| A theory of learning from different domains S Ben-David, J Blitzer, K Crammer, A Kulesza, F Pereira, JW Vaughan Machine learning 79, 151-175, 2010 | 3012 | 2010 |
| Determinantal point processes for machine learning A Kulesza, B Taskar Foundations and Trends® in Machine Learning 5 (2–3), 123-286, 2012 | 1006 | 2012 |
| Learning bounds for domain adaptation J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman Advances in neural information processing systems 20, 2007 | 511 | 2007 |
| 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 | 438 | 2004 |
| Adaptive regularization of weight vectors K Crammer, A Kulesza, M Dredze Advances in neural information processing systems 22, 2009 | 360 | 2009 |
| k-DPPs: Fixed-size determinantal point processes A Kulesza, B Taskar Proceedings of the 28th International Conference on Machine Learning (ICML …, 2011 | 278 | 2011 |
| Structured learning with approximate inference A Kulesza, F Pereira Advances in neural information processing systems 20, 2007 | 181 | 2007 |
| Structured determinantal point processes A Kulesza, B Taskar Proc. NIPS, 2010 | 153 | 2010 |
| Multi-domain learning by confidence-weighted parameter combination M Dredze, A Kulesza, K Crammer Machine Learning 79 (1-2), 123-149, 2010 | 140 | 2010 |
| 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 | 133 | 2015 |
| Near-optimal map inference for determinantal point processes J Gillenwater, A Kulesza, B Taskar Advances in Neural Information Processing Systems 25, 2012 | 130 | 2012 |
| Learning determinantal point processes A Kulesza, B Taskar | 129 | 2011 |
| A learning approach to improving sentence-level MT evaluation A Kulesza, SM Shieber Proceedings of the 10th International Conference on Theoretical and …, 2004 | 128 | 2004 |
| 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 | 127 | 2014 |
| Adaptive regularization of weight vectors K Crammer, A Kulesza, M Dredze Machine learning 91, 155-187, 2013 | 127 | 2013 |
| 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 | 104 | 2012 |
| Empirical limitations on high-frequency trading profitability M Kearns, A Kulesza, Y Nevmyvaka The Journal of Trading 5 (4), 50-62, 2010 | 103 | 2010 |
| Multi-class confidence weighted algorithms K Crammer, M Dredze, A Kulesza Proceedings of the 2009 Conference on Empirical Methods in Natural Language …, 2009 | 100 | 2009 |
| Expectation-maximization for learning determinantal point processes JA Gillenwater, A Kulesza, E Fox, B Taskar Advances in Neural Information Processing Systems 27, 2014 | 94 | 2014 |
| Abstraction selection in model-based reinforcement learning N Jiang, A Kulesza, S Singh International Conference on Machine Learning, 179-188, 2015 | 70 | 2015 |
