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 | 2632 | 2009 |
A natural policy gradient S Kakade Advances in neural information processing systems 14, 1531-1538, 2001 | 1533 | 2001 |
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 | 1332 | 2014 |
Approximately optimal approximate reinforcement learning S Kakade, J Langford MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 267-274, 2002 | 1195 | 2002 |
Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006 | 1130 | 2006 |
Opponent interactions between serotonin and dopamine ND Daw, S Kakade, P Dayan Neural networks 15 (4-6), 603-616, 2002 | 1007 | 2002 |
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 | 962 | 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 | 962 | 2011 |
How to escape saddle points efficiently C Jin, R Ge, P Netrapalli, SM Kakade, MI Jordan International conference on machine learning, 1724-1732, 2017 | 959 | 2017 |
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 | 940 | 2009 |
Stochastic Linear Optimization under Bandit Feedback. V Dani, TP Hayes, SM Kakade COLT 2, 3, 2008 | 912 | 2008 |
Meta-learning with implicit gradients A Rajeswaran, C Finn, SM Kakade, S Levine Advances in neural information processing systems 32, 2019 | 844 | 2019 |
On the sample complexity of reinforcement learning SM Kakade PQDT-Global, 2003 | 820 | 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 | 645 | 2018 |
A spectral algorithm for learning hidden markov models D Hsu, SM Kakade, T Zhang Journal of Computer and System Sciences, 2012 | 609 | 2012 |
Learning and selective attention P Dayan, S Kakade, PR Montague Nature neuroscience 3 (11), 1218-1223, 2000 | 608 | 2000 |
Dopamine: generalization and bonuses S Kakade, P Dayan Neural Networks 15 (4-6), 549-559, 2002 | 560 | 2002 |
Robust aggregation for federated learning K Pillutla, SM Kakade, Z Harchaoui IEEE Transactions on Signal Processing 70, 1142-1154, 2022 | 542 | 2022 |
Multi-label prediction via compressed sensing DJ Hsu, SM Kakade, J Langford, T Zhang Advances in neural information processing systems 22, 2009 | 525 | 2009 |
Online meta-learning C Finn, A Rajeswaran, S Kakade, S Levine International conference on machine learning, 1920-1930, 2019 | 483 | 2019 |