Clayton Scott - RankMe

81 papers A* 17A 8B 1Misc 2Journal 47Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Domain Generalization: A Tale of Two ERMs. CoRR Yilun Zhu, Naihao Deng, Naichen Shi, Aditya Gangrade, Clayton Scott
2025	J	`jnl`	Estimating Phase From Observed Trajectories Using the Temporal 1-Form. Neural Comput. Simon Wilshin, Matthew D. Kvalheim, Clayton Scott, Shai Revzen
2025	A*	`conf`	Feasible Action Search for Bandit Linear Programs via Thompson Sampling. ICML Aditya Gangrade, Aldo Pacchiano, Clayton Scott, Venkatesh Saligrama
2025	J	`jnl`	Flow Straight and Fast in Hilbert Space: Functional Rectified Flow. CoRR Jianxin Zhang, Clayton Scott
2025	J	`jnl`	Label Embedding via Low-Coherence Matrices. Trans. Mach. Learn. Res. Jianxin Zhang, Clayton Scott
2025	J	`jnl`	Semantic Editing with Coupled Stochastic Differential Equations. CoRR Jianxin Zhang, Clayton Scott
2024	—	`conf`	Joint Optimization of Piecewise Linear Ensembles. MLSP Matt Raymond, Angela Violi, Clayton Scott
2024	J	`jnl`	Joint Optimization of Piecewise Linear Ensembles. CoRR Matt Raymond, Angela Violi, Clayton Scott
2024	A*	`conf`	Label Noise: Ignorance Is Bliss. NeurIPS Yilun Zhu, Jianxin Zhang, Aditya Gangrade, Clayton Scott
2024	J	`jnl`	Label Noise: Ignorance Is Bliss. CoRR Yilun Zhu, Jianxin Zhang, Aditya Gangrade, Clayton Scott
2024	A*	`conf`	Testing the Feasibility of Linear Programs with Bandit Feedback. ICML Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott
2024	J	`jnl`	Testing the Feasibility of Linear Programs with Bandit Feedback. CoRR Aditya Gangrade, Aditya Gopalan, Venkatesh Saligrama, Clayton Scott
2024	A*	`conf`	The Implicit Bias of Gradient Descent on Separable Multiclass Data. NeurIPS Hrithik Ravi, Clayton Scott, Daniel Soudry, Yutong Wang
2024	J	`jnl`	The Implicit Bias of Gradient Descent on Separable Multiclass Data. CoRR Hrithik Ravi, Clayton Scott, Daniel Soudry, Yutong Wang
2024	J	`jnl`	Unified Binary and Multiclass Margin-Based Classification. J. Mach. Learn. Res. Yutong Wang, Clayton Scott
2024	J	`jnl`	Universal Feature Selection for Simultaneous Interpretability of Multitask Datasets. CoRR Matt Raymond, Jacob Charles Saldinger, Paolo Elvati, Clayton Scott, Angela Violi
2023	J	`jnl`	Label Embedding by Johnson-Lindenstrauss Matrices. CoRR Jianxin Zhang, Clayton Scott
2023	A*	`conf`	Mixture Proportion Estimation Beyond Irreducibility. ICML Yilun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, Clayton Scott
2023	J	`jnl`	Mixture Proportion Estimation Beyond Irreducibility. CoRR Yilun Zhu, Aaron Fjeldsted, Darren Holland, George Landon, Azaree Lintereur, Clayton Scott
2023	A*	`conf`	On Classification-Calibration of Gamma-Phi Losses. COLT Yutong Wang, Clayton Scott
2023	J	`jnl`	Unified Binary and Multiclass Margin-Based Classification. CoRR Yutong Wang, Clayton Scott
2022	A*	`conf`	Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel. NeurIPS Yutong Wang, Clayton Scott
2022	A*	`conf`	Learning from Label Proportions by Learning with Label Noise. NeurIPS Jianxin Zhang, Yutong Wang, Clayton Scott
2022	J	`jnl`	Learning from Label Proportions by Learning with Label Noise. CoRR Jianxin Zhang, Yutong Wang, Clayton Scott
2022	A*	`conf`	VC dimension of partially quantized neural networks in the overparametrized regime. ICLR Yutong Wang, Clayton Scott
2021	A*	`conf`	An exact solver for the Weston-Watkins SVM subproblem. ICML Yutong Wang, Clayton Scott
