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Clayton Scott
Clayton D. Scott
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- affiliation: University of Michigan, Ann Arbor, USA
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2020 – today
- 2024
- [j26]Yutong Wang, Clayton Scott:
Unified Binary and Multiclass Margin-Based Classification. J. Mach. Learn. Res. 25: 143:1-143:51 (2024) - [c51]Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott:
Testing the Feasibility of Linear Programs with Bandit Feedback. ICML 2024 - [c50]Matt Raymond, Angela Violi, Clayton Scott:
Joint Optimization of Piecewise Linear Ensembles. MLSP 2024: 1-6 - [i38]Matt Raymond, Jacob Charles Saldinger, Paolo Elvati
, Clayton Scott, Angela Violi:
Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets. CoRR abs/2403.14466 (2024) - [i37]Matt Raymond, Angela Violi, Clayton Scott:
Joint Optimization of Piecewise Linear Ensembles. CoRR abs/2405.00303 (2024) - [i36]Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott:
Testing the Feasibility of Linear Programs with Bandit Feedback. CoRR abs/2406.15648 (2024) - [i35]Yilun Zhu, Jianxin Zhang, Aditya Gangrade, Clayton Scott:
Label Noise: Ignorance Is Bliss. CoRR abs/2411.00079 (2024) - [i34]Hrithik Ravi, Clayton Scott, Daniel Soudry, Yutong Wang:
The Implicit Bias of Gradient Descent on Separable Multiclass Data. CoRR abs/2411.01350 (2024) - 2023
- [c49]Yutong Wang, Clayton Scott:
On Classification-Calibration of Gamma-Phi Losses. COLT 2023: 4929-4951 - [c48]Yilun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, Clayton Scott:
Mixture Proportion Estimation Beyond Irreducibility. ICML 2023: 42962-42982 - [i33]Yutong Wang, Clayton D. Scott:
On Classification-Calibration of Gamma-Phi Losses. CoRR abs/2302.07321 (2023) - [i32]Jianxin Zhang, Clayton Scott:
Label Embedding by Johnson-Lindenstrauss Matrices. CoRR abs/2305.19470 (2023) - [i31]Yilun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, Clayton Scott:
Mixture Proportion Estimation Beyond Irreducibility. CoRR abs/2306.01253 (2023) - [i30]Yutong Wang, Clayton Scott:
Unified Binary and Multiclass Margin-Based Classification. CoRR abs/2311.17778 (2023) - 2022
- [c47]Yutong Wang, Clayton Scott:
VC dimension of partially quantized neural networks in the overparametrized regime. ICLR 2022 - [c46]Yutong Wang, Clayton Scott:
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel. NeurIPS 2022 - [c45]Jianxin Zhang, Yutong Wang, Clayton Scott:
Learning from Label Proportions by Learning with Label Noise. NeurIPS 2022 - [i29]Jianxin Zhang, Yutong Wang, Clayton Scott:
Learning from Label Proportions by Learning with Label Noise. CoRR abs/2203.02496 (2022) - [i28]Yutong Wang, Clayton D. Scott:
Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel. CoRR abs/2205.09342 (2022) - 2021
- [j25]Gilles Blanchard, Aniket Anand Deshmukh, Ürün Dogan, Gyemin Lee, Clayton Scott:
Domain Generalization by Marginal Transfer Learning. J. Mach. Learn. Res. 22: 2:1-2:55 (2021) - [c44]Yutong Wang, Clayton Scott:
An exact solver for the Weston-Watkins SVM subproblem. ICML 2021: 10894-10904 - [i27]Yutong Wang, Clayton D. Scott:
An exact solver for the Weston-Watkins SVM subproblem. CoRR abs/2102.05640 (2021) - [i26]Yutong Wang, Clayton D. Scott:
VC dimension of partially quantized neural networks in the overparametrized regime. CoRR abs/2110.02456 (2021) - 2020
- [c43]Han Bao, Clayton Scott, Masashi Sugiyama:
Calibrated Surrogate Losses for Adversarially Robust Classification. COLT 2020: 408-451 - [c42]Alexander Ritchie, Robert A. Vandermeulen, Clayton D. Scott:
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations. NeurIPS 2020 - [c41]Clayton Scott, Jianxin Zhang:
Learning from Label Proportions: A Mutual Contamination Framework. NeurIPS 2020 - [c40]Yutong Wang, Clayton Scott:
Weston-Watkins Hinge Loss and Ordered Partitions. NeurIPS 2020 - [i25]Han Bao, Clayton Scott, Masashi Sugiyama:
