| Fast and faster convergence of sgd for over-parameterized models and an accelerated perceptron S Vaswani, F Bach, M Schmidt The 22nd international conference on artificial intelligence and statistics …, 2019 | 329 | 2019 |
| Painless stochastic gradient: Interpolation, line-search, and convergence rates S Vaswani, A Mishkin, I Laradji, M Schmidt, G Gidel, S Lacoste-Julien Advances in neural information processing systems 32, 2019 | 213 | 2019 |
| Stochastic polyak step-size for sgd: An adaptive learning rate for fast convergence N Loizou, S Vaswani, IH Laradji, S Lacoste-Julien International Conference on Artificial Intelligence and Statistics, 1306-1314, 2021 | 169 | 2021 |
| Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback Z Wen, B Kveton, M Valko, S Vaswani arXiv preprint arXiv:1605.06593, 2017 | 148* | 2017 |
| Model-independent online learning for influence maximization S Vaswani, B Kveton, Z Wen, M Ghavamzadeh, LVS Lakshmanan, ... International conference on machine learning, 3530-3539, 2017 | 82* | 2017 |
| Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits B Kveton, C Szepesvari, S Vaswani, Z Wen, M Ghavamzadeh, T Lattimore Proceedings of the 36th International Conference on Machine Learning 97 …, 2019 | 78 | 2019 |
| Influence Maximization with Bandits S Vaswani, L Lakshmanan, M Schmidt arXiv preprint arXiv:1503.00024, 2015 | 74 | 2015 |
| Adaptive gradient methods converge faster with over-parameterization (but you should do a line-search) S Vaswani, I Laradji, F Kunstner, SY Meng, M Schmidt, S Lacoste-Julien arXiv preprint arXiv:2006.06835, 2020 | 39* | 2020 |
| Fast and furious convergence: Stochastic second order methods under interpolation SY Meng, S Vaswani, IH Laradji, M Schmidt, S Lacoste-Julien International Conference on Artificial Intelligence and Statistics, 2020 | 37 | 2020 |
| Old Dog Learns New Tricks: Randomized UCB for Bandit Problems S Vaswani, A Mehrabian, A Durand, B Kveton International Conference on Artificial Intelligence and Statistics, 2020 | 30 | 2020 |
| Near-optimal sample complexity bounds for constrained MDPs S Vaswani, L Yang, C Szepesvári Advances in Neural Information Processing Systems 35, 3110-3122, 2022 | 28 | 2022 |
| Combining Bayesian optimization and Lipschitz optimization MO Ahmed, S Vaswani, M Schmidt Machine Learning 109, 79-102, 2020 | 24 | 2020 |
| Adaptive influence maximization in social networks: Why commit when you can adapt? S Vaswani, LVS Lakshmanan arXiv preprint arXiv:1604.08171, 2016 | 22 | 2016 |
| New insights into bootstrapping for bandits S Vaswani, B Kveton, Z Wen, A Rao, M Schmidt, Y Abbasi-Yadkori arXiv preprint arXiv:1805.09793, 2018 | 21 | 2018 |
| A general class of surrogate functions for stable and efficient reinforcement learning S Vaswani, O Bachem, S Totaro, R Müller, S Garg, M Geist, MC Machado, ... arXiv preprint arXiv:2108.05828, 2021 | 19* | 2021 |
| SVRG meets adagrad: Painless variance reduction B Dubois-Taine, S Vaswani, R Babanezhad, M Schmidt, S Lacoste-Julien Machine Learning 111 (12), 4359-4409, 2022 | 18 | 2022 |
| Horde of bandits using gaussian markov random fields S Vaswani, M Schmidt, L Lakshmanan Artificial Intelligence and Statistics, 690-699, 2017 | 18 | 2017 |
| Modeling non-progressive phenomena for influence propagation VY Lou, S Bhagat, LVS Lakshmanan, S Vaswani Proceedings of the second ACM conference on Online social networks, 131-138, 2014 | 17 | 2014 |
| Towards noise-adaptive, problem-adaptive (accelerated) stochastic gradient descent S Vaswani, B Dubois-Taine, R Babanezhad International conference on machine learning, 22015-22059, 2022 | 14* | 2022 |
| Towards painless policy optimization for constrained mdps A Jain, S Vaswani, R Babanezhad, C Szepesvari, D Precup Uncertainty in Artificial Intelligence, 895-905, 2022 | 8 | 2022 |
Mark SchmidtProfessor of Computer Science, University of British Columbia在 cs.ubc.ca 的电子邮件经过验证
