Publications by Clayton Scott

Recent or Submitted papers

T. Watanabe and C. Scott, ``Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty," accepted to Workshop on Biomedical Image Registration, 2012.
G. Blanchard, G. Lee, and C. Scott,``Generalizing from Several Related Classification Tasks to a New Unlabeled Sample," Neural Information Processing Systems (NIPS) 2011 [Supplemental material].

Journal Papers

J. Kim and C. Scott, ``Robust kernel density estimation," accepted to JMLR pending minor revisions. [Matlab code]
G. Bellala, S. Bhavnani, and C. Scott, ``Group-based active query selection for rapid diagnosis in time-critical situations, accepted for publication in IEEE Transactions on Information Theory.
C. Scott, ``Calibrated Asymmetric Surrogate Losses," Electronic Journal of Statistics, vol. 6, pp. 958-992, 2012.
G. Lee and C. Scott, ``EM algorithms for multivariate Gaussian mixture models with truncated and censored data," Computational Statistics and Data Analysis, vol. 56, no. 9, pp. 2816-2829, 2012. [Matlab code]
D. J. Lingenfelter, J. A. Fessler, C. Scott, Z. He. ``Asymptotic Source Detection Performance of Gamma Ray Imaging Systems Under Model Mismatch," IEEE Trans. Signal Processing, vol. 58, no. 9, pp. 5141--5151, 2011.

G. Lee, W. Finn, and C. Scott, ``Statistical file matching of flow cytometry data," J. Biomedical Informatics, vol. 44, no. 4., pp. 663-676, 2011. [Matlab code]
G. Blanchard, G. Lee, and C. Scott, ``Semi-supervised novelty detection," Journal of Machine Learning Research, vol. 11, pp. 2973-3009, Nov. 2010.
D. J. Lingenfelter, J. A. Fessler, C. Scott, Z. He. ``Benefits of Position-Sensitive Detectors for Radioactive Source Detection," IEEE Trans. Signal Processing, vol. 58, no. 9, pp. 4473-4483, 2010.

S. K. Bhavnani, G. Bellala, A. Ganesan, R. Krishna, P. Saxman, C. Scott, M. Silveira, and C. Given, ``The Nested Structure of Cancer Symptoms: Implications for Analyzing Co-occurrence and Managing Symptoms," Methods of Information in Medicine, vol. 49, no. 6, pp. 581-591, 2010.
M. Davenport, R. Baraniuk, and C. Scott, ``Tuning support vector machines for minimax and Neyman-Pearson classification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 10, Oct. 2010, 1888-1898. [Matlab code]
J. Kim and C. Scott, ``$L_2$ kernel classification," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 32, no. 10, Oct. 2010, 1822 - 1831. [Matlab code]
C. Scott and E. Kolaczyk, ``Nonparametric assessment of contamination in multivariate data using generalized quantile sets and FDR," J. Computational and Graphical Statistics, June 1, 2010, 19(2): 439-456. [appendix | Matlab code | JCGS]
G. Lee and C. Scott, ``Nested support vector machines," IEEE Trans. Signal Processing, vol. 58, no. 3, 1648 - 1660, 2010. [technical report | Matlab code]
C. Scott, G. Bellala, and R. Willett, ``The false discovery rate for statistical pattern recognition," Electronic Journal of Statistics, Vol. 3, 651-677, 2009. [bib | EJS]

A. Singh, C. Scott, and R. Nowak, ``Adaptive Hausdorff estimation of density level sets," The Annals of Statistics, Vol. 37, No. 5B, 2760-2782, 2009. [bib | AoS ]
D. Rossell, R. Guerra and C. Scott, ``Semi-parametric differential expression analysis via partial mixture estimation," Statistical Applications in Genetics and Molecular Biology, vol. 7, no. 1, article 15, 2008. [bib | R code]

C. Scott, ``Performance measures for Neyman-Pearson classification," IEEE Transactions on Information Theory, vol. 53, no. 8, pp. 2852--2863, 2007. [bib]

C. Scott and M. Davenport, ``Regression level set estimation via cost-sensitive classification," IEEE Transactions on Signal Processing, vol. 55, no. 6, pp. 2752-2757, June 2007. [bib]
C. Scott and R. Nowak, ``Robust contour matching via the order preserving assignment problem," IEEE Transactions on Image Processing, vol. 15, no. 7, pp. 1831-1838, July 2006. [bib | Matlab code]
C. Scott and R. Nowak, ``Learning minimum volume sets," Journal of Machine Learning Research, vol. 7, pp. 665--704, April 2006. [bib | Matlab code | errata]

