| Dive into deep learning A Zhang, ZC Lipton, M Li, AJ Smola Cambridge University Press, 2023 | 1748 | 2023 |
| Using deep learning to enhance cancer diagnosis and classification R Fakoor, F Ladhak, A Nazi, M Huber The 30th International Conference on Machine Learning (ICML 2013),WHEALTH …, 2013 | 675 | 2013 |
| Meta-Q-Learning R Fakoor, P Chaudhari, S Soatto, AJ Smola International Conference on Learning Representations (ICLR 2020), 2019 | 190 | 2019 |
| P3O: Policy-on Policy-off Policy Optimization R Fakoor, P Chaudhari, AJ Smola Proceedings of The 35th Uncertainty in Artificial Intelligence Conference …, 2020 | 69 | 2020 |
| Fast, accurate, and simple models for tabular data via augmented distillation R Fakoor, JW Mueller, N Erickson, P Chaudhari, AJ Smola 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 2020 | 67 | 2020 |
| Continuous doubly constrained batch reinforcement learning R Fakoor, JW Mueller, K Asadi, P Chaudhari, AJ Smola Advances in Neural Information Processing Systems 34, 11260-11273, 2021 | 36 | 2021 |
| An integrated cloud-based framework for mobile phone sensing R Fakoor, M Raj, A Nazi, M Di Francesco, SK Das Proceedings of the first edition of the MCC workshop on Mobile cloud …, 2012 | 34 | 2012 |
| Memory-augmented attention modelling for videos R Fakoor, A Mohamed, M Mitchell, SB Kang, P Kohli arXiv preprint arXiv:1611.02261, 2016 | 30 | 2016 |
| TraDE: Transformers for Density Estimation R Fakoor, P Chaudhari, J Mueller, AJ Smola arXiv preprint arXiv:2004.02441, 2020 | 29* | 2020 |
| Task-Agnostic Continual Reinforcement Learning: Gaining Insights and Overcoming Challenges M Caccia, J Mueller, T Kim, L Charlin, R Fakoor Conference on Lifelong Learning Agents (CoLLAs), 2022 | 28* | 2022 |
| Flexible Model Aggregation for Quantile Regression R Fakoor, T Kim, J Mueller, AJ Smola, RJ Tibshirani Journal of Machine Learning Research (JMLR), 2022 | 27* | 2022 |
| Resetting the optimizer in deep RL: An empirical study K Asadi, R Fakoor, S Sabach Advances in Neural Information Processing Systems 36, 2023 | 21 | 2023 |
| TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models Z Liu, J Zhang, K Asadi, Y Liu, D Zhao, S Sabach, R Fakoor International Conference on Learning Representations (ICLR 2024), 2024 | 17 | 2024 |
| Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality R Fakoor, X He, I Tashev, S Zarar NIPS 2017, Machine Learning for Audio Signal Processing workshop, 2017 | 15 | 2017 |
| Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy R Fakoor, X He, I Tashev, S Zarar. IEEE International Conference on Acoustics, Speech and Signal Processing …, 2018 | 10 | 2018 |
| Differentiable Greedy Networks T Powers, R Fakoor, S Shakeri, A Sethy, A Kainth, ... arXiv preprint arXiv:1810.12464, 2018 | 8 | 2018 |
| TD Convergence: An Optimization Perspective K Asadi, S Sabach, Y Liu, O Gottesman, R Fakoor Advances in Neural Information Processing Systems 36, 2023 | 7 | 2023 |
| Faster deep reinforcement learning with slower online network K Asadi, R Fakoor, O Gottesman, T Kim, ML Littman, A Smola 36th Conference on Neural Information Processing Systems (NeurIPS 2022)., 2022 | 7* | 2022 |
| DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning R Fakoor, P Chaudhari, A Smola | 6 | 2020 |
| Budgeting Counterfactual for Offline RL Y Liu, P Chaudhari, R Fakoor Advances in Neural Information Processing Systems 36, 2023 | 5 | 2023 |
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