Alex Kulesza

Postdoctoral Fellow
Computer Science and Engineering
University of Michigan

Office: 4817 BBB
Email: lastname umich edu

Code

Determinantal point processes
MATLAB sampling algorithms for DPPs, k-DPPs, dual DPPs, and sequence SDPPs: tgz

Publications

Abstraction selection in model-based reinforcement learning
Nan Jiang, Alex Kulesza, and Satinder Singh. ICML 2015.
pdf bib
@inproceedings{jiang2015abstraction,
  title = 	{Abstraction Selection in Model-Based Reinforcement Learning},
  author = 	{Nan Jiang and Alex Kulesza and Satinder Singh},
  booktitle = 	{Proceedings of the 32nd International Conference on
		 Machine Learning},
  year = 	{2015},
}
The dependence of effective planning horizon on model accuracy
Nan Jiang, Alex Kulesza, Satinder Singh, and Richard Lewis. AAMAS 2015.
AAMAS 2015 Best Paper Award
pdf bib
@inproceedings{jiang2015dependence,
  title = 	{The Dependence of Effective Planning Horizon on
		 Model Accuracy},
  author = 	{Nan Jiang and Alex Kulesza and Satinder Singh and
		 Richard Lewis},
  booktitle = 	{Proceedings of the 14th International Conference on
		 Autonomous Agents and Multiagent Systems},
  year = 	{2015},
}
Low-rank spectral learning with weighted loss functions
Alex Kulesza, Nan Jiang, and Satinder Singh. AISTATS 2015.
pdf bib
@inproceedings{kulesza2015lowrank,
  title = 	{Low-Rank Spectral Learning with Weighted Loss Functions},
  author = 	{Alex Kulesza and Nan Jiang and Satinder
		 Singh},
  booktitle = 	{Proceedings of the 18th International Conference on
		 Artificial Intelligence and Statistics},
  year = 	{2015},
}
Spectral learning of predictive state representations with insufficient statistics
Alex Kulesza, Nan Jiang, and Satinder Singh. AAAI 2015.
pdf bib
@inproceedings{kulesza2015spectral,
  title = 	{Spectral Learning of Predictive State
		 Representations with Insufficient Statistics},
  author = 	{Alex Kulesza and Nan Jiang and Satinder Singh},
  booktitle = 	{Proceedings of the 29th AAAI Conference on
		 Artificial Intelligence},
  year = 	{2015},
}
An efficient algorithm for the symmetric principal minor assignment problem
Justin Rising, Alex Kulesza, and Ben Taskar. Linear Algebra and its Applications, May 2015.
pdf code bib
@article{rising2015efficient,
  title = 	{An Efficient Algorithm for the Symmetric Principal
		 Minor Assignment Problem},
  author = 	{Justin Rising and Alex Kulesza and Ben Taskar},
  journal = 	{Linear Algebra and its Applications},
  volume = 	{473},
  pages = 	{126--144},
  month = 	{May},
  year = 	{2015},
}
Expectation-maximization for learning determinantal point processes
Jennifer Gillenwater, Alex Kulesza, Emily Fox, and Ben Taskar. NIPS 2014.
pdf supplement bib
@inproceedings{gillenwater2014expectation,
  title = 	{Expectation-Maximization for Learning Determinantal
		 Point Processes},
  author = 	{Jennifer Gillenwater and Alex Kulesza and Emily Fox
		 and Ben Taskar},
  booktitle = 	{Advances in Neural Information Processing Systems
		 27},
  year = 	{2014},
}
A repository of state of the art and competitive baseline summaries for generic news summarization
Kai Hong, John M. Conroy, Benoit Favre, Alex Kulesza, Hui Lin, and Ani Nenkova. LREC 2014.
pdf bib
@inproceedings{hong2014repository,
  title = 	{A Repository of State of the Art and Competitive
		 Baseline Summaries for Generic News Summarization},
  author = 	{Kai Hong and John M. Conroy and Benoit Favre and
		 Alex Kulesza and Hui Lin and Ani Nenkova},
  booktitle = 	{Language Resources and Evaluation Conference 2014},
  year = 	{2014},
}
Low-rank spectral learning
Alex Kulesza, Raj Rao Nadakuditi, and Satinder Singh. AISTATS 2014.
pdf bib
@inproceedings{kulesza2014lowrank,
  title = 	{Low-Rank Spectral Learning},
  author = 	{Alex Kulesza and Raj Rao Nadakuditi and Satinder
		 Singh},
  booktitle = 	{Proceedings of the 17th Conference on Artificial
		 Intelligence and Statistics},
  year = 	{2014},
}
Social collaborative retrieval
Ko-Jen Hsiao, Alex Kulesza, and Alfred O. Hero. Journal of Selected Topics in Signal Processing, August 2014.