C. Scott and R. Nowak, ``Minimax-optimal classification with dyadic decision trees," IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1335--1353, April 2006. [bib | Matlab code]

C. Scott, ``Tree pruning with subadditive penalties," IEEE Transactions on Signal Processing, vol. 53, no. 12, pp. 4518-4525, Dec. 2005. [bib | errata]

C. Scott and R. Nowak, ``A Neyman-Pearson approach to statistical learning," IEEE Transactions on Information Theory, vol. 51, no. 11, pp. 3806-3819, Nov. 2005. [bib]

C. Scott and R. Nowak, ``TEMPLAR: A wavelet-based framework for pattern learning and analysis," IEEE Transactions on Signal Processing, vo. 52, no. 8, pp. 2264-2274, Sept. 2004. [bib | Matlab code]

J. B. Conrey, E. Fransen, R. Klein, and C. Scott, ``Mean values of Dedekind sums," Journal of Number Theory, 1996. [ps.gz ]

Conference Papers

G. Bellala, J. Stanley, C. Scott and S. Bhavnani, ``Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks," Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), 35-42, 2011. [supplemental material]
J. Kim and C. Scott, ``On the Robustness of Kernel Density M-Estimators," Proceedings of the Twenty-Eighth International Conference on Machine Learning (ICML), 2011.
C. Scott, ``Surrogate Losses and Regret Bounds for Cost-Sensitive Classification with Example-Dependent Costs," Proceedings of the Twenty-Eighth International Conference on Machine Learning (ICML), 2011. Extended version: Tech. Rep. CSPL-400, Communications and Signal Processing Laboratory, University of Michigan, Jan. 2011.
G. Bellala, S. Bhavnani, C. Scott, ``Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach," AISTATS 2011. [supplemental material]
G. Bellala, S. Bhavnani, C. Scott, ``Extensions of Generalized Binary Search to Group Identification and Exponential Costs," Advances in Neural Information Processing Systems 23, 154--162, 2010. [supplemental material]
D. J. Lingenfelter, J. A. Fessler, C. Scott, Z. He. ``Benefits of position-sensitive detectors for source detection with known background," Proc. IEEE Nuc. Sci. Symp. Med. Im. Conf., 2009.
C. Scott and G. Blanchard, ``Novelty detection: Unlabeled data definitely help," D. van Dyk and M. Welling, Eds., Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), JMLR: W&CP 5, 464-471, 2009. [pdf | bib]
J. Kim and C. Scott, ``Performance analysis for L2 kernel classification," Advances in Neural Information Processing Systems 21 (NIPS 2008), D. Koller, D. Schuurmans, Y. Bengio and L. Bottou, Eds., pp. 833-840, 2009. [pdf | bib]
A. Singh, R. Nowak and C. Scott, ``Adaptive Hausdorff estimation of density level sets," Proc. Conference on Learning Theory (COLT), 491-501, Helsinki, July 2008. [pdf | bib]
P. Chhabra, C. Scott, E. Kolaczyk, and M. Crovella, ``Distributed spatial anomaly detection," Proc. 27th IEEE Conf. on Computer Communications (IEEE INFOCOM), 1705-1713, 2008. [pdf | bib]
J. Kim and C. Scott, ``Robust kernel density estimation," Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 3381-3384, Las Vegas, 2008. [pdf | bib]

G. Lee and C. Scott, ``Nested support vector machines," Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2008), 1985-1988, Las Vegas, 2008. [pdf]
F. Thouin, M. Coates, B. Erikkson, R. Nowak, and C.Scott, ``Learning to Satisfy," Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 1981-1984, Las Vegas, 2008. [pdf]

J. Kim and C. Scott, ``Kernel classification via integrated squared error," IEEE Workshop on Statistical Signal Processing, 783-787, Madison, WI, August 2007. [pdf]

C. Scott, G. Bellala, and R. Willett, ``Generalization error analysis for FDR controlled classification," IEEE Workshop on Statistical Signal Processing, 792-796, Madison, WI, August 2007. [pdf]

C. Scott and E. Kolaczyk, ``Annotated minimum volume sets for nonparametric anomaly discovery," IEEE Workshop on Statistical Signal Processing, 234-238, Madison, WI, August 2007 [pdf].

M. Davenport, R. Baraniuk, and C. Scott, ``Minimax support vector machines," IEEE Workshop on Statistical Signal Processing, 630-634, Madison, WI, August 2007 [pdf].