Calibrated Surrogate Losses for Adversarially Robust Classification. CoRR abs/2005.13748 (2020) - [i24]Clayton Scott, Jianxin Zhang:
Learning from Label Proportions: A Mutual Contamination Framework. CoRR abs/2006.07330 (2020) - [i23]Yutong Wang, Clayton D. Scott:
Weston-Watkins Hinge Loss and Ordered Partitions. CoRR abs/2006.07346 (2020) - [i22]Alexander Ritchie, Robert A. Vandermeulen, Clayton D. Scott:
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations. CoRR abs/2006.07459 (2020) - [i21]Alexander Ritchie, Laura Balzano, Clayton D. Scott:
Supervised PCA: A Multiobjective Approach. CoRR abs/2011.05309 (2020)
2010 – 2019
- 2019
- [j24]Julian Katz-Samuels, Gilles Blanchard, Clayton Scott:
Decontamination of Mutual Contamination Models. J. Mach. Learn. Res. 20: 41:1-41:57 (2019) - [c39]Julian Katz-Samuels, Clayton Scott:
Top Feasible Arm Identification. AISTATS 2019: 1593-1601 - [c38]Clayton Scott:
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation. ALT 2019: 738-761 - [c37]Alexander Ritchie, Clayton D. Scott, Laura Balzano, Daniel Kessler, Chandra Sekhar Sripada:
Supervised Principal Component Analysis Via Manifold Optimization. DSW 2019: 6-10 - [i20]Aniket Anand Deshmukh, Yunwen Lei, Srinagesh Sharma, Ürün Dogan, James W. Cutler, Clayton Scott:
A Generalization Error Bound for Multi-class Domain Generalization. CoRR abs/1905.10392 (2019) - [i19]Yuren Zhong, Aniket Anand Deshmukh, Clayton Scott:
PAC Reinforcement Learning without Real-World Feedback. CoRR abs/1909.10449 (2019) - [i18]Clayton Scott, Jianxin Zhang:
Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions. CoRR abs/1910.04665 (2019) - 2018
- [j23]Gopal Nataraj
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels. IEEE Trans. Medical Imaging 37(9): 2103-2114 (2018) - [c36]Julian Katz-Samuels, Clayton Scott:
Nonparametric Preference Completion. AISTATS 2018: 632-641 - [c35]Julian Katz-Samuels, Clayton Scott:
Feasible Arm Identification. ICML 2018: 2540-2548 - [i17]Clayton Scott:
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation. CoRR abs/1810.01545 (2018) - [i16]Aniket Anand Deshmukh, Srinagesh Sharma, James W. Cutler, Mark Moldwin, Clayton Scott:
Simple Regret Minimization for Contextual Bandits. CoRR abs/1810.07371 (2018) - 2017
- [j22]Efren Cruz Cortes, Clayton Scott:
Sparse Approximation of a Kernel Mean. IEEE Trans. Signal Process. 65(5): 1310-1323 (2017) - [c34]Aniket Anand Deshmukh, Ürün Dogan, Clayton Scott:
Multi-Task Learning for Contextual Bandits. NIPS 2017: 4848-4856 - [i15]Max Yi Ren, Clayton Scott:
Adaptive Questionnaires for Direct Identification of Optimal Product Design. CoRR abs/1701.01231 (2017) - [i14]Aniket Anand Deshmukh, Ürün Dogan, Clayton Scott:
Multi-Task Learning for Contextual Bandits. CoRR abs/1705.08618 (2017) - [i13]Julian Katz-Samuels, Clayton Scott:
Nonparametric Preference Completion. CoRR abs/1705.08621 (2017) - [i12]Efrén Cruz Cortés
Consistent Kernel Density Estimation with Non-Vanishing Bandwidth. CoRR abs/1705.08921 (2017) - 2016
- [j21]Hossein Keshavarz
On the consistency of inversion-free parameter estimation for Gaussian random fields. J. Multivar. Anal. 150: 245-266 (2016) - [c33]Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari:
Mixture Proportion Estimation via Kernel Embeddings of Distributions. ICML 2016: 2052-2060 - [i11]Hossein Keshavarz, Clayton Scott, XuanLong Nguyen:
On the consistency of inversion-free parameter estimation for Gaussian random fields. CoRR abs/1601.03822 (2016) - [i10]Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari:
Mixture Proportion Estimation via Kernel Embedding of Distributions. CoRR abs/1603.02501 (2016) - 2015
- [c32]Clayton Scott:
A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels. AISTATS 2015 - [i9]Robert A. Vandermeulen, Clayton D. Scott:
On The Identifiability of Mixture Models from Grouped Samples. CoRR abs/1502.06644 (2015) - [i8]Efren Cruz Cortes
Sparse Approximation of a Kernel Mean. CoRR abs/1503.00323 (2015) - [i7]Hossein Keshavarz, Clayton Scott, XuanLong Nguyen:
