| Combining labeled and unlabeled data with co-training A Blum, T Mitchell Proceedings of the eleventh annual conference on Computational learning …, 1998 | 7806 | 1998 |
| Selection of relevant features and examples in machine learning AL Blum, P Langley Artificial intelligence 97 (1-2), 245-271, 1997 | 4790 | 1997 |
| Fast planning through planning graph analysis AL Blum, ML Furst Artificial intelligence 90 (1-2), 281-300, 1997 | 3055 | 1997 |
| Correlation clustering N Bansal, A Blum, S Chawla Machine learning 56, 89-113, 2004 | 1872 | 2004 |
| Training a 3-node neural network is NP-complete A Blum, R Rivest Advances in neural information processing systems 1, 1988 | 1453 | 1988 |
| Learning from labeled and unlabeled data using graph mincuts A Blum, S Chawla Carnegie Mellon University, 2001 | 1340 | 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 | 1119 | 2005 |
| 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 | 967 | 2003 |
| A learning theory approach to noninteractive database privacy A Blum, K Ligett, A Roth Journal of the ACM (JACM) 60 (2), 1-25, 2013 | 915 | 2013 |
| 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 | 533 | 1999 |
| 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 | 484 | 2007 |
| 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 | 450 | 1994 |
| Foundations of data science A Blum, J Hopcroft, R Kannan Cambridge University Press, 2020 | 449 | 2020 |
| Cryptographic primitives based on hard learning problems A Blum, M Furst, M Kearns, RJ Lipton Annual international cryptology conference, 278-291, 1993 | 448 | 1993 |
| Planning in the presence of cost functions controlled by an adversary HB McMahan, GJ Gordon, A Blum Proceedings of the 20th International Conference on Machine Learning (ICML …, 2003 | 420 | 2003 |
| Co-training and expansion: Towards bridging theory and practice MF Balcan, A Blum, K Yang Advances in neural information processing systems 17, 2004 | 401 | 2004 |
| Empirical support for winnow and weighted-majority algorithms: Results on a calendar scheduling domain A Blum Machine Learning 26, 5-23, 1997 | 373 | 1997 |
| 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 | 372 | 2007 |
| On-line algorithms in machine learning A Blum Online algorithms: the state of the art, 306-325, 2005 | 363 | 2005 |
| Weakly learning DNF and characterizing statistical query learning using Fourier analysis A Blum, M Furst, J Jackson, M Kearns, Y Mansour, S Rudich Proceedings of the twenty-sixth annual ACM symposium on Theory of computing …, 1994 | 358 | 1994 |