G. Lee and C. Scott, ``The one class support vector machine solution path," Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007), vol. 2, II-521--II-524, Honolulu, USA, April 2007. [ pdf | Matlab code | movies]

M. Davenport, R. Baraniuk, and C. Scott, ``Learning minimum volume sets with support vector machines,'' IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2006), 301-306, Maynooth, Ireland, Sept. 2006. [pdf ]

M. Davenport, R. Baraniuk, and C. Scott, ``Controlling false alarms with support vector machines,'' IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2006), V-589-V-592, Toulouse, France, May 2006. [pdf ]

C. Scott and R. Nowak, ``Learning minimum volume sets," Advances in Neural Information Processing Systems 18 (NIPS 2005), Y. Weiss, B. Schoelkopf and J. Platt, Eds., MIT Press, Cambridge, MA, 1209-1216, 2006. [ ps | pdf ]

C. Scott and R. Nowak, ``On the adaptive properties of decision trees," Advances in Neural Information Processing Systems 17 (NIPS 2004), L. Saul, Y. Weiss and L. Bottou, eds., MIT Press, Cambridge, MA, 1225-1232, 2005. [ ps | pdf ]

C. Scott and R. Nowak, ``Near-minimax optimal classification with dyadic classification trees," Advances in Neural Information Processing Systems 16 (NIPS 2003), S. Thrun, L. Saul, and B. Schoelkopf, eds., MIT Press, Cambridge, MA, 2004. [ ps | pdf ]

C. Scott, R. Willett, and R. Nowak, ``CORT: Classification or regression trees," Proc. IEEE Int. Conf. Accoust., Speech, Signal Processing (ICASSP), VI-153-VI-156, 2003. [ pdf ]
C. Scott and R. Nowak, ``Dyadic classification trees via structural risk minimization," Advances in Neural Information Processing Systems 15 (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, eds., MIT Press, Cambridge, MA, 359-366, 2003. [ ps | pdf ]

C. Scott and R. Nowak, ``Template learning from atomic representations: A wavelet-based approach to pattern analysis," Proc. IEEE Workshop on Statistical and Computational Theories of Vision (published on the web), Vancouver, CA, July 2001. [ps.gz | pdf ]

Workshop Proceedings

G. Lee, L. Stoolman, and C. Scott, ``Transfer Learning for Automatic Gating of Flow Cytometry Data," ICML Workshop on Unsupervised and Transfer Learning, to be published, JMLR W&CP 7, 2011. Also Tech. Rep. CSPL-402, Communications and Signal Processing Laboratory, University of Michigan, 2011.

Posters

S. Bhavnani, A. Ganesan, C. Scott, C. Weber, P. Saxman, ``A Novel System for Rapidly Identifying Toxic Chemicals," Proc. American Medical Informatics Association Annual Symp., 878, Washington, DC, July 2008. [pdf]

Technical reports not entirely subsumed by something above.

C. Scott, ``Calibrated Surrogate Losses for Classification with Label-Dependent Costs," 2010.
G. Bellala, S. Bhavnani, C. Scott, "Query learning with exponential query costs." [pdf]
G. Bellala, S. Bhavnani, C. Scott, "Group-based query learning for rapid diagnosis in time-critical situations." [pdf]
J. Kim, J. Blum, and C. Scott, ``Temporal Features and Kernel Methods for Predicting Sepsis in Postoperative Patients," 2010.
A. Singh, C. Scott, and R. Nowak, ``Adaptive Hausdorff estimation of density level sets," Tech. Rep. ECE-07-06, Univ. Wisconsin-Madison, 2008. [pdf]
C. Scott and E. Kolaczyk, ``Nonparametric assessment of contamination in multivariate data using minimum volume sets and FDR," Tech. Rep. CSPL-381, Communications and Signal Processing Laboratory, University of Michigan, 2007. [pdf]

C. Scott and R. Nowak, ``Learning minimum volume sets," UW-Madison Tech. Rep. ECE-05-2 [ps | pdf]

C. Scott and R. Nowak, "Complexity regularized dyadic classification trees: Efficient pruning and rates of convergence," Rice University Tech. Rep. TREE0201, 2002. [ps]

Dissertation

Clayton D. Scott, Dyadic Decision Trees, Electrical and Computer Engineering, Rice University, August 2004.

Master's Thesis

Clayton D. Scott, A Hierarchical Wavelet-Based Framework For Pattern Analysis and Synthesis, Electrical and Computer Engineering, Rice University, May 2000.

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