Heinrich Jiang

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 57

Top Topics

Locally Adaptive

Kernel Density Estimation

Top Venues

Publications

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 (2024)
Dara Bahri, Heinrich Jiang, Tal Schuster, Afshin Rostamizadeh
Is margin all you need? An extensive empirical study of active learning on tabular data. CoRR (2022)
Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar
Predicting on the Edge: Identifying Where a Larger Model Does Better. CoRR (2022)
Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption. ICLR (2022)
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh
Churn Reduction via Distillation. ICLR (2022)
Heinrich Jiang, Afshin Rostamizadeh
Active Covering. CoRR (2021)
Heinrich Jiang, Afshin Rostamizadeh
Active Covering. ICML (2021)
Jennifer Jang, Heinrich Jiang
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking. CoRR (2021)
Dara Bahri, Heinrich Jiang
Locally Adaptive Label Smoothing for Predictive Churn. CoRR (2021)
Jennifer Jang, Heinrich Jiang
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking. CVPR (2021)
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
Stochastic Bandits with Linear Constraints. AISTATS (2021)
Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection. CoRR (2021)
Heinrich Jiang, Maya R. Gupta
Bootstrapping for Batch Active Sampling. KDD (2021)
Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption. CoRR (2021)
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh
Churn Reduction via Distillation. CoRR (2021)
Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI (2021)
Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
Learning the Truth From Only One Side of the Story. AISTATS (2021)
Dara Bahri, Heinrich Jiang
Locally Adaptive Label Smoothing Improves Predictive Churn. ICML (2021)
Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
Learning the Truth From Only One Side of the Story. CoRR (2020)
Dara Bahri, Heinrich Jiang, Maya R. Gupta
Deep k-NN for Noisy Labels. ICML (2020)
Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
Stochastic Bandits with Linear Constraints. CoRR (2020)
Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR (2020)
Heinrich Jiang, Ofir Nachum
Identifying and Correcting Label Bias in Machine Learning. AISTATS (2020)
Dara Bahri, Heinrich Jiang, Maya R. Gupta
Deep k-NN for Noisy Labels. CoRR (2020)
Heinrich Jiang, Jennifer Jang, Jakub Lacki
Faster DBSCAN via subsampled similarity queries. NeurIPS (2020)
Heinrich Jiang, Jennifer Jang, Jakub Lacki
Faster DBSCAN via subsampled similarity queries. CoRR (2020)
Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides
A General Approach to Fairness with Optimal Transport. AAAI (2020)
Heinrich Jiang, Jennifer Jang, Ofir Nachum
Robustness Guarantees for Density Clustering. AISTATS (2019)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Wang, Tao Zhu
Shape Constraints for Set Functions. ICML (2019)
Andrew Cotter, Heinrich Jiang, Karthik Sridharan
Two-Player Games for Efficient Non-Convex Constrained Optimization. ALT (2019)
Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena 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 (2019)
Heinrich Jiang, Maya R. Gupta
Minimum-Margin Active Learning. CoRR (2019)
Heinrich Jiang, Ofir Nachum
Identifying and Correcting Label Bias in Machine Learning. CoRR (2019)
Heinrich Jiang
Non-Asymptotic Uniform Rates of Consistency for k-NN Regression. AAAI (2019)
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
Wasserstein Fair Classification. CoRR (2019)
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
Wasserstein Fair Classification. UAI (2019)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML (2019)
Jennifer Jang, Heinrich Jiang
DBSCAN++: Towards fast and scalable density clustering. ICML (2019)
Ofir Nachum, Heinrich Jiang
Group-based Fair Learning Leads to Counter-intuitive Predictions. CoRR (2019)
Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta
To Trust Or Not To Trust A Classifier. NeurIPS (2018)
Heinrich Jiang, Jennifer Jang, Samory Kpotufe
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift. ICML (2018)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR (2018)
Heinrich Jiang, Jennifer Jang, Samory Kpotufe
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift. CoRR (2018)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Wang, Tao Zhu
Interpretable Set Functions. CoRR (2018)
Andrew Cotter, Heinrich Jiang, Serena 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 (2018)
Heinrich Jiang, Been Kim, Maya R. Gupta
To Trust Or Not To Trust A Classifier. CoRR (2018)
Melody Y. Guan, Heinrich Jiang
Nonparametric Stochastic Contextual Bandits. AAAI (2018)
Jennifer Jang, Heinrich Jiang
DBSCAN++: Towards fast and scalable density clustering. CoRR (2018)
Andrew Cotter, Heinrich Jiang, Karthik Sridharan
Two-Player Games for Efficient Non-Convex Constrained Optimization. CