Publications by Clayton Scott

2024 and beyond

Yilun Zhu, Jianxin Zhang, Aditya Gangrade, Clayton Scott, ``Label Noise: Ignorance is Bliss , NeurIPS 2024.
Hrithik Ravi, Clayton Scott, Daniel Soudry, and Yutong Wang, ``The Implicit Bias of Gradient Descent on Separable Multiclass Data,'' NeurIPS 2024.
Matt Raymond, Angela Violi, Clayton Scott, ``Joint Optimization of Piecewise Linear Ensembles,'' MLSP 2024.
Alan Van Omen, Tyler Morrow, Clayton Scott, Elliott Leonard, ``Multilabel Proportion Prediction and Out-of-Distribution Detection on Gamma Spectra of Short-Lived Fission Products,'' Annals of Nuclear Energy, vol. 208, 2004.
Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott, ``Testing the Feasibility of Linear Programs with Bandit Feedback,'' ICML 2024.
Yutong Wang and Clayton Scott, ``Unified Binary and Multiclass Margin-Based Classification," Journal of Machine Learning Research, vol. 25, no. 143, pp. 1--51, 2024.
Jianxin Zhang and Clayton Scott, ``Label Embeddeding by Low Coherence Matrices."

2023

Yilun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, and Clayton Scott, ``Mixture Proportion Estimation Beyond Irreducibility," International Conference on Machine Learning, 2023.

Yutong Wang and Clayton Scott, ``On Classification-Calibration of Gamma-Phi Losses," Conference on Learning Theory, 2023.

2022

Yutong Wang and Clayton Scott, ``Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel," Neural Information Processing Systems 2022.
Jianxin Zhang, Yutong Wang, and Clayton Scott, ``Learning from Label Proportions by Learning with Label Noise," Neural Information Processing Systems 2022.
Yilun Zhu, Clayton Scott, Darren Holland, George Landon, Aaron Fjeldsted, and Azaree Lintereur, ``Fusing Sparsity with Deep Learning for Rotating Scatter Mask Gamma Imaging," IEEE Symposium on Nuclear Science, 2022.
Yutong Wang and Clayton Scott, ``VC dimension of partially quantized neural networks in the overparametrized regime," ICLR 2022.
Alexander Ritchie, Laura Balzano, Daniel Kessler, Chandra Sripada, Clayton Scott, ``Supervised PCA: A Multiobjective Approach.''

2021

Yutong Wang and Clayton Scott, ``An exact solver for the Weston-Watkins SVM subproblem," Proceedings of the 38th International Conference on Machine Learning, PMLR 139:10894-10904, 2021.
G. Blanchard, A. Deshmukh, U. Dogan, G. Lee, and C. Scott, ``Domain Generalization by Marginal Transfer Learning," Journal of Machine Learning Research, vol. 22, no. 2, pp. 1-55, 2021.

2020

Alexander Ritchie, Robert A. Vandermeulen, Clayton Scott, ``Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations,'' Advances in Neural Information Processing Systems (NeurIPS), 2020.
Yutong Wang and Clayton Scott, ``Weston-Watkins Hinge Loss and Ordered Partitions," Advances in Neural Information Processing Systems (NeurIPS), 2020.
Clayton Scott and Jianxin Zhang, ``Learning from Label Proportions: A Mutual Contamination Framework ," Advances in Neural Information Processing Systems (NeurIPS), 2020 (earlier version)
Han Bao, Clayton Scott, and Masashi Sugiyama, ``Calibrated Surrogate Losses for Adversarially Robust Classification," Conference on Learning Theory, 2020 (Original Version).
Tasha Thong, Yutong Wang, Michael D. Brooks, Christopher T. Lee, Clayton Scott, Laura Balzano, Max S. Wicha, Justin A. Colacino, ``Hybrid Stem Cell States: Insights Into the Relationship Between Mammary Development and Breast Cancer Using Single-Cell Transcriptomics," Frontiers in Cell and Developmental Biology, vol. 8, article 288, 2000. Supporting technical report here.

