In this page, we show the list of references for each algorithms.

Classifier & Regression


PA(PA, PA1, PA2): Passive Aggressive
[Crammer03a]Koby Crammer, Ofer Dekel, Shai Shalev-Shwartz and Yoram Singer, Online Passive-Aggressive Algorithms, Proceedings of the Sixteenth Annual Conference on Neural Information Processing Systems (NIPS), 2003.
[Crammer03b]Koby Crammer and Yoram Singer. Ultraconservative online algorithms for multiclass problems. Journal of Machine Learning Research, 2003.
[Crammer06]Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Online Passive-Aggressive Algorithms. Journal of Machine Learning Research, 2006.
CW: Confidence Weighted Learning
[Dredze08]Mark Dredze, Koby Crammer and Fernando Pereira, Confidence-Weighted Linear Classification, Proceedings of the 25th International Conference on Machine Learning (ICML), 2008
[Crammer08]Koby Crammer, Mark Dredze and Fernando Pereira, Exact Convex Confidence-Weighted Learning, Proceedings of the Twenty Second Annual Conference on Neural Information Processing Systems (NIPS), 2008
[Crammer09a]Koby Crammer, Mark Dredze and Alex Kulesza, Multi-Class Confidence Weighted Algorithms, Empirical Methods in Natural Language Processing (EMNLP), 2009
AROW: Adaptive Regularization of Weight vectors
[Crammer09b]Koby Crammer, Alex Kulesza and Mark Dredze, Adaptive Regularization Of Weight Vectors, Advances in Neural Information Processing Systems, 2009
NHERD: Normal Herd
[Crammer10]Koby Crammer and Daniel D. Lee, Learning via Gaussian Herding, Neural Information Processing Systems (NIPS), 2010.
Iterative Parameter Mixture
[McDonald10]Ryan McDonald, K. Hall and G. Mann, Distributed Training Strategies for the Structured Perceptron, North American Association for Computational Linguistics (NAACL), 2010.
[Mann09]Gideon Mann, R. McDonald, M. Mohri, N. Silberman, and D. Walker, Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models, Neural Information Processing Systems (NIPS), 2009.



minhash: b-Bit Minwise Hash
[Ping2010]Ping Li, Arnd Christian Konig, b-Bit Minwise Hashing, WWW, 2010
euclid_lsh: Euclidean LSH
[Datar2004]Mayur Datar, Nicole Immorlica, Piotr Indyk, Vahab S. Mirokni, Locality-Sensitive Hashing Scheme Based on p-Stable Distributions, SCG, 2004.
[Andoni2005]Alex Andoni, LSH Algorithm and Implementation (E2LSH),
[Lv2007]Qin Lv, William Josephson, Zhe Wang, Moses Charikar, Kai Li, Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search, VLDB, 2007.



Local Outlier Factor
[Breunig2000]Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander, LOF: Identifying Density-Based Local Outliers, SIGMOD, 2000.



  1. Feldman, M. Langberg. “A Unified Framework for Approximating and Clustering Data.” STOC ‘11: Proceedings of the 43rd annual ACM Symposium on Theory of Computing, pp. 569-578.
  1. Feldman, M. Faulkner, A. Krause. “Scalable Training of Mixture Models via Coresets.” Advances in Neural Information Processing Systems 24, 2011.