Alex Kulesza

Ph.D. Candidate
Computer and Information Science
University of Pennsylvania

Office: Levine 514
Email: lastname cis upenn edu

Publications

New H∞ bounds for the recursive least squares algorithm exploiting input structure
K. Crammer, A. Kulesza, and M. Dredze. ICASSP 2012.
pdf bib
@inproceedings{crammer2012new,
  title =	 {{New $H^\infty$ bounds for the recursive least squares algorithm
	           exploiting input structure}},
  author =	 {Crammer, K. and Kulesza, A. and Dredze, M.},
  booktitle =	 {2012 IEEE International Conference on Acoustics, Speech and
	          Signal Processing (ICASSP)},
  year =	 2012,
}	
Learning determinantal point processes
A. Kulesza and B. Taskar. UAI 2011.
pdf bib
@inproceedings{kulesza2011learning,
  title =	 {{Learning determinantal point processes}},
  author =	 {Kulesza, A. and Taskar, B.},
  booktitle =	 {Proceedings of the 27th Conference on Uncertainty
	          in Artificial Intelligence},
  year =	 2011,
}	
k-DPPs: fixed-size determinantal point processes
A. Kulesza and B. Taskar. ICML 2011.
pdf bib
@inproceedings{kulesza2011kdpps,
  title =	 {{k-DPPs: fixed-size determinantal point processes}},
  author =	 {Kulesza, A. and Taskar, B.},
  booktitle =	 {Proceedings of the 28th International Conference on
	          Machine Learning},
  year =	 2011,
}	
Structured determinantal point processes
A. Kulesza and B. Taskar. NIPS 2010.
pdf supplement bib
@inproceedings{kulesza2011structured,
  title =	 {{Structured determinantal point processes}},
  author =	 {Kulesza, A. and Taskar, B.},
  booktitle =	 {Advances in neural information processing systems
                  23},
  year =	 2011,
}	
Empirical limitations on high-frequency trading profitability
M. Kearns, A. Kulesza, and Y. Nevmyvaka. Journal of Trading, Fall 2010.
Journal of Trading Best Paper Award 2010
pdf bib
@article{kearns2010empirical,
  title =	 {{Empirical Limitations on High-Frequency Trading
                  Profitability}},
  author =	 {Kearns, M. and Kulesza, A. and Nevmyvaka, Y.},
  journal =	 {The Journal of Trading},
  volume =	 5,
  number =	 4,
  pages =	 {50--62},
  year =	 2010,
}	
Exploiting feature covariance in high-dimensional online learning
J. Ma, A. Kulesza, M. Dredze, K. Crammer, L.K. Saul, and F. Pereira. AISTATS 2010.
pdf bib
@inproceedings{ma2010exploiting,
  title =	 {{Exploiting feature covariance in high-dimensional
                  online learning}},
  author =	 {Ma, J. and Kulesza, A. and Dredze, M. and Crammer,
                  K. and Saul, L.K. and Pereira, F.},
  booktitle =	 {Proceedings of the International Conference on
                  Artificial Intelligence and Statistics},
  year =	 2010,
}	
A theory of learning from different domains
S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J.W. Vaughan. Machine Learning, May 2010.
pdf bib
@article{ben2010theory,
  title =	 {{A theory of learning from different domains}},
  author =	 {Ben-David, S. and Blitzer, J. and Crammer, K. and
                  Kulesza, A. and Pereira, F. and Vaughan, J.W.},
  journal =	 {Machine learning},
  volume =	 79,
  number =	 1,
  pages =	 {151--175},
  issn =	 {0885-6125},
  year =	 2010,
  publisher =	 {Springer},
}	
Multi-domain learning by confidence-weighted parameter combination
M. Dredze, A. Kulesza, and K. Crammer. Machine Learning, May 2010.
pdf bib
@article{dredze2010multi,
  title =	 {{Multi-domain learning by confidence-weighted
                  parameter combination}},
  author =	 {Dredze, M. and Kulesza, A. and Crammer, K.},
  journal =	 {Machine learning},
  volume =	 79,
  number =	 1,
  pages =	 {123--149},
  issn =	 {0885-6125},
  year =	 2010,
  publisher =	 {Springer},
}	
Adaptive regularization of weight vectors
K. Crammer, A. Kulesza, and M. Dredze. NIPS 2009.
pdf bib
@inproceedings{crammer2010adaptive,
  title =	 {{Adaptive regularization of weight vectors}},