2019

H. Keshavarz, X. Nguyen, C. Scott, ``Local inversion-free estimation of spatial Gaussian processes" Electronic Journal of Statistics, vol. 13, no. 2, pp. 4224-4279, 2019.
Yuren Zhong, Aniket Anand Deshmukh, Clayton Scott, ``PAC Reinforcement Learning without Real-World Feedback."
R. Vandermeulen and C. Scott, ``An Operator Theoretic Approach to Nonparametric Mixture Models," The Annals of Statistics, vol. 47, no. 5, pp. 2704-2733, 2019. (Supplemental).
Aniket Anand Deshmukh, Srinagesh Sharma, James W. Cutler, Mark Moldwin, Clayton Scott, ``Simple Regret Minimization for Contextual Bandits," presented at the ICML Workshop on Exploration in Reinforcement Learning, 2019 (workshop site)
Alexander Ritchie, Clayton Scott, Laura Balzano, Daniel Kessler, Chandra Sripada, ``Supervised Principal Component Analysis via Manifold Optimization," IEEE Data Science Workshop, pp. 6-10, 2019.
J. Katz-Samuels and C. Scott, ``Top Feasible Arm Identification," Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 89:1593-1601, 2019.
J. Katz-Samuels, G. Blanchard, and C. Scott, "Decontamination of Mutual Contamination Models," Journal of Machine Learning Research, vol. 20, no. 41, pp. 1-57, 2019. Subsumes our AISTATS 2014 paper of the same name.
C. Scott, ``A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation," Proceedings of the 30th International Conference on Algorithmic Learning Theory, vol. 98 of Proceedings of Machine Learning Research, pp. 738-761, 2019.

2018

G. Nataraj, J.-F. Nielsen, C. Scott, J. A. Fessler, ``Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels," IEEE Transactions on Medical Imaging, vol. 37, no. 9, pp. 2103--2114, 2018.
J. Katz-Samuels and C. Scott, ``Feasible Arm Identification," Proceedings of the 35th International Conference on Machine Learning, PMLR 80:2535-2543, 2018.
H. Keshavarz, C. Scott, X. Nguyen, "Optimal change point detection in Gaussian processes," Journal of Statistical Planning and Inference, vol. 193, pp. 151-178, 2018. Also avXiv:1506.01338.
J. Katz-Samuels and C. Scott, ``Nonparametric Preference Completion," Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, PMLR 84:632-641, 2018.

2017

A. Deshmukh, U. Dogan, and C. Scott, ``Multi-Task Learning for Contextual Bandits," Advances in Neural Information Processing Systems 30, pp. 4848--4856, 2017.
E. Cruz Cortes and C. Scott, ``Sparse Approximation of a Kernel Mean," IEEE Trans. Signal Processing, vol. 65, pp. 1310-1323, 2017.
E. Cruz Cortes and C. Scott, ``Consistent Kernel Density Estimation with Non-Vanishing Bandwidth," arXiv:1705.08921.
Y. Ren and C. Scott, ``Adaptive Questionnaires for Direct Identification of Optimal Product Design," arXiv:1701.01231.

2016

G. Blanchard, M. Flaska, G. Handy, S. Pozzi, and C. Scott``Classification with Asymmetric Label Noise: Consistency and Maximal Denoising," Electronic Journal of Statistics, vol. 10, vo. 2, pp. 2780-2824, 2016 (Corrigendum). Preliminary versions appeared at COLT 2013 and AISTATS 2015 (see below).
H. Keshavarz, C. Scott, X. Nguyen, "On the consistency of inversion-free parameter estimation for Gaussian random fields," J. Multivariate Analysis, vol. 150, pp. 245-266, Sept. 2016; also avXiv:1601.03822
H. Ramaswamy, C. Scott, and A. Tewari, "Mixture Proportion Estimation via Kernel Embedding of Distributions" Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2052-2060, 2016.

2015

C. Scott, ``A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels," Proc 18th Int. Conf. on Artificial Intelligence and Statistics, pp. 838--846, 2015

2014

R. Vandermeulen and C. Scott, ``Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space," Advances in Neural Information Processing Systems 27, pp. 433--441, 2014.
T. Watanabe, D. Kessler, C. Scott, C. Sripada, ``Multisite Disease Classification with Functional Connectomes via Multitask Structured Sparse SVM," Worshop on Sparsity Techniques in Medical Imaging (STMI), 2014.
G. Blanchard and C. Scott, ``Decontamination of Mutually Contaminated Models," Proc. 17th Int. Conf. Artificial Intelligence and Statistics (AISTATS), JMLR W&CP 33:1-9, 2014. Selected as a Notable Paper. File includes supplemental material.
T. Sanderson and C. Scott, ``Class Proportion Estimation with Application to Multiclass Anomaly Rejection," Proc. 17th Int. Conf. Artificial Intelligence and Statistics (AISTATS), JMLR W&CP 33 :850-858, 2014.
E. Cruz Cortes and C. Scott, ``Scalable sparse approximation of a sample mean, Proc. 2014 IEEE Int. Conf. on Acoustic, Speech and Signal Processing (ICASSP), pp. 5237-5241, 2014.
T. Watanabe, D. Kessler, C. Scott, M. Angstadt, C. Sripada, ``Disease Prediction based on Functional Connectomes using a Scalable and Spatially-Informed Support Vector Machine," NeuroImage, 96C:183-202, 2014. Next item is a shorter version.
T. Watanabe, C. Scott, D. Kessler, M. Angstadt, C. Sripada, ``Scalable Fused Lasso SVM for Connectome-based Disease Prediction," Proc. 2014 IEEE Int. Conf. on Acoustic, Speech and Signal Processing (ICASSP), pp. 5989-5993, 2014.

