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Heinrich Jiang
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2020 – today
- 2024
- [i29]Ke Ye, Heinrich Jiang, Afshin Rostamizadeh, Ayan Chakrabarti, Giulia DeSalvo, Jean-François Kagy, Lazaros Karydas, Gui Citovsky, Sanjiv Kumar:
SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection. CoRR abs/2401.13160 (2024) - 2022
- [c27]Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler:
Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption. ICLR 2022 - [c26]Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh:
Churn Reduction via Distillation. ICLR 2022 - [i28]Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar:
Predicting on the Edge: Identifying Where a Larger Model Does Better. CoRR abs/2202.07652 (2022) - [i27]Dara Bahri, Heinrich Jiang, Tal Schuster
, Afshin Rostamizadeh:
Is margin all you need? An extensive empirical study of active learning on tabular data. CoRR abs/2210.03822 (2022) - 2021
- [c25]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI 2021: 9277-9284 - [c24]Heinrich Jiang, Qijia Jiang, Aldo Pacchiano:
Learning the Truth From Only One Side of the Story. AISTATS 2021: 2413-2421 - [c23]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. AISTATS 2021: 2827-2835 - [c22]Jennifer Jang, Heinrich Jiang:
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking. CVPR 2021: 4102-4113 - [c21]Dara Bahri, Heinrich Jiang:
Locally Adaptive Label Smoothing Improves Predictive Churn. ICML 2021: 532-542 - [c20]Heinrich Jiang, Afshin Rostamizadeh:
Active Covering. ICML 2021: 5013-5022 - [c19]Heinrich Jiang, Maya R. Gupta:
Bootstrapping for Batch Active Sampling. KDD 2021: 3086-3096 - [i26]Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler:
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection. CoRR abs/2102.05131 (2021) - [i25]Dara Bahri, Heinrich Jiang:
Locally Adaptive Label Smoothing for Predictive Churn. CoRR abs/2102.05140 (2021) - [i24]Jennifer Jang, Heinrich Jiang:
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking. CoRR abs/2104.00303 (2021) - [i23]Heinrich Jiang, Afshin Rostamizadeh:
Active Covering. CoRR abs/2106.02552 (2021) - [i22]Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh:
Churn Reduction via Distillation. CoRR abs/2106.02654 (2021) - [i21]Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler:
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption. CoRR abs/2106.15147 (2021) - 2020
- [c18]Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides:
A General Approach to Fairness with Optimal Transport. AAAI 2020: 3633-3640 - [c17]Heinrich Jiang, Ofir Nachum:
Identifying and Correcting Label Bias in Machine Learning. AISTATS 2020: 702-712 - [c16]Dara Bahri, Heinrich Jiang, Maya R. Gupta:
Deep k-NN for Noisy Labels. ICML 2020: 540-550 - [c15]Heinrich Jiang, Jennifer Jang, Jakub Lacki:
Faster DBSCAN via subsampled similarity queries. NeurIPS 2020 - [i20]Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan:
Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR abs/2003.02932 (2020) - [i19]Dara Bahri, Heinrich Jiang, Maya R. Gupta:
Deep k-NN for Noisy Labels. CoRR abs/2004.12289 (2020) - [i18]Heinrich Jiang, Qijia Jiang, Aldo Pacchiano:
Learning the Truth From Only One Side of the Story. CoRR abs/2006.04858 (2020) - [i17]Heinrich Jiang, Jennifer Jang, Jakub Lacki:
Faster DBSCAN via subsampled similarity queries. CoRR abs/2006.06743 (2020) - [i16]Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang:
Stochastic Bandits with Linear Constraints. CoRR abs/2006.10185 (2020)
2010 – 2019
- 2019
- [j1]Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Lutong Wang, Taman Narayan, Seungil You, Karthik Sridharan:
