A theory of learning from different domains S Ben-David, J Blitzer, K Crammer, A Kulesza, F Pereira, JW Vaughan Machine learning 79 (1-2), 151-175, 2010 | 1441 | 2010 |

Determinantal point processes for machine learning A Kulesza, B Taskar arXiv preprint arXiv:1207.6083, 2012 | 663 | 2012 |

Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference S Thrun, LK Saul, B Schölkopf MIT press, 2004 | 605* | 2004 |

Learning bounds for domain adaptation J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman Advances in neural information processing systems, 129-136, 2008 | 372 | 2008 |

Confidence estimation for machine translation J Blatz, E Fitzgerald, G Foster, S Gandrabur, C Goutte, A Kulesza, ... Coling 2004: Proceedings of the 20th international conference on …, 2004 | 353 | 2004 |

Adaptive regularization of weight vectors K Crammer, A Kulesza, M Dredze Advances in neural information processing systems 22, 414-422, 2009 | 315 | 2009 |

k-DPPs: Fixed-size determinantal point processes A Kulesza, B Taskar ICML, 2011 | 197 | 2011 |

Structured learning with approximate inference A Kulesza, F Pereira Advances in neural information processing systems, 785-792, 2008 | 173 | 2008 |

Structured determinantal point processes A Kulesza, B Taskar Proc. NIPS, 2010 | 127 | 2010 |

A learning approach to improving sentence-level MT evaluation A Kulesza, SM Shieber Proceedings of the 10th International Conference on Theoretical and …, 2004 | 120 | 2004 |

Adaptive regularization of weight vectors K Crammer, A Kulesza, M Dredze Machine learning 91 (2), 155-187, 2013 | 116 | 2013 |

Near-optimal map inference for determinantal point processes J Gillenwater, A Kulesza, B Taskar Advances in Neural Information Processing Systems 25, 2735-2743, 2012 | 106 | 2012 |

Multi-domain learning by confidence-weighted parameter combination M Dredze, A Kulesza, K Crammer Machine Learning 79 (1-2), 123-149, 2010 | 105 | 2010 |

A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization. K Hong, JM Conroy, B Favre, A Kulesza, H Lin, A Nenkova LREC, 1608-1616, 2014 | 103 | 2014 |

Learning determinantal point processes A Kulesza, B Taskar | 100 | 2011 |

Multi-class confidence weighted algorithms K Crammer, M Dredze, A Kulesza Proceedings of the 2009 Conference on Empirical Methods in Natural Language …, 2009 | 93 | 2009 |

Empirical limitations on high-frequency trading profitability M Kearns, A Kulesza, Y Nevmyvaka The Journal of Trading 5 (4), 50-62, 2010 | 92 | 2010 |

Discovering diverse and salient threads in document collections J Gillenwater, A Kulesza, B Taskar Proceedings of the 2012 Joint Conference on Empirical Methods in Natural …, 2012 | 86 | 2012 |

Expectation-maximization for learning determinantal point processes JA Gillenwater, A Kulesza, E Fox, B Taskar Advances in Neural Information Processing Systems 27, 3149-3157, 2014 | 75 | 2014 |

The dependence of effective planning horizon on model accuracy N Jiang, A Kulesza, S Singh, R Lewis Proceedings of the 2015 International Conference on Autonomous Agents and …, 2015 | 72 | 2015 |