| Combining labeled and unlabeled data with co-training A Blum, T Mitchell Proceedings of the eleventh annual conference on Computational learning …, 1998 | 6471 | 1998 |
| Selection of relevant features and examples in machine learning AL Blum, P Langley Artificial intelligence 97 (1-2), 245-271, 1997 | 4834* | 1997 |
| Fast planning through planning graph analysis AL Blum, ML Furst Artificial intelligence 90 (1-2), 281-300, 1997 | 2872 | 1997 |
| Correlation clustering N Bansal, A Blum, S Chawla Machine learning 56 (1), 89-113, 2004 | 1459 | 2004 |
| Learning from labeled and unlabeled data using graph mincuts A Blum, S Chawla Carnegie Mellon University, 2001 | 1213 | 2001 |
| Practical privacy: the SuLQ framework A Blum, C Dwork, F McSherry, K Nissim Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on …, 2005 | 860 | 2005 |
| Training a 3-node neural network is NP-complete AL Blum, RL Rivest Neural Networks 5 (1), 117-127, 1992 | 845 | 1992 |
| A learning theory approach to noninteractive database privacy A Blum, K Ligett, A Roth Journal of the ACM (JACM) 60 (2), 1-25, 2013 | 746 | 2013 |
| Noise-tolerant learning, the parity problem, and the statistical query model A Blum, A Kalai, H Wasserman Journal of the ACM (JACM) 50 (4), 506-519, 2003 | 695 | 2003 |
| The minimum latency problem A Blum, P Chalasani, D Coppersmith, B Pulleyblank, P Raghavan, ... Proceedings of the twenty-sixth annual ACM symposium on Theory of computing …, 1994 | 391 | 1994 |
| Training a 3-node neural network is NP-complete A Blum, RL Rivest Proceedings of the 1st International Conference on Neural Information …, 1988 | 372* | 1988 |
| Clearing algorithms for barter exchange markets: Enabling nationwide kidney exchanges DJ Abraham, A Blum, T Sandholm Proceedings of the 8th ACM conference on Electronic commerce, 295-304, 2007 | 363 | 2007 |
| Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain A Blum Machine Learning 26 (1), 5-23, 1997 | 344 | 1997 |
| Cryptographic primitives based on hard learning problems A Blum, M Furst, M Kearns, RJ Lipton Annual International Cryptology Conference, 278-291, 1993 | 332 | 1993 |
| Beating the hold-out: Bounds for k-fold and progressive cross-validation A Blum, A Kalai, J Langford Proceedings of the twelfth annual conference on Computational learning …, 1999 | 321 | 1999 |
| Co-training and expansion: Towards bridging theory and practice MF Balcan, A Blum, K Yang Advances in neural information processing systems 17, 89-96, 2005 | 320 | 2005 |
| On-line algorithms in machine learning A Blum Online algorithms, 306-325, 1998 | 317 | 1998 |
| Linear approximation of shortest superstrings A Blum, T Jiang, M Li, J Tromp, M Yannakakis Journal of the ACM (JACM) 41 (4), 630-647, 1994 | 312 | 1994 |
| Semi-supervised learning using randomized mincuts A Blum, J Lafferty, MR Rwebangira, R Reddy Proceedings of the twenty-first international conference on Machine learning, 13, 2004 | 311 | 2004 |
| Approximation algorithms for orienteering and discounted-reward TSP A Blum, S Chawla, DR Karger, T Lane, A Meyerson, M Minkoff SIAM Journal on Computing 37 (2), 653-670, 2007 | 297 | 2007 |