2021	J	`jnl`	Domain Generalization by Marginal Transfer Learning. J. Mach. Learn. Res. Gilles Blanchard, Aniket Anand Deshmukh, Ürün Dogan, Gyemin Lee, Clayton Scott
2020	A*	`conf`	Calibrated Surrogate Losses for Adversarially Robust Classification. COLT Han Bao, Clayton Scott, Masashi Sugiyama
2020	J	`jnl`	Calibrated Surrogate Losses for Adversarially Robust Classification. CoRR Han Bao, Clayton Scott, Masashi Sugiyama
2020	A*	`conf`	Learning from Label Proportions: A Mutual Contamination Framework. NeurIPS Clayton Scott, Jianxin Zhang
2020	J	`jnl`	Learning from Label Proportions: A Mutual Contamination Framework. CoRR Clayton Scott, Jianxin Zhang
2020	A*	`conf`	Weston-Watkins Hinge Loss and Ordered Partitions. NeurIPS Yutong Wang, Clayton Scott
2019	J	`jnl`	A Generalization Error Bound for Multi-class Domain Generalization. CoRR Aniket Anand Deshmukh, Yunwen Lei, Srinagesh Sharma, Ürün Dogan, James W. Cutler, Clayton Scott
2019	B	`conf`	A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation. ALT Clayton Scott
2019	J	`jnl`	Decontamination of Mutual Contamination Models. J. Mach. Learn. Res. Julian Katz-Samuels, Gilles Blanchard, Clayton Scott
2019	J	`jnl`	Learning from Multiple Corrupted Sources, with Application to Learning from Label Proportions. CoRR Clayton Scott, Jianxin Zhang
2019	J	`jnl`	PAC Reinforcement Learning without Real-World Feedback. CoRR Yuren Zhong, Aniket Anand Deshmukh, Clayton Scott
2019	A	`conf`	Top Feasible Arm Identification. AISTATS Julian Katz-Samuels, Clayton Scott
2018	J	`jnl`	A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation. CoRR Clayton Scott
2018	A*	`conf`	Feasible Arm Identification. ICML Julian Katz-Samuels, Clayton Scott
2018	A	`conf`	Nonparametric Preference Completion. AISTATS Julian Katz-Samuels, Clayton Scott
2018	J	`jnl`	Simple Regret Minimization for Contextual Bandits. CoRR Aniket Anand Deshmukh, Srinagesh Sharma, James W. Cutler, Mark Moldwin, Clayton Scott
2017	J	`jnl`	Adaptive Questionnaires for Direct Identification of Optimal Product Design. CoRR Max Yi Ren, Clayton Scott
2017	J	`jnl`	Consistent Kernel Density Estimation with Non-Vanishing Bandwidth. CoRR Efrén Cruz Cortés, Clayton Scott
2017	—	`conf`	Multi-Task Learning for Contextual Bandits. NIPS Aniket Anand Deshmukh, Ürün Dogan, Clayton Scott
2017	J	`jnl`	Multi-Task Learning for Contextual Bandits. CoRR Aniket Anand Deshmukh, Ürün Dogan, Clayton Scott
2017	J	`jnl`	Nonparametric Preference Completion. CoRR Julian Katz-Samuels, Clayton Scott
2017	J	`jnl`	Sparse Approximation of a Kernel Mean. IEEE Trans. Signal Process. Efren Cruz Cortes, Clayton Scott
2016	J	`jnl`	Mixture Proportion Estimation via Kernel Embedding of Distributions. CoRR Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari
2016	A*	`conf`	Mixture Proportion Estimation via Kernel Embeddings of Distributions. ICML Harish G. Ramaswamy, Clayton Scott, Ambuj Tewari
2016	J	`jnl`	On the consistency of inversion-free parameter estimation for Gaussian random fields. CoRR Hossein Keshavarz, Clayton Scott, XuanLong Nguyen
2016	J	`jnl`	On the consistency of inversion-free parameter estimation for Gaussian random fields. J. Multivar. Anal. Hossein Keshavarz, Clayton Scott, XuanLong Nguyen
2015	A	`conf`	A Rate of Convergence for Mixture Proportion Estimation, with Application to Learning from Noisy Labels. AISTATS Clayton Scott
2015	J	`jnl`	Optimal change point detection in Gaussian processes. CoRR Hossein Keshavarz, Clayton Scott, XuanLong Nguyen
2015	J	`jnl`	Sparse Approximation of a Kernel Mean. CoRR Efren Cruz Cortes, Clayton Scott