2013

Sripada CS, Kessler D, Welsh R, Angstadt M, Liberzon I, Phan KL, Scott C, Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis, NeuroImage, v. 81, pp. 213-221, 2013.
G. Bellala, J. Stanley, S. Bhavnani, C. Scott, ``A Rank-based Approach to Active Diagnosis", IEEE Trans. Pattern Analysis and Machine Intelligence, v. 35, no. 9, pp. 2078-2090, 2013. [Supplemental]
Ren, Y., Scott, C., and Papalambros, P. Y., ``A Scalable Preference Elicitation Algorithm Using Group Generalized Binary Search," ASME International Design Engineering Technical Conferences, Portland, OR, Aug 4 - 7, 2013, DETC2013-13059.
R. Vandermeulen and C. Scott, ``Consistency of Robust Kernel Density Estimators," Proc. Conf. on Learning Theory (COLT), JMLR W&CP 30:568-591, 2013.
C. Scott, G. Blanchard, G. Handy, ``Classification with Asymmetric Label Noise: Consistency and Maximal Denoising," Proc. Conf. on Learning Theory (COLT), JMLR W&CP 30:489-511, 2013.
D. J. Lingenfelter, J. A. Fessler, C. Scott, Z. He, ``Source Detection Performance Prediction for a CdZnTe Array," IEEE Trans. Nuclear Science, v. 60, no. 1, pp. 204 - 212, 2013.

2012

J. Kim and C. Scott, ``Robust kernel density estimation," Journal of Machine Learning Research, vol. 13, pp. 2529-2565, 2012. [Matlab code]
T. Sanderson, C. Scott, M. Flaska, and S. A. Pozzi, "Machine Learning for Digital Pulse Shape Discrimination," Proc. IEEE Nuc. Sci. Symp., 2012.
C. Scott, ``The equivalence between the one-class and paired support vector machines for nonseparable data," Tech. Rep. CSPL-412, Communications and Signal Processing Laboratory, University of Michigan, Oct. 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]
C. Scott, ``Calibrated Asymmetric Surrogate Losses," Electronic Journal of Statistics, vol. 6, pp. 958-992, 2012.
T. Watanabe and C. Scott, ``Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty," Workshop on Biomedical Image Registration (WBIR), 2012.
G. Bellala, S. Bhavnani, and C. Scott, ``Group-based active query selection for rapid diagnosis in time-critical situations," IEEE Transactions on Information Theory, vol. 58, no. 1, pp. 459-478, 2012.

2011

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].
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]
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. 11, pp. 5141--5151, 2011.

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. 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. 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.

2010

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.

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. [Supplemental material | 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. 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]
G. Lee and C. Scott, ``Nested support vector machines," IEEE Trans. Signal Processing, vol. 58, no. 3, 1648 - 1660, 2010. [technical report | Matlab code]

2009

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. 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. [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. [bib]
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]

2008

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. [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. [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. [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.
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.
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]

2007

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]

J. Kim and C. Scott, ``Kernel classification via integrated squared error," IEEE Workshop on Statistical Signal Processing, 783-787, Madison, WI, August 2007.
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.
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.
M. Davenport, R. Baraniuk, and C. Scott, ``Minimax support vector machines," IEEE Workshop on Statistical Signal Processing, 630-634, Madison, WI, August 2007
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. [Matlab code | movies]

2006

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]

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.
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.
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.

2005

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, ``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.

2004 and earlier

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]

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

Additional 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."
G. Bellala, S. Bhavnani, C. Scott, "Group-based query learning for rapid diagnosis in time-critical situations."
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.
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.
C. Scott and R. Nowak, ``Learning minimum volume sets," UW-Madison Tech. Rep. ECE-05-2
C. Scott and R. Nowak, "Complexity regularized dyadic classification trees: Efficient pruning and rates of convergence," Rice University Tech. Rep. TREE0201, 2002.

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.

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.