Pattern Extraction and Synthesis Using a Hierarchical Wavelet-Based Framework. - Clayton D. Scott; Robert D. Nowak (ICIP, 2000)

A Novel Hierarchical Wavelet-Based Framework for Pattern Analysis and Synthesis. - Clayton D. Scott; Robert D. Nowak (SSIAI, 2000)

Dyadic Classification Trees via Structural Risk Minimization. - Clayton D. Scott; Robert D. Nowak (NIPS, 2002)

CORT: classification or regression trees. - Clayton D. Scott; Rebecca M. Willett; Robert D. Nowak (ICASSP (6), 2003)

Near-Minimax Optimal Classification with Dyadic Classification Trees. - Clayton D. Scott; Robert D. Nowak (NIPS, 2003)

On the Adaptive Properties of Decision Trees. - Clayton D. Scott; Robert D. Nowak (NIPS, 2004)

TEMPLAR: a wavelet-based framework for pattern learning and analysis. - Clayton D. Scott; Robert D. Nowak (IEEE Trans. Signal Process., 2004)

A Neyman-Pearson approach to statistical learning. - Clayton D. Scott; Robert D. Nowak (IEEE Trans. Inf. Theory, 2005)

Controlling False Alarms With Support Vector Machines. - Mark A. Davenport; Richard G. Baraniuk; Clayton D. Scott (ICASSP (5), 2006)

Learning Minimum Volume Sets. - Clayton D. Scott; Robert D. Nowak (J. Mach. Learn. Res., 2006)

Robust Contour Matching Via the Order-Preserving Assignment Problem. - Clayton D. Scott; Robert D. Nowak (IEEE Trans. Image Process., 2006)

Minimax-optimal classification with dyadic decision trees. - Clayton D. Scott; Robert D. Nowak (IEEE Trans. Inf. Theory, 2006)

The One Class Support Vector Machine Solution Path. - Gyemin Lee; Clayton Scott (ICASSP (2), 2007)

Performance Measures for Neyman-Pearson Classification. - Clayton Scott (IEEE Trans. Inf. Theory, 2007)

Regression Level Set Estimation Via Cost-Sensitive Classification. - Clayton D. Scott; Mark A. Davenport (IEEE Trans. Signal Process., 2007)

Adaptive Hausdorff Estimation of Density Level Sets. - Aarti Singh; Robert D. Nowak; Clayton D. Scott (COLT, 2008)

Learning to satisfy. - Frederic Thouin; Mark Coates; Brian Eriksson; Robert D. Nowak; Clayton D. Scott (ICASSP, 2008)

Distributed Spatial Anomaly Detection. - Parminder Chhabra; Clayton D. Scott; Eric D. Kolaczyk; Mark Crovella (INFOCOM, 2008)

Performance analysis for L_2 kernel classification. - JooSeuk Kim; Clayton D. Scott (NIPS, 2008)

Group-based Query Learning for rapid diagnosis in time-critical situations - Gowtham Bellala; Suresh K. Bhavnani; Clayton Scott (CoRR, 2009)

Novelty detection: Unlabeled data definitely help. - Clayton Scott; Gilles Blanchard (AISTATS, 2009)

Extensions of Generalized Binary Search to Group Identification and Exponential Costs. - Gowtham Bellala; Suresh K. Bhavnani; Clayton Scott (NIPS, 2010)

Query Learning with Exponential Query Costs - Gowtham Bellala; Suresh K. Bhavnani; Clayton Scott (CoRR, 2010)

Semi-Supervised Novelty Detection. - Gilles Blanchard; Gyemin Lee; Clayton Scott (J. Mach. Learn. Res., 2010)

Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification. - Mark A. Davenport; Richard G. Baraniuk; Clayton D. Scott (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

L2 Kernel Classification. - JooSeuk Kim; Clayton D. Scott (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Nested support vector machines. - Gyemin Lee; Clayton Scott (IEEE Trans. Signal Process., 2010)

Benefits of position-sensitive detectors for radioactive source detection. - Daniel J. Lingenfelter; Jeffrey A. Fessler; Clayton D. Scott; Zhong He (IEEE Trans. Signal Process., 2010)

On the Robustness of Kernel Density M-Estimators. - JooSeuk Kim; Clayton D. Scott (ICML, 2011)

Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs. - Clayton Scott (ICML, 2011)

Generalizing from Several Related Classification Tasks to a New Unlabeled Sample. - Gilles Blanchard; Gyemin Lee; Clayton Scott (NIPS, 2011)

Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks. - Gowtham Bellala; Jason Stanley; Clayton Scott; Suresh K. Bhavnani (UAI, 2011)

