| Epistemic neural networks I Osband, Z Wen, SM Asghari, V Dwaracherla, M Ibrahimi, X Lu, ... Advances in Neural Information Processing Systems 36, 2024 | 86 | 2024 |
| Reinforcement learning, bit by bit X Lu, B Van Roy, V Dwaracherla, M Ibrahimi, I Osband, Z Wen Foundations and Trends® in Machine Learning 16 (6), 733-865, 2023 | 68 | 2023 |
| Motion-based object segmentation based on dense rgb-d scene flow L Shao, P Shah, V Dwaracherla, J Bohg IEEE Robotics and Automation Letters 3 (4), 3797-3804, 2018 | 48 | 2018 |
| Hypermodels for exploration V Dwaracherla, X Lu, M Ibrahimi, I Osband, Z Wen, B Van Roy arXiv preprint arXiv:2006.07464, 2020 | 44 | 2020 |
| Motion planning for point-to-point navigation of spherical robot using position feedback V Dwaracherla, S Thakar, L Vachhani, A Gupta, A Yadav, S Modi IEEE/ASME Transactions on Mechatronics 24 (5), 2416-2426, 2019 | 20 | 2019 |
| The neural testbed: Evaluating joint predictions I Osband, Z Wen, SM Asghari, V Dwaracherla, X Lu, M Ibrahimi, ... Advances in Neural Information Processing Systems 35, 12554-12565, 2022 | 16 | 2022 |
| Probabilistic approach for visual homing of a mobile robot in the presence of dynamic obstacles A Sabnis, GK Arunkumar, V Dwaracherla, L Vachhani IEEE Transactions on Industrial Electronics 63 (9), 5523-5533, 2016 | 16 | 2016 |
| Approximate thompson sampling via epistemic neural networks I Osband, Z Wen, SM Asghari, V Dwaracherla, M Ibrahimi, X Lu, ... Uncertainty in Artificial Intelligence, 1586-1595, 2023 | 13 | 2023 |
| Ensembles for uncertainty estimation: Benefits of prior functions and bootstrapping V Dwaracherla, Z Wen, I Osband, X Lu, SM Asghari, B Van Roy arXiv preprint arXiv:2206.03633, 2022 | 12 | 2022 |
| From predictions to decisions: The importance of joint predictive distributions Z Wen, I Osband, C Qin, X Lu, M Ibrahimi, V Dwaracherla, M Asghari, ... arXiv preprint arXiv:2107.09224, 2021 | 11 | 2021 |
| Gradient estimation with simultaneous perturbation and compressive sensing VS Borkar, VR Dwaracherla, N Sahasrabudhe Journal of Machine Learning Research 18 (161), 1-27, 2018 | 11 | 2018 |
| Evaluating predictive distributions: Does Bayesian deep learning work? I Osband, Z Wen, SM Asghari, X Lu, M Ibrahimi, V Dwaracherla, ... | 9 | 2021 |
| Efficient exploration for llms V Dwaracherla, SM Asghari, B Hao, B Van Roy arXiv preprint arXiv:2402.00396, 2024 | 8 | 2024 |
| Evaluating high-order predictive distributions in deep learning I Osband, Z Wen, SM Asghari, V Dwaracherla, X Lu, B Van Roy Uncertainty in Artificial Intelligence, 1552-1560, 2022 | 7 | 2022 |
| Parameterized indexed value function for efficient exploration in reinforcement learning T Tan, Z Xiong, VR Dwaracherla Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5948-5955, 2020 | 7 | 2020 |
| Langevin dqn V Dwaracherla, B Van Roy arXiv preprint arXiv:2002.07282, 2020 | 6 | 2020 |
| Posterior sampling networks VR Dwaracherla, B Van Roy, M Ibrahimi Reinforcement Learning and Decision Making Conference, 366-370, 2019 | 4 | 2019 |
| Discrete time position feedback based steering control for autonomous homing of a mobile robot VR Dwaracherla, S Thakar, GKA Kumar, L Vachhani 2016 12th IEEE International Conference on Control and Automation (ICCA …, 2016 | 4 | 2016 |
| Exploration using hyper-models B Van Roy, X Lu, VR Dwaracherla, Z Wen, M Ibrahimi, IDM Osband US Patent App. 17/639,504, 2022 | 1 | 2022 |
| Robustness of epinets against distributional shifts X Lu, I Osband, SM Asghari, S Gowal, V Dwaracherla, Z Wen, B Van Roy arXiv preprint arXiv:2207.00137, 2022 | 1 | 2022 |
Benjamin Van RoyStanford University在 stanford.edu 的电子邮件经过验证