2014	A	`conf`	Class Proportion Estimation with Application to Multiclass Anomaly Rejection. AISTATS Tyler Sanderson, Clayton Scott
2014	A	`conf`	Decontamination of Mutually Contaminated Models. AISTATS Gilles Blanchard, Clayton Scott
2014	Misc	`conf`	Scalable sparse approximation of a sample mean. ICASSP Efren Cruz Cortes, Clayton Scott
2013	J	`jnl`	A Rank-Based Approach to Active Diagnosis. IEEE Trans. Pattern Anal. Mach. Intell. Gowtham Bellala, Jason Stanley, Suresh K. Bhavnani, Clayton Scott
2013	J	`jnl`	Classification with Asymmetric Label Noise: Consistency and Maximal Denoising CoRR Clayton Scott, Gilles Blanchard, Gregory Handy, Sara Pozzi, Marek Flaska
2013	A*	`conf`	Classification with Asymmetric Label Noise: Consistency and Maximal Denoising. COLT Clayton Scott, Gilles Blanchard, Gregory Handy
2013	J	`jnl`	Semi-supervised Classification with Anomaly Rejection. CoRR Tyler Sanderson, Clayton Scott
2012	J	`jnl`	Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks CoRR Gowtham Bellala, Jason Stanley, Clayton Scott, Suresh K. Bhavnani
2012	J	`jnl`	EM algorithms for multivariate Gaussian mixture models with truncated and censored data. Comput. Stat. Data Anal. Gyemin Lee, Clayton Scott
2012	J	`jnl`	Group-Based Active Query Selection for Rapid Diagnosis in Time-Critical Situations. IEEE Trans. Inf. Theory Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott
2012	—	`conf`	Transfer Learning for Auto-gating of Flow Cytometry Data. ICML Unsupervised and Transfer Learning Gyemin Lee, Lloyd Stoolman, Clayton Scott
2011	A	`conf`	Active Diagnosis under Persistent Noise with Unknown Noise Distribution: A Rank-Based Approach. AISTATS Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott
2011	A	`conf`	Active Diagnosis via AUC Maximization: An Efficient Approach for Multiple Fault Identification in Large Scale, Noisy Networks. UAI Gowtham Bellala, Jason Stanley, Clayton Scott, Suresh K. Bhavnani
2011	—	`conf`	Generalizing from Several Related Classification Tasks to a New Unlabeled Sample. NIPS Gilles Blanchard, Gyemin Lee, Clayton Scott
2011	J	`jnl`	Statistical file matching of flow cytometry data. J. Biomed. Informatics Gyemin Lee, William Finn, Clayton Scott
2011	A*	`conf`	Surrogate losses and regret bounds for cost-sensitive classification with example-dependent costs. ICML Clayton Scott
2010	—	`conf`	Extensions of Generalized Binary Search to Group Identification and Exponential Costs. NIPS Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott
2010	J	`jnl`	Nested support vector machines. IEEE Trans. Signal Process. Gyemin Lee, Clayton Scott
2010	J	`jnl`	Query Learning with Exponential Query Costs CoRR Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott
2010	J	`jnl`	Semi-Supervised Novelty Detection. J. Mach. Learn. Res. Gilles Blanchard, Gyemin Lee, Clayton Scott
2009	J	`jnl`	Group-based Query Learning for rapid diagnosis in time-critical situations CoRR Gowtham Bellala, Suresh K. Bhavnani, Clayton Scott
2009	A	`conf`	Novelty detection: Unlabeled data definitely help. AISTATS Clayton Scott, Gilles Blanchard
2008	Misc	`conf`	Nested support vector machines. ICASSP Gyemin Lee, Clayton Scott
2007	J	`jnl`	Performance Measures for Neyman-Pearson Classification. IEEE Trans. Inf. Theory Clayton Scott
2007	—	`conf`	The One Class Support Vector Machine Solution Path. ICASSP (2) Gyemin Lee, Clayton Scott
2005	J	`jnl`	Tree pruning with subadditive penalties. IEEE Trans. Signal Process. Clayton Scott