| A global geometric framework for nonlinear dimensionality reduction JB Tenenbaum, V De Silva, JC Langford science 290 (5500), 2319-2323, 2000 | 14303 | 2000 |
| Principal manifolds and nonlinear dimensionality reduction via tangent space alignment Z Zhang, H Zha SIAM journal on scientific computing 26 (1), 313-338, 2004 | 2124 | 2004 |
| CAPTCHA: Using hard AI problems for security L Von Ahn, M Blum, NJ Hopper, J Langford International conference on the theory and applications of cryptographic …, 2003 | 1834 | 2003 |
| A contextual-bandit approach to personalized news article recommendation L Li, W Chu, J Langford, RE Schapire Proceedings of the 19th international conference on World wide web, 661-670, 2010 | 1815 | 2010 |
| Telling humans and computers apart automatically L Von Ahn, M Blum, J Langford Communications of the ACM 47 (2), 56-60, 2004 | 1098 | 2004 |
| Cover trees for nearest neighbor A Beygelzimer, S Kakade, J Langford Proceedings of the 23rd international conference on Machine learning, 97-104, 2006 | 908 | 2006 |
| Feature hashing for large scale multitask learning K Weinberger, A Dasgupta, J Langford, A Smola, J Attenberg Proceedings of the 26th annual international conference on machine learning …, 2009 | 855 | 2009 |
| The isomap algorithm and topological stability M Balasubramanian, EL Schwartz, JB Tenenbaum, V de Silva, ... Science 295 (5552), 7-7, 2002 | 803 | 2002 |
| Cost-sensitive learning by cost-proportionate example weighting B Zadrozny, J Langford, N Abe Third IEEE international conference on data mining, 435-442, 2003 | 714 | 2003 |
| Search-based structured prediction H Daumé, J Langford, D Marcu Machine learning 75 (3), 297-325, 2009 | 573 | 2009 |
| Epoch-Greedy algorithm for multi-armed bandits with side information J Langford, T Zhang Advances in Neural Information Processing Systems (NIPS 2007) 20, 1, 2007 | 507 | 2007 |
| Agnostic active learning MF Balcan, A Beygelzimer, J Langford Journal of Computer and System Sciences 75 (1), 78-89, 2009 | 502 | 2009 |
| Approximately optimal approximate reinforcement learning S Kakade, J Langford In Proc. 19th International Conference on Machine Learning, 2002 | 496 | 2002 |
| Multi-label prediction via compressed sensing D Hsu, SM Kakade, J Langford, T Zhang arXiv preprint arXiv:0902.1284, 2009 | 461 | 2009 |
| Scaling up machine learning: Parallel and distributed approaches R Bekkerman, M Bilenko, J Langford Cambridge University Press, 2011 | 453 | 2011 |
| Doubly robust policy evaluation and learning M Dudík, J Langford, L Li arXiv preprint arXiv:1103.4601, 2011 | 448* | 2011 |
| Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms L Li, W Chu, J Langford, X Wang Proceedings of the fourth ACM international conference on Web search and …, 2011 | 446 | 2011 |
| PAC model-free reinforcement learning AL Strehl, L Li, E Wiewiora, J Langford, ML Littman Proceedings of the 23rd international conference on Machine learning, 881-888, 2006 | 434 | 2006 |
| Sparse Online Learning via Truncated Gradient. J Langford, L Li, T Zhang Journal of Machine Learning Research 10 (3), 2009 | 432 | 2009 |
| A reliable effective terascale linear learning system A Agarwal, O Chapelle, M Dudík, J Langford The Journal of Machine Learning Research 15 (1), 1111-1133, 2014 | 381 | 2014 |