Statistical file matching of flow cytometry data. - Gyemin Lee; William Finn; Clayton Scott (J. Biomed. Informatics, 2011)

Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach. - Gowtham Bellala; Suresh K. Bhavnani; Clayton Scott (AISTATS, 2011)

Asymptotic Source Detection Performance of Gamma-Ray Imaging Systems Under Model Mismatch. - Daniel J. Lingenfelter; Jeffrey A. Fessler; Clayton D. Scott; Zhong He (IEEE Trans. Signal Process., 2011)

Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty. - Takanori Watanabe; Clayton D. Scott (WBIR, 2012)

EM algorithms for multivariate Gaussian mixture models with truncated and censored data. - Gyemin Lee; Clayton Scott (Comput. Stat. Data Anal., 2012)

Robust kernel density estimation. - JooSeuk Kim; Clayton D. Scott (J. Mach. Learn. Res., 2012)

Transfer Learning for Auto-gating of Flow Cytometry Data. - Gyemin Lee; Lloyd Stoolman; Clayton Scott (ICML Unsupervised and Transfer Learning, 2012)

Group-Based Active Query Selection for Rapid Diagnosis in Time-Critical Situations. - Gowtham Bellala; Suresh K. Bhavnani; Clayton Scott (IEEE Trans. Inf. Theory, 2012)

Classification with Asymmetric Label Noise: Consistency and Maximal Denoising. - Clayton Scott; Gilles Blanchard; Gregory Handy (COLT, 2013)

Consistency of Robust Kernel Density Estimators. - Robert A. Vandermeulen; Clayton D. Scott (COLT, 2013)

Semi-supervised Classification with Anomaly Rejection. - Tyler Sanderson; Clayton Scott (CoRR, 2013)

Classification with Asymmetric Label Noise: Consistency and Maximal Denoising - Clayton Scott; Gilles Blanchard; Gregory Handy; Sara Pozzi; Marek Flaska (CoRR, 2013)

Distributed effects of methylphenidate on the network structure of the resting brain: A connectomic pattern classification analysis. - Chandra Sekhar Sripada; Daniel Kessler; Robert C. Welsh; Michael Angstadt; Israel Liberzon; K. Luan Phan; Clayton D. Scott (NeuroImage, 2013)

A Rank-Based Approach to Active Diagnosis. - Gowtham Bellala; Jason Stanley; Suresh K. Bhavnani; Clayton Scott (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

Decontamination of Mutually Contaminated Models. - Gilles Blanchard; Clayton Scott (AISTATS, 2014)

Class Proportion Estimation with Application to Multiclass Anomaly Rejection. - Tyler Sanderson; Clayton Scott (AISTATS, 2014)

Scalable sparse approximation of a sample mean. - Efren Cruz Cortes; Clayton Scott (ICASSP, 2014)

Scalable fused Lasso SVM for connectome-based disease prediction. - Takanori Watanabe; Clayton D. Scott; Daniel Kessler; Michael Angstadt; Chandra Sekhar Sripada (ICASSP, 2014)

Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space. - Robert A. Vandermeulen; Clayton D. Scott (NIPS, 2014)

Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. - Takanori Watanabe; Daniel Kessler; Clayton D. Scott; Michael Angstadt; Chandra Sekhar Sripada (NeuroImage, 2014)

A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels. - Clayton Scott (AISTATS, 2015)

Optimal change point detection in Gaussian processes. - Hossein Keshavarz; Clayton Scott; XuanLong Nguyen (CoRR, 2015)

On The Identifiability of Mixture Models from Grouped Samples. - Robert A. Vandermeulen; Clayton D. Scott (CoRR, 2015)

Mixture Proportion Estimation via Kernel Embeddings of Distributions. - Harish G. Ramaswamy; Clayton Scott; Ambuj Tewari (ICML, 2016)

Mixture Proportion Estimation via Kernel Embedding of Distributions. - Harish G. Ramaswamy; Clayton Scott; Ambuj Tewari (CoRR, 2016)

On the consistency of inversion-free parameter estimation for Gaussian random fields. - Hossein Keshavarz; Clayton Scott; XuanLong Nguyen (J. Multivar. Anal., 2016)

Multi-Task Learning for Contextual Bandits. - Aniket Anand Deshmukh; Ürün Dogan; Clayton Scott (NIPS, 2017)

Consistent Kernel Density Estimation with Non-Vanishing Bandwidth. - Efrén Cruz Cortés; Clayton Scott (CoRR, 2017)

Adaptive Questionnaires for Direct Identification of Optimal Product Design. - Max Yi Ren; Clayton Scott (CoRR, 2017)

