt
Improved Policy Optimization for Online Imitation Learning. CoRR (2022)
Jonathan Wilder Lavington, Sharan Vaswani, Mark Schmidt
Improved Policy Optimization for Online Imitation Learning. CoLLAs (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)
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)
Sharan Vaswani, Olivier Bachem, Simone Totaro, Robert Mueller, Matthieu Geist, Marlos C. Machado, Pablo Samuel Castro, Nicolas Le Roux
A functional mirror ascent view of policy gradient methods with function approximation. CoRR (2021)
Nicolas Loizou, Sharan Vaswani, Issam Hadj Laradji, Simon Lacoste-Julien
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. AISTATS (2021)
Si Yi Meng, Sharan Vaswani, Issam Hadj Laradji, Mark Schmidt, Simon Lacoste-Julien
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. AISTATS (2020)
Sharan Vaswani, Abbas Mehrabian, Audrey Durand, Branislav Kveton
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems. AISTATS (2020)
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)
Nicolas Loizou, Sharan Vaswani, Issam H. Laradji, Simon Lacoste-Julien
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence. CoRR (2020)
Sharan Vaswani, Frederik Kunstner, Issam H. Laradji, Si Yi Meng, Mark Schmidt, Simon Lacoste-Julien
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search). CoRR (2020)
Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt
Combining Bayesian optimization and Lipschitz optimization. Mach. Learn. 109 (1) (2020)
Sharan Vaswani, Francis R. Bach, Mark Schmidt
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. AISTATS (2019)
Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. NeurIPS (2019)
Branislav Kveton, Csaba Szepesvári, Sharan Vaswani, Zheng Wen, Tor Lattimore, Mohammad Ghavamzadeh
Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits. ICML (2019)
Sharan Vaswani, Abbas Mehrabian, Audrey Durand, Branislav Kveton
Old Dog Learns New Tricks: Randomized UCB for Bandit Problems. CoRR (2019)
Sharan Vaswani, Aaron Mishkin, Issam H. Laradji, Mark Schmidt, Gauthier Gidel, Simon Lacoste-Julien
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates. CoRR (2019)
Si Yi Meng, Sharan Vaswani, Issam H. Laradji, Mark Schmidt, Simon Lacoste-Julien
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation. CoRR (2019)
Sharan Vaswani, Branislav Kveton, Zheng Wen, Anup Rao, Mark Schmidt, Yasin Abbasi-Yadkori
New Insights into Bootstrapping for Bandits. CoRR (2018)
Mohamed Osama Ahmed, Sharan Vaswani, Mark Schmidt
Combining Bayesian Optimization and Lipschitz Optimization. CoRR (2018)
Sharan Vaswani, Francis R. Bach, Mark Schmidt
Fast and Faster Convergence of SGD for Over-Parameterized Models and an Accelerated Perceptron. CoRR (2018)
Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt
Model-Independent Online Learning for Influence Maximization. ICML (2017)
Sharan Vaswani, Branislav Kveton, Zheng Wen, Mohammad Ghavamzadeh, Laks V. S. Lakshmanan, Mark Schmidt
Diffusion Independent Semi-Bandit Influence Maximization. CoRR (2017)
Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan
Horde of Bandits using Gaussian Markov Random Fields. CoRR (2017)
Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback. NIPS (2017)
Sharan Vaswani, Mark Schmidt, Laks V. S. Lakshmanan
Horde of Bandits using Gaussian Markov Random Fields. AISTATS (2017)
Sharan Vaswani, Laks V. S. Lakshmanan
Adaptive Influence Maximization in Social Networks: Why Commit when You can Adapt? CoRR (2016)
Sharan Vaswani, Laks V. S. Lakshmanan
Influence Maximization with Bandits. CoRR (2015)
Vincent Yun Lou, Smriti Bhagat, Laks V. S. Lakshmanan, Sharan Vaswani
Modeling Non-Progressive Phenomena for Influence Propagation. CoRR (2014)
Vincent Yun Lou, Smriti Bhagat, Laks V. S. Lakshmanan, Sharan Vaswani
Modeling non-progressive phenomena for influence propagation. COSN (2014)
Jyotsna Khemka, Mrugesh R. Gajjar, Sharan Vaswani, Naga Vydyanathan, Rama Malladi, Vinutha S. V
MIC platform. HiPC (2013)
Rahul Thota, Sharan Vaswani, Amit A. Kale, Nagavijayalakshmi Vydyanathan
Fast 3D Salient Region Detection in Medical Images using GPUs. CoRR (2013)