Combining labeled and unlabeled data with co-training A Blum, T Mitchell Proceedings of the eleventh annual conference on Computational learning …, 1998 | 6930 | 1998 |

Selection of relevant features and examples in machine learning AL Blum, P Langley Artificial intelligence 97 (1-2), 245-271, 1997 | 5139 | 1997 |

Fast planning through planning graph analysis AL Blum, ML Furst Artificial intelligence 90 (1-2), 281-300, 1997 | 2939 | 1997 |

Correlation clustering N Bansal, A Blum, S Chawla Machine learning 56 (1), 89-113, 2004 | 1600 | 2004 |

Learning from labeled and unlabeled data using graph mincuts A Blum, S Chawla Carnegie Mellon University, 2001 | 1263 | 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 | 913 | 2005 |

Training a 3-node neural network is NP-complete AL Blum, RL Rivest Neural Networks 5 (1), 117-127, 1992 | 879 | 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 | 798 | 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 | 765 | 2003 |

Training a 3-node neural network is NP-complete A Blum, R Rivest Advances in neural information processing systems 1, 1988 | 437* | 1988 |

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 | 414 | 1994 |

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 | 401 | 2007 |

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 | 378 | 1999 |

Cryptographic primitives based on hard learning problems A Blum, M Furst, M Kearns, RJ Lipton Annual International Cryptology Conference, 278-291, 1993 | 362 | 1993 |

Co-training and expansion: Towards bridging theory and practice MF Balcan, A Blum, K Yang Advances in neural information processing systems 17, 2004 | 351 | 2004 |

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 |

On-line algorithms in machine learning A Blum Online algorithms, 306-325, 1998 | 331 | 1998 |

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 | 323 | 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 | 318 | 2007 |

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 | 314 | 1994 |