Sparse Approximation of a Kernel Mean. - Efren Cruz Cortes; Clayton Scott (IEEE Trans. Signal Process., 2017)

Nonparametric Preference Completion. - Julian Katz-Samuels; Clayton Scott (AISTATS, 2018)

Feasible Arm Identification. - Julian Katz-Samuels; Clayton Scott (ICML, 2018)

Simple Regret Minimization for Contextual Bandits. - Aniket Anand Deshmukh; Srinagesh Sharma; James W. Cutler; Mark Moldwin; Clayton Scott (CoRR, 2018)

Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels. - Gopal Nataraj; Jon-Fredrik Nielsen; Clayton D. Scott; Jeffrey A. Fessler (IEEE Trans. Medical Imaging, 2018)

Top Feasible Arm Identification. - Julian Katz-Samuels; Clayton Scott (AISTATS, 2019)

A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation. - Clayton Scott (ALT, 2019)

Supervised Principal Component Analysis Via Manifold Optimization. - Alexander Ritchie; Clayton D. Scott; Laura Balzano; Daniel Kessler; Chandra Sekhar Sripada (DSW, 2019)

A Generalization Error Bound for Multi-class Domain Generalization. - Aniket Anand Deshmukh; Yunwen Lei; Srinagesh Sharma; Ürün Dogan; James W. Cutler; Clayton Scott (CoRR, 2019)

PAC Reinforcement Learning without Real-World Feedback. - Yuren Zhong; Aniket Anand Deshmukh; Clayton Scott (CoRR, 2019)

Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions. - Clayton Scott; Jianxin Zhang (CoRR, 2019)

Decontamination of Mutual Contamination Models. - Julian Katz-Samuels; Gilles Blanchard; Clayton Scott (J. Mach. Learn. Res., 2019)

Calibrated Surrogate Losses for Adversarially Robust Classification. - Han Bao 002; Clayton Scott; Masashi Sugiyama (COLT, 2020)

Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations. - Alexander Ritchie; Robert A. Vandermeulen; Clayton D. Scott (NeurIPS, 2020)

Learning from Label Proportions: A Mutual Contamination Framework. - Clayton Scott; Jianxin Zhang (NeurIPS, 2020)

Weston-Watkins Hinge Loss and Ordered Partitions. - Yutong Wang 002; Clayton Scott (NeurIPS, 2020)

Supervised PCA: A Multiobjective Approach. - Alexander Ritchie; Laura Balzano; Clayton D. Scott (CoRR, 2020)

An exact solver for the Weston-Watkins SVM subproblem. - Yutong Wang 002; Clayton Scott (ICML, 2021)

Domain Generalization by Marginal Transfer Learning. - Gilles Blanchard; Aniket Anand Deshmukh; Ürün Dogan; Gyemin Lee; Clayton Scott (J. Mach. Learn. Res., 2021)

VC dimension of partially quantized neural networks in the overparametrized regime. - Yutong Wang 002; Clayton Scott (ICLR, 2022)

Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel. - Yutong Wang 002; Clayton Scott (NeurIPS, 2022)

Learning from Label Proportions by Learning with Label Noise. - Jianxin Zhang; Yutong Wang 002; Clayton Scott (NeurIPS, 2022)

On Classification-Calibration of Gamma-Phi Losses. - Yutong Wang 002; Clayton Scott (COLT, 2023)

Mixture Proportion Estimation Beyond Irreducibility. - Yilun Zhu 002; Aaron Fjeldsted; Darren Holland; George Landon; Azaree Lintereur; Clayton Scott (ICML, 2023)

Label Embedding by Johnson-Lindenstrauss Matrices. - Jianxin Zhang; Clayton Scott (CoRR, 2023)

Testing the Feasibility of Linear Programs with Bandit Feedback. - Aditya Gangrade; Aditya Gopalan; Venkatesh Saligrama; Clayton Scott (ICML, 2024)

Joint Optimization of Piecewise Linear Ensembles. - Matt Raymond; Angela Violi; Clayton Scott (MLSP, 2024)

The Implicit Bias of Gradient Descent on Separable Multiclass Data. - Hrithik Ravi; Clayton Scott; Daniel Soudry; Yutong Wang 002 (NeurIPS, 2024)

Label Noise: Ignorance Is Bliss. - Yilun Zhu 002; Jianxin Zhang; Aditya Gangrade; Clayton Scott (NeurIPS, 2024)

Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets. - Matt Raymond; Jacob Charles Saldinger; Paolo Elvati; Clayton Scott; Angela Violi (CoRR, 2024)

Unified Binary and Multiclass Margin-Based Classification. - Yutong Wang 002; Clayton Scott (J. Mach. Learn. Res., 2024)