Optimal change point detection in Gaussian processes. CoRR abs/1506.01338 (2015) - 2014
- [j20]Takanori Watanabe, Daniel Kessler
Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. NeuroImage 96: 183-202 (2014) - [c31]Gilles Blanchard, Clayton Scott:
Decontamination of Mutually Contaminated Models. AISTATS 2014: 1-9 - [c30]Tyler Sanderson, Clayton Scott:
Class Proportion Estimation with Application to Multiclass Anomaly Rejection. AISTATS 2014: 850-858 - [c29]Efren Cruz Cortes, Clayton Scott:
Scalable sparse approximation of a sample mean. ICASSP 2014: 5237-5241 - [c28]Takanori Watanabe, Clayton D. Scott, Daniel Kessler
Scalable fused Lasso SVM for connectome-based disease prediction. ICASSP 2014: 5989-5993 - [c27]Robert A. Vandermeulen, Clayton D. Scott:
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space. NIPS 2014: 433-441 - 2013
- [j19]Chandra Sekhar Sripada, Daniel Kessler
Distributed effects of methylphenidate on the network structure of the resting brain: A connectomic pattern classification analysis. NeuroImage 81: 213-221 (2013) - [j18]Gowtham Bellala, Jason Stanley, Suresh K. Bhavnani, Clayton Scott:
A Rank-Based Approach to Active Diagnosis. IEEE Trans. Pattern Anal. Mach. Intell. 35(9): 2078-2090 (2013) - [c26]Clayton Scott, Gilles Blanchard, Gregory Handy:
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising. COLT 2013: 489-511 - [c25]Robert A. Vandermeulen, Clayton D. Scott:
Consistency of Robust Kernel Density Estimators. COLT 2013: 568-591 - [i6]Clayton Scott, Gilles Blanchard, Gregory Handy, Sara Pozzi, Marek Flaska:
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising. CoRR abs/1303.1208 (2013) - [i5]Tyler Sanderson, Clayton Scott:
Semi-supervised Classification with Anomaly Rejection. CoRR abs/1306.5056 (2013) - 2012
- [j17]Gyemin Lee
EM algorithms for multivariate Gaussian mixture models with truncated and censored data. Comput. Stat. Data Anal. 56(9): 2816-2829 (2012) - [j16]JooSeuk Kim, Clayton D. Scott:
Robust kernel density estimation. J. Mach. Learn. Res. 13: 2529-2565 (2012) - [j15]Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
Group-Based Active Query Selection for Rapid Diagnosis in Time-Critical Situations. IEEE Trans. Inf. Theory 58(1): 459-478 (2012) - [c24]Takanori Watanabe, Clayton D. Scott:
Spatial Confidence Regions for Quantifying and Visualizing Registration Uncertainty. WBIR 2012: 120-130 - [c23]Gyemin Lee, Lloyd Stoolman, Clayton Scott:
Transfer Learning for Auto-gating of Flow Cytometry Data. ICML Unsupervised and Transfer Learning 2012: 155-166 - [i4]Gowtham Bellala, Jason Stanley, Clayton Scott, Suresh K. Bhavnani:
Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks. CoRR abs/1202.3701 (2012) - 2011
- [j14]Gyemin Lee
Statistical file matching of flow cytometry data. J. Biomed. Informatics 44(4): 663-676 (2011) - [j13]Daniel J. Lingenfelter, Jeffrey A. Fessler, Clayton D. Scott, Zhong He:
Asymptotic Source Detection Performance of Gamma-Ray Imaging Systems Under Model Mismatch. IEEE Trans. Signal Process. 59(11): 5141-5151 (2011) - [c22]Clayton Scott:
Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs. ICML 2011: 153-160 - [c21]JooSeuk Kim, Clayton D. Scott:
On the Robustness of Kernel Density M-Estimators. ICML 2011: 697-704 - [c20]Gilles Blanchard, Gyemin Lee, Clayton Scott:
Generalizing from Several Related Classification Tasks to a New Unlabeled Sample. NIPS 2011: 2178-2186 - [c19]Gowtham Bellala, Jason Stanley, Clayton Scott, Suresh K. Bhavnani:
Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks. UAI 2011: 35-42 - [c18]Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach. AISTATS 2011: 155-163 - [i3]JooSeuk Kim, Clayton D. Scott:
Robust Kernel Density Estimation. CoRR abs/1107.3133 (2011) - 2010
- [j12]Gilles Blanchard, Gyemin Lee, Clayton Scott:
Semi-Supervised Novelty Detection. J. Mach. Learn. Res. 11: 2973-3009 (2010) - [j11]JooSeuk Kim, Clayton D. Scott:
L2 Kernel Classification. IEEE Trans. Pattern Anal. Mach. Intell. 32(10): 1822-1831 (2010) - [j10]Mark A. Davenport, Richard G. Baraniuk, Clayton D. Scott:
Tuning Support Vector Machines for Minimax and Neyman-Pearson Classification. IEEE Trans. Pattern Anal. Mach. Intell. 32(10): 1888-1898 (2010) - [j9]Gyemin Lee
Nested support vector machines. IEEE Trans. Signal Process. 58(3): 1648-1660 (2010) - [j8]Daniel J. Lingenfelter, Jeffrey A. Fessler
Benefits of position-sensitive detectors for radioactive source detection. IEEE Trans. Signal Process. 58(9): 4473-4483 (2010) - [c17]Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
Extensions of Generalized Binary Search to Group Identification and Exponential Costs. NIPS 2010: 154-162 - [i2]Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
Query Learning with Exponential Query Costs. CoRR abs/1002.4019 (2010)
2000 – 2009
- 2009
- [c16]Clayton Scott, Gilles Blanchard:
Novelty detection: Unlabeled data definitely help. AISTATS 2009: 464-471 - [i1]Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott:
Group-based Query Learning for rapid diagnosis in time-critical situations. CoRR abs/0911.4511 (2009) - 2008
- [c15]Aarti Singh, Robert D. Nowak, Clayton D. Scott:
Adaptive Hausdorff Estimation of Density Level Sets. COLT 2008: 491-502 - [c14]Frederic Thouin, Mark Coates, Brian Eriksson, Robert D. Nowak, Clayton D. Scott:
Learning to satisfy. ICASSP 2008: 1981-1984 - [c13]Gyemin Lee
Nested support vector machines. ICASSP 2008: 1985-1988 - [c12]JooSeuk Kim, Clayton D. Scott:
Robust kernel density estimation. ICASSP 2008: 3381-3384 - [c11]Parminder Chhabra, Clayton D. Scott, Eric D. Kolaczyk, Mark Crovella:
Distributed Spatial Anomaly Detection. INFOCOM 2008: 1705-1713 - [c10]JooSeuk Kim, Clayton D. Scott:
Performance analysis for L_2 kernel classification. NIPS 2008: 833-840 - 2007
- [j7]Clayton Scott:
Performance Measures for Neyman-Pearson Classification. IEEE Trans. Inf. Theory 53(8): 2852-2863 (2007) - [j6]Clayton D. Scott, Mark A. Davenport:
Regression Level Set Estimation Via Cost-Sensitive Classification. IEEE Trans. Signal Process. 55(6-1): 2752-2757 (2007) - [c9]Gyemin Lee
The One Class Support Vector Machine Solution Path. ICASSP (2) 2007: 521-524 - 2006
- [j5]Clayton D. Scott, Robert D. Nowak:
Learning Minimum Volume Sets. J. Mach. Learn. Res. 7: 665-704 (2006) - [j4]Clayton D. Scott, Robert D. Nowak:
Robust Contour Matching Via the Order-Preserving Assignment Problem. IEEE Trans. Image Process. 15(7): 1831-1838 (2006) - [j3]Clayton D. Scott, Robert D. Nowak:
Minimax-optimal classification with dyadic decision trees. IEEE Trans. Inf. Theory 52(4): 1335-1353 (2006) - [c8]Mark A. Davenport, Richard G. Baraniuk, Clayton D. Scott:
Controlling False Alarms With Support Vector Machines. ICASSP (5) 2006: 589-592 - 2005
- [j2]Clayton D. Scott, Robert D. Nowak:
A Neyman-Pearson approach to statistical learning. IEEE Trans. Inf. Theory 51(11): 3806-3819 (2005) - [c7]Clayton D. Scott, Robert D. Nowak:
Learning Minimum Volume Sets. NIPS 2005: 1209-1216 - 2004
- [j1]Clayton D. Scott, Robert D. Nowak:
TEMPLAR: a wavelet-based framework for pattern learning and analysis. IEEE Trans. Signal Process. 52(8): 2264-2274 (2004) - [c6]Clayton D. Scott, Robert D. Nowak:
On the Adaptive Properties of Decision Trees. NIPS 2004: 1225-1232 - 2003
- [c5]Clayton D. Scott, Rebecca M. Willett, Robert D. Nowak:
CORT: classification or regression trees. ICASSP (6) 2003: 153-156 - [c4]Clayton D. Scott, Robert D. Nowak:
Near-Minimax Optimal Classification with Dyadic Classification Trees. NIPS 2003: 1117-1124 - 2002
- [c3]Clayton D. Scott, Robert D. Nowak:
Dyadic Classification Trees via Structural Risk Minimization. NIPS 2002: 359-366 - 2000
- [c2]Clayton D. Scott, Robert D. Nowak:
Pattern Extraction and Synthesis Using a Hierarchical Wavelet-Based Framework. ICIP 2000: 383-386 - [c1]Clayton D. Scott, Robert D. Nowak:
A Novel Hierarchical Wavelet-Based Framework for Pattern Analysis and Synthesis. SSIAI 2000: 242-246
Coauthor Index
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