  author =	 {Crammer, K. and Kulesza, A. and Dredze, M.},
  booktitle =	 {Advances in Neural Information Processing Systems
                  22},
  year =	 2010,
}	
Approximate learning for structured prediction problems
A. Kulesza. WPE-II report, November 2009.
pdf bib
@unpublished{kulesza2009approximate,
  title =	 {{Approximate learning for structured prediction
                  problems}},
  author =	 {Kulesza, A.},
  month =	 {November},
  year =	 2009,
  note =	 {University of Pennsylvania WPE-II report},
}	
Multi-class confidence weighted algorithms
K. Crammer, M. Dredze, and A. Kulesza. EMNLP 2009.
pdf bib
@inproceedings{crammer2009multi,
  title =	 {{Multi-class confidence weighted algorithms}},
  author =	 {Crammer, K. and Dredze, M. and Kulesza, A.},
  booktitle =	 {Proceedings of the 2009 Conference on Empirical
                  Methods in Natural Language Processing},
  year =	 2009,
}	
Structured learning with approximate inference
A. Kulesza and F. Pereira. NIPS 2007.
pdf bib
@inproceedings{kulesza2008structured,
  title =	 {{Structured learning with approximate inference}},
  author =	 {Kulesza, A. and Pereira, F.},
  booktitle =	 {Advances in neural information processing systems
                  20},
  year =	 2008,
}	
Learning bounds for domain adaptation
J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman. NIPS 2007.
pdf audio remix bib
@inproceedings{blitzer2008learning,
  title =	 {{Learning bounds for domain adaptation}},
  author =	 {Blitzer, J. and Crammer, K. and Kulesza, A. and
                  Pereira, F. and Wortman, J.},
  booktitle =	 {Advances in neural information processing systems
                  20},
  year =	 2008,
}	
Empirical price modeling for sponsored search
K. Ganchev, A. Kulesza, J. Tan, R. Gabbard, Q. Liu, and M. Kearns. WINE 2007.
pdf longer version bib
@inproceedings{ganchev2007empirical,
  title =	 {{Empirical price modeling for sponsored search}},
  author =	 {Ganchev, K. and Kulesza, A. and Tan, J. and Gabbard,
                  R. and Liu, Q. and Kearns, M.},
  booktitle =	 {Proceedings of the 3rd International Conference on
                  Internet and Network Economics},
  year =	 2007,
}	
TBBL: A tree-based bidding language for iterative combinatorial exchanges
R. Cavallo, D.C. Parkes, A.I. Juda, A. Kirsch, A. Kulesza, S. Lahaie, B. Lubin, L. Michael, and J. Shneidman. IJCAI 2005.
pdf bib
@inproceedings{cavallo5tbbl,
  title =	 {{TBBL: A tree-based bidding language for iterative
                  combinatorial exchanges}},
  author =	 {Cavallo, R. and Parkes, D.C. and Juda, A.I. and
                  Kirsch, A. and Kulesza, A. and Lahaie, S. and Lubin,
                  B. and Michael, L. and Shneidman, J.},
  booktitle =	 {Multidisciplinary IJCAI-05 Workshop on Advances in
                  Preference Handling},
  year =	 2005,
}	
A learning approach to improving sentence-level MT evaluation
A. Kulesza and S.M. Shieber. TMI 2004.
pdf thesis version bib
@inproceedings{kulesza2004learning,
  title =	 {{A learning approach to improving sentence-level MT
                  evaluation}},
  author =	 {Kulesza, A. and Shieber, S.M.},
  booktitle =	 {Proceedings of the 10th International Conference on
                  Theoretical and Methodological Issues in Machine
                  Translation},
  year =	 2004,
}	
Confidence estimation for machine translation
J. Blatz, E. Fitzgerald, G. Foster, S. Gandrabur, C. Goutte, A. Kulesza, A. Sanchis, and N. Ueffing. CoLing 2004.
pdf long report bib
@inproceedings{blatz2004confidence,
  title =	 {{Confidence estimation for machine translation}},
  author =	 {Blatz, J. and Fitzgerald, E. and Foster, G. and
                  Gandrabur, S. and Goutte, C. and Kulesza, A. and
                  Sanchis, A. and Ueffing, N.},
  booktitle =	 {Proceedings of the 20th international conference on
                  Computational Linguistics},
  year =	 2004,
}