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. J. Mach. Learn. Res. 20: 172:1-172:59 (2019) - [c14]Heinrich Jiang:
Non-Asymptotic Uniform Rates of Consistency for k-NN Regression. AAAI 2019: 3999-4006 - [c13]Heinrich Jiang, Jennifer Jang, Ofir Nachum:
Robustness Guarantees for Density Clustering. AISTATS 2019: 3342-3351 - [c12]Andrew Cotter, Heinrich Jiang, Karthik Sridharan:
Two-Player Games for Efficient Non-Convex Constrained Optimization. ALT 2019: 300-332 - [c11]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu:
Shape Constraints for Set Functions. ICML 2019: 1388-1396 - [c10]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML 2019: 1397-1405 - [c9]Jennifer Jang, Heinrich Jiang:
DBSCAN++: Towards fast and scalable density clustering. ICML 2019: 3019-3029 - [c8]Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa:
Wasserstein Fair Classification. UAI 2019: 862-872 - [i15]Heinrich Jiang, Ofir Nachum:
Identifying and Correcting Label Bias in Machine Learning. CoRR abs/1901.04966 (2019) - [i14]Heinrich Jiang, Maya R. Gupta:
Minimum-Margin Active Learning. CoRR abs/1906.00025 (2019) - [i13]Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa:
Wasserstein Fair Classification. CoRR abs/1907.12059 (2019) - [i12]Ofir Nachum, Heinrich Jiang:
Group-based Fair Learning Leads to Counter-intuitive Predictions. CoRR abs/1910.02097 (2019) - 2018
- [c7]Melody Y. Guan, Heinrich Jiang:
Nonparametric Stochastic Contextual Bandits. AAAI 2018: 3119-3125 - [c6]Heinrich Jiang, Jennifer Jang, Samory Kpotufe:
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift. ICML 2018: 2299-2308 - [c5]Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta:
To Trust Or Not To Trust A Classifier. NeurIPS 2018: 5546-5557 - [i11]Melody Y. Guan, Heinrich Jiang:
Nonparametric Stochastic Contextual Bandits. CoRR abs/1801.01750 (2018) - [i10]Andrew Cotter, Heinrich Jiang, Karthik Sridharan:
Two-Player Games for Efficient Non-Convex Constrained Optimization. CoRR abs/1804.06500 (2018) - [i9]Heinrich Jiang, Jennifer Jang, Samory Kpotufe:
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift. CoRR abs/1805.07909 (2018) - [i8]Heinrich Jiang, Been Kim, Maya R. Gupta:
To Trust Or Not To Trust A Classifier. CoRR abs/1805.11783 (2018) - [i7]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu:
Interpretable Set Functions. CoRR abs/1806.00050 (2018) - [i6]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR abs/1807.00028 (2018) - [i5]Andrew Cotter, Heinrich Jiang, Serena Lutong Wang, Taman Narayan, Maya R. Gupta, Seungil You, Karthik Sridharan:
Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. CoRR abs/1809.04198 (2018) - [i4]Jennifer Jang, Heinrich Jiang:
DBSCAN++: Towards fast and scalable density clustering. CoRR abs/1810.13105 (2018) - 2017
- [c4]Heinrich Jiang, Samory Kpotufe:
Modal-set estimation with an application to clustering. AISTATS 2017: 1197-1206 - [c3]Heinrich Jiang:
Density Level Set Estimation on Manifolds with DBSCAN. ICML 2017: 1684-1693 - [c2]Heinrich Jiang:
Uniform Convergence Rates for Kernel Density Estimation. ICML 2017: 1694-1703 - [c1]Heinrich Jiang:
On the Consistency of Quick Shift. NIPS 2017: 46-55 - [i3]Heinrich Jiang:
Rates of Uniform Consistency for k-NN Regression. CoRR abs/1707.06261 (2017) - [i2]Heinrich Jiang:
On the Consistency of Quick Shift. CoRR abs/1710.10646 (2017) - 2016
- [i1]Heinrich Jiang, Samory Kpotufe:
Modal-set estimation with an application to clustering. CoRR abs/1606.04166 (2016)
Coauthor Index
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last updated on 2024-09-13 00:44 CEST by the dblp team
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