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SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption


Jun 29, 2021
Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler


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Churn Reduction via Distillation


Jun 04, 2021
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh


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Active Covering


Jun 04, 2021
Heinrich Jiang, Afshin Rostamizadeh

* ICML 2021 

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MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking


Apr 01, 2021
Jennifer Jang, Heinrich Jiang


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Locally Adaptive Label Smoothing for Predictive Churn


Feb 09, 2021
Dara Bahri, Heinrich Jiang


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Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection


Feb 09, 2021
Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler


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Stochastic Bandits with Linear Constraints


Jun 17, 2020
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter Bartlett, Heinrich Jiang

* 9 pages 

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Faster DBSCAN via subsampled similarity queries


Jun 11, 2020
Heinrich Jiang, Jennifer Jang, Jakub Łącki


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Learning the Truth From Only One Side of the Story


Jun 08, 2020
Heinrich Jiang, Qijia Jiang, Aldo Pacchiano


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Deep k-NN for Noisy Labels


Apr 26, 2020
Dara Bahri, Heinrich Jiang, Maya Gupta

* Full paper (including supplemental) can be found at https://github.com/dbahri/deepknn 

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Robustness Guarantees for Mode Estimation with an Application to Bandits


Mar 05, 2020
Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan

* 12 pages, 7 figures, 14 appendix pages 

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Group-based Fair Learning Leads to Counter-intuitive Predictions


Oct 04, 2019
Ofir Nachum, Heinrich Jiang


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Wasserstein Fair Classification


Jul 28, 2019
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

* Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019 

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Minimum-Margin Active Learning


May 31, 2019
Heinrich Jiang, Maya Gupta


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Identifying and Correcting Label Bias in Machine Learning


Jan 15, 2019
Heinrich Jiang, Ofir Nachum


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Non-Asymptotic Uniform Rates of Consistency for k-NN Regression


Nov 03, 2018
Heinrich Jiang

* In Proceedings of 33rd AAAI Conference on Artificial Intelligence (AAAI 2019) 

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DBSCAN++: Towards fast and scalable density clustering


Oct 31, 2018
Jennifer Jang, Heinrich Jiang


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To Trust Or Not To Trust A Classifier


Oct 26, 2018
Heinrich Jiang, Been Kim, Melody Y. Guan, Maya Gupta

* NIPS 2018 

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Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints


Sep 28, 2018
Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, Seungil You


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Two-Player Games for Efficient Non-Convex Constrained Optimization


Sep 28, 2018
Andrew Cotter, Heinrich Jiang, Karthik Sridharan


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Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals


Sep 11, 2018
Andrew Cotter, Heinrich Jiang, Serena Wang, Taman Narayan, Maya Gupta, Seungil You, Karthik Sridharan


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Interpretable Set Functions


May 31, 2018
Andrew Cotter, Maya Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Wang, Tao Zhu


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Quickshift++: Provably Good Initializations for Sample-Based Mean Shift


May 21, 2018
Heinrich Jiang, Jennifer Jang, Samory Kpotufe

* ICML 2018. Code release: https://github.com/google/quickshift 

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Nonparametric Stochastic Contextual Bandits


Jan 05, 2018
Melody Y. Guan, Heinrich Jiang

* AAAI 2018 

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On the Consistency of Quick Shift


Dec 23, 2017
Heinrich Jiang

* Proceedings of 31st Conference on Neural Information Processing Systems (NIPS 2017) 

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Density Level Set Estimation on Manifolds with DBSCAN


Jul 12, 2017
Heinrich Jiang

* Proceedings of the 34th International Conference on Machine Learning, PMLR 70: 1684-1693 (2017) 

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Modal-set estimation with an application to clustering


Jun 13, 2016
Heinrich Jiang, Samory Kpotufe

* Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, in PMLR 54:1197-1206, 2017 

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