| Stochastic activation pruning for robust adversarial defense GS Dhillon, K Azizzadenesheli, ZC Lipton, J Bernstein, J Kossaifi, ... International Conference on Learning Representations (ICLR) 2018, 2018 | 258 | 2018 |
| signSGD: compressed optimisation for non-convex problems J Bernstein, YX Wang, K Azizzadenesheli, A Anandkumar International Conference on Machine Learning (ICML) 2018, 2018 | 239 | 2018 |
| Efficient Exploration through Bayesian Deep Q-Networks K Azizzadenesheli, A Anandkumar Neural Information Processing Systems (NIPS) 2017 Workshop, 2018 | 84 | 2018 |
| Neural Lander: Stable Drone Landing Control using Learned Dynamics G Shi, X Shi, M O'Connell, R Yu, K Azizzadenesheli, A Anandkumar, ... International Conference on Robotics and Automation (ICRA) 2019, 2018 | 64 | 2018 |
| Reinforcement learning of POMDPs using spectral methods K Azizzadenesheli, A Lazaric, A Anandkumar 29th Annual Conference on Learning Theory (COLT) 2016, 2016 | 60 | 2016 |
| Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear ZC Lipton, K Azizzadenesheli, A Kumar, L Li, J Gao, L Deng Neural Information Processing Systems (NIPS) 2016 Workshop, 2016 | 57* | 2016 |
| Regularized Learning for Domain Adaptation under Label Shifts K Azizzadenesheli, A Liu, F Yang, A Anandkumar International Conference on Learning Representations (ICLR) 2018, 2018 | 52 | 2018 |
| signSGD with Majority Vote is Communication Efficient and Fault Tolerant J Bernstein, J Zhao, K Azizzadenesheli, A Anandkumar International Conference on Learning Representations (ICLR) 2019, 2018 | 40 | 2018 |
| Surprising Negative Results for Generative Adversarial Tree Search K Azizzadenesheli, B Yang, W Liu, Z Lipton, A Anandkumar Neural Information Processing Systems (NeurIPS) 2018 Workshop, 2018 | 23* | 2018 |
| Reinforcement learning in rich-observation MDPs using spectral methods K Azizzadenesheli, A Lazaric, A Anandkumar Multi-disciplinary Conference on Reinforcement Learning and Decision Making …, 2016 | 16 | 2016 |
| Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems S Lale, K Azizzadenesheli, B Hassibi, A Anandkumar Neural Information Processing Systems (Neurips) 2020, 2020 | 11 | 2020 |
| Learning causal state representations of partially observable environments A Zhang, ZC Lipton, L Pineda, K Azizzadenesheli, A Anandkumar, L Itti, ... arXiv preprint arXiv:1906.10437, 2019 | 9 | 2019 |
| Stochastic linear bandits with hidden low rank structure S Lale, K Azizzadenesheli, A Anandkumar, B Hassibi arXiv preprint arXiv:1901.09490, 2019 | 8 | 2019 |
| Eikonet: Solving the eikonal equation with deep neural networks JD Smith, K Azizzadenesheli, ZE Ross IEEE Transactions on Geoscience and Remote Sensing 2020, 2020 | 7 | 2020 |
| Open problem: Approximate planning of POMDPs in the class of memoryless policies K Azizzadenesheli, A Lazaric, A Anandkumar Conference on Learning Theory (COLT) 2016, 1639-1642, 1639-1642, 2016 | 7 | 2016 |
| Neural Operator: Graph Kernel Network for Partial Differential Equations Z Li, N Kovachki, K Azizzadenesheli, B Liu, K Bhattacharya, A Stuart, ... The International Conference on Learning Representations (ICLR) 2020, Workshop, 2020 | 5 | 2020 |
| Adaptive Control and Regret Minimization in Linear Quadratic Gaussian (LQG) Setting S Lale, K Azizzadenesheli, B Hassibi, A Anandkumar arXiv preprint arXiv:2003.05999, 2020 | 5* | 2020 |
| Maybe a few considerations in Reinforcement Learning Research? K Azizzadenesheli | 5 | 2019 |
| Policy Gradient in Partially Observable Environments: Approximation and Convergence K Azizzadenesheli, Y Yue, A Anandkumar arXiv preprint arXiv:1810.07900, 2018 | 4* | 2018 |
| Experimental results: Reinforcement learning of pomdps using spectral methods K Azizzadenesheli, A Lazaric, A Anandkumar arXiv preprint arXiv:1705.02553, 2017 | 4 | 2017 |
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