arxiv version bib
@article{hsiao2014social2,
  title = 	{Social Collaborative Retrieval},
  author = 	{Ko-Jen Hsiao and Alex Kulesza and Alfred O. Hero},
  journal = 	{Journal of Selected Topics in Signal Processing},
  volume = 	{8},
  number = 	{4},
  pages = 	{680--689},
  month = 	{Aug},
  year = 	{2014},
}
Multi-layer graph analysis for dynamic social networks
Brandon Oselio, Alex Kulesza, and Alfred O. Hero. Journal of Selected Topics in Signal Processing, August 2014.
arxiv version bib
@article{oselio2014multilayer,
  title = 	{Multi-Layer Graph Analysis for Dynamic Social
		 Networks},
  author = 	{Brandon Oselio and Alex Kulesza and Alfred O. Hero},
  journal = 	{Journal of Selected Topics in Signal Processing},
  volume = 	{8},
  number = 	{4},
  pages = 	{514--523},
  month = 	{Aug},
  year = 	{2014},
}
Social collaborative retrieval
Ko-Jen Hsiao, Alex Kulesza, and Alfred O. Hero. WSDM 2014.
pdf bib
@inproceedings{hsiao2014social,
  title = 	{Social Collaborative Retrieval},
  author = 	{Ko-Jen Hsiao and Alex Kulesza and Alfred O. Hero},
  booktitle = 	{Proceedings of the 7th International ACM Conference
		 on Web Search and Data Mining},
  year = 	{2014},
}
Adaptive regularization of weight vectors
Koby Crammer, Alex Kulesza, and Mark Dredze. Machine Learning, May 2013.
pdf bib
@article{crammer2013adaptive,
  title = 	{Adaptive Regularization of Weight Vectors},
  author = 	{Koby Crammer and Alex Kulesza and Mark Dredze},
  journal = 	{Machine Learning},
  volume = 	{91},
  number = 	{2},
  pages = 	{155--187},
  year = 	{2013},
  publisher = 	{Springer},
}
Nyström approximation for large-scale determinantal processes
Raja Hafiz Affandi, Alex Kulesza, Emily Fox, and Ben Taskar. AISTATS 2013.
pdf bib
@inproceedings{affandi2013nystrom,
  title = 	{{N}ystr{\"o}m Approximation for Large-Scale
		 Determinantal Processes},
  author = 	{Raja Hafiz Affandi and Alex Kulesza and Emily Fox
		 and Ben Taskar},
  booktitle = 	{Proceedings of the 16th International Conference on
		 Artificial Intelligence and Statistics},
  year = 	{2013},
}
Determinantal point processes for machine learning
Alex Kulesza and Ben Taskar. Foundations and Trends in Machine Learning, December 2012.
updated arxiv version bib
@article{kulesza2012determinantal,
  title = 	{Determinantal Point Processes for Machine Learning},
  author = 	{Alex Kulesza and Ben Taskar},
  journal = 	{Foundations and Trends in Machine Learning},
  volume = 	{5},
  number = 	{2--3},
  year = 	{2012},
  publisher = 	{Now Publishers},
}
Near-optimal MAP inference for determinantal point processes
Jennifer Gillenwater, Alex Kulesza, and Ben Taskar. NIPS 2012.
pdf supplement bib
@inproceedings{gillenwater2012near,
  title = 	{Near-Optimal {MAP} Inference for Determinantal Point
		 Processes},
  author = 	{Jennifer Gillenwater and Alex Kulesza and Ben
		 Taskar},
  booktitle = 	{Advances in Neural Information Processing Systems
		 25},
  year = 	{2012},
}
Learning with determinantal point processes
Alex Kulesza. Ph.D. thesis, 2012.
