| Gaussian process optimization in the bandit setting: No regret and experimental design N Srinivas, A Krause, SM Kakade, M Seeger arXiv preprint arXiv:0912.3995, 2009 | 3358 | 2009 |
| A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001 | 1862 | 2001 |
| Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002 | 1598 | 2002 |
| Tensor decompositions for learning latent variable models. A Anandkumar, R Ge, DJ Hsu, SM Kakade, M Telgarsky J. Mach. Learn. Res. 15 (1), 2773-2832, 2014 | 1522 | 2014 |
| Information-theoretic regret bounds for gaussian process optimization in the bandit setting N Srinivas, A Krause, SM Kakade, MW Seeger IEEE Transactions on Information Theory 58 (5), 3250-3265, 2012 | 1280 | 2012 |
| Information-Theoretic Regret Boundsfor Gaussian Process Optimization in the Bandit Setting N Srinivas, A Krause, S Kakade, M Seeger Information Theory, IEEE Transactions on, 1-1, 2011 | 1280 | 2011 |
| Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006 | 1246 | 2006 |
| How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International conference on machine learning, 1724-1732, 2017 | 1153 | 2017 |
| Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019 | 1152 | 2019 |
| Robust aggregation for federated learning K Pillutla, SM Kakade, Z Harchaoui IEEE Transactions on Signal Processing 70, 1142-1154, 2022 | 1111 | 2022 |
| Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002 | 1100 | 2002 |
| Stochastic linear optimization under bandit feedback V Dani, TP Hayes, SM Kakade 21st Annual Conference on Learning Theory, 355-366, 2008 | 1076 | 2008 |
| Multi-view clustering via canonical correlation analysis K Chaudhuri, SM Kakade, K Livescu, K Sridharan Proceedings of the 26th annual international conference on machine learning …, 2009 | 1020 | 2009 |
| On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003 | 968 | 2003 |
| Global convergence of policy gradient methods for the linear quadratic regulator M Fazel, R Ge, S Kakade, M Mesbahi International conference on machine learning, 1467-1476, 2018 | 851 | 2018 |
| On the theory of policy gradient methods: Optimality, approximation, and distribution shift A Agarwal, SM Kakade, JD Lee, G Mahajan Journal of Machine Learning Research 22 (98), 1-76, 2021 | 678 | 2021 |
| Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000 | 658 | 2000 |
| A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012 | 656 | 2012 |
| Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002 | 618 | 2002 |
| Online meta-learning C Finn, A Rajeswaran, S Kakade, S Levine International conference on machine learning, 1920-1930, 2019 | 612 | 2019 |
Daniel HsuColumbia UniversityVerified email at cs.columbia.edu