2012 Morris and Dorothy Rubinoff Dissertation Award
pdf bib
@phdthesis{kulesza2012learning,
  title = 	{Learning with Determinantal Point Processes},
  author = 	{Alex Kulesza},
  school = 	{University of Pennsylvania},
  year = 	{2012},
}
Markov determinantal point processes
Raja Hafiz Affandi, Alex Kulesza, and Emily Fox. UAI 2012.
pdf supplement bib
@inproceedings{affandi2012markov,
  title = 	{Markov determinantal point processes},
  author = 	{Raja Hafiz Affandi and Alex Kulesza and Emily Fox},
  booktitle = 	{Proceedings of the 28th Conference on Uncertainty in
		 Artificial Intelligence},
  year = 	{2012},
}
Discovering diverse and salient threads in document collections
Jennifer Gillenwater, Alex Kulesza, and Ben Taskar. EMNLP 2012.
pdf supplement bib
@inproceedings{gillenwater2012discovering,
  title = 	{Discovering Diverse and Salient Threads in Document
		 Collections},
  author = 	{Jennifer Gillenwater and Alex Kulesza and Ben
		 Taskar},
  booktitle = 	{Proceedings of the 2012 Conference on Empirical
		 Methods in Natural Language Processing},
  year = 	{2012},
}
New H∞ bounds for the recursive least squares algorithm exploiting input structure
Koby Crammer, Alex Kulesza, and Mark Dredze. ICASSP 2012.
pdf bib
@inproceedings{crammer2012new,
  title = 	{New $H^\infty$ Bounds for the Recursive Least
		 Squares Algorithm Exploiting Input Structure},
  author = 	{Koby Crammer and Alex Kulesza and Mark Dredze},
  booktitle = 	{Proceedings of the 2012 IEEE International
		 Conference on Acoustics, Speech, and Signal
		 Processing},
  year = 	{2012},
}
Learning determinantal point processes
Alex Kulesza and Ben Taskar. UAI 2011.
pdf bib
@inproceedings{kulesza2011learning,
  title = 	{Learning Determinantal Point Processes},
  author = 	{Alex Kulesza and Ben Taskar},
  booktitle = 	{Proceedings of the 27th Conference on Uncertainty in
		 Artificial Intelligence},
  year = 	{2011},
}
k-DPPs: fixed-size determinantal point processes
Alex Kulesza and Ben Taskar. ICML 2011.
pdf bib
@inproceedings{kulesza2011kdpps,
  title = 	{{k-DPP}s: Fixed-Size Determinantal Point Processes},
  author = 	{Alex Kulesza and Ben Taskar},
  booktitle = 	{Proceedings of the 28th International Conference on
		 Machine Learning},
  year = 	{2011},
}
Structured determinantal point processes
Alex Kulesza and Ben Taskar. NIPS 2010.
pdf supplement bib
@inproceedings{kulesza2011structured,
  title = 	{Structured Determinantal Point Processes},
  author = 	{Alex Kulesza and Ben Taskar},
  booktitle = 	{Advances in Neural Information Processing Systems
		 23},
  year = 	{2011},
}
Empirical limitations on high-frequency trading profitability
Michael Kearns, Alex Kulesza, and Yuriy 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 = 	{Michael Kearns and Alex Kulesza and Yuriy Nevmyvaka},
  journal = 	{The Journal of Trading},
  volume = 	{5},
  number = 	{4},
  pages = 	{50--62},
  year = 	{2010},
}
Exploiting feature covariance in high-dimensional online learning
Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, and Fernando Pereira. AISTATS 2010.
pdf bib
@inproceedings{ma2010exploiting,
  title = 	{Exploiting Feature Covariance in High-Dimensional
		 Online Learning},
  author = 	{Justin Ma and Alex Kulesza and Mark Dredze and Koby
		 Crammer and Lawrence K. Saul and Fernando Pereira},
  booktitle = 	{Proceedings of the 13th International Conference on
		 Artificial Intelligence and Statistics},
  year = 	{2010},
}
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. Machine Learning, May 2010.
pdf bib
@article{ben2010theory,
  title = 	{A Theory of Learning from Different Domains},
  author = 	{Shai Ben-David and John Blitzer and Koby Crammer and
		 Alex Kulesza and Fernando Pereira and Jennifer
		 Wortman Vaughan},
  journal = 	{Machine Learning},
  volume = 	{79},
  number = 	{1},
  pages = 	{151--175},
  year = 	{2010},
  publisher = 	{Springer},
}
Multi-domain learning by confidence-weighted parameter combination
Mark Dredze, Alex Kulesza, and Koby Crammer. Machine Learning, May 2010.
pdf bib
@article{dredze2010multi,
  title = 	{Multi-Domain Learning by Confidence-Weighted
		 Parameter Combination},
  author = 	{Mark Dredze and Alex Kulesza and Koby Crammer},
  journal = 	{Machine Learning},
  volume = 	{79},
  number = 	{1},
  pages = 	{123--149},
  year = 	{2010},
  publisher = 	{Springer},
}
Adaptive regularization of weight vectors
Koby Crammer, Alex Kulesza, and Mark Dredze. NIPS 2009.
pdf bib
@inproceedings{crammer2010adaptive,
  title = 	{Adaptive Regularization of Weight Vectors},
  author = 	{Koby Crammer and Alex Kulesza and Mark Dredze},
  booktitle = 	{Advances in Neural Information Processing Systems
		 22},
  year = 	{2010},
}
Approximate learning for structured prediction problems
Alex Kulesza. WPE-II report, November 2009.
pdf bib
@unpublished{kulesza2009approximate,
  title = 	{Approximate Learning for Structured Prediction
		 Problems},
  author = 	{Alex Kulesza},
  month = 	{November},
  year = 	{2009},
  note = 	{University of Pennsylvania WPE-II report},
}
Multi-class confidence weighted algorithms
Koby Crammer, Mark Dredze, and Alex Kulesza. EMNLP 2009.
pdf bib
@inproceedings{crammer2009multi,
  title = 	{Multi-Class Confidence Weighted Algorithms},
  author = 	{Koby Crammer and Mark Dredze and Alex Kulesza},
  booktitle = 	{Proceedings of the 2009 Conference on Empirical
		 Methods in Natural Language Processing},
  year = 	{2009},
}
Structured learning with approximate inference
Alex Kulesza and Fernando Pereira. NIPS 2007.
pdf bib
@inproceedings{kulesza2008structured,
  title = 	{Structured Learning with Approximate Inference},
  author = 	{Alex Kulesza and Fernando Pereira},
  booktitle = 	{Advances in Neural Information Processing Systems
		 20},
  year = 	{2008},
}
Learning bounds for domain adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman. NIPS 2007.
pdf audio remix bib
@inproceedings{blitzer2008learning,
  title = 	{Learning Bounds for Domain Adaptation},
  author = 	{John Blitzer and Koby Crammer and Alex Kulesza and
		 Fernando Pereira and Jennifer Wortman},
  booktitle = 	{Advances in Neural Information Processing Systems
		 20},
  year = 	{2008},
}
Empirical price modeling for sponsored search
Kuzman Ganchev, Alex Kulesza, Jinsong Tan, Ryan Gabbard, Qian Liu, and Michael Kearns. WINE 2007.
pdf longer version bib
@inproceedings{ganchev2007empirical,
  title = 	{Empirical Price Modeling for Sponsored Search},
  author = 	{Kuzman Ganchev and Alex Kulesza and Jinsong Tan and
		 Ryan Gabbard and Qian Liu and Michael Kearns},
  booktitle = 	{Proceedings of the 3rd International Conference on
		 Internet and Network Economics},
  year = 	{2007},
}
TBBL: a tree-based bidding language for iterative combinatorial exchanges
Ruggiero Cavallo, David C. Parkes, Adam I. Juda, Adam Kirsch, Alex Kulesza, Sébastien Lahaie, Benjamin Lubin, Loizos Michael, and Jeffrey Shneidman. IJCAI 2005.
pdf bib
@inproceedings{cavallo5tbbl,
  title = 	{{TBBL}: a Tree-Based Bidding Language for Iterative
		 Combinatorial Exchanges},
  author = 	{Ruggiero Cavallo and David C. Parkes and Adam
		 I. Juda and Adam Kirsch and Alex Kulesza and
		 S{\'e}bastien Lahaie and Benjamin Lubin and Loizos
		 Michael and Jeffrey Shneidman},
  booktitle = 	{Multidisciplinary IJCAI-05 Workshop on Advances in
		 Preference Handling},
  year = 	{2005},
}
A learning approach to improving sentence-level MT evaluation
Alex Kulesza and Stuart M. Shieber. TMI 2004.
pdf thesis version bib
@inproceedings{kulesza2004learning,
  title = 	{A Learning Approach to Improving Sentence-Level {MT}
		 Evaluation},
  author = 	{Alex Kulesza and Stuart M. Shieber},
  booktitle = 	{Proceedings of the 10th International Conference on
		 Theoretical and Methodological Issues in Machine
		 Translation},
  year = 	{2004},
}
Confidence estimation for machine translation
John Blatz, Erin Fitzgerald, George Foster, Simona Gandrabur, Cyril Goutte, Alex Kulesza, Alberto Sanchis, and Nicola Ueffing. CoLing 2004.
pdf long report bib
@inproceedings{blatz2004confidence,
  title = 	{Confidence Estimation for Machine Translation},
  author = 	{John Blatz and Erin Fitzgerald and George Foster and
		 Simona Gandrabur and Cyril Goutte and Alex Kulesza
		 and Alberto Sanchis and Nicola Ueffing},
  booktitle = 	{Proceedings of the 20th International Conference on
		 Computational Linguistics},
  year = 	{2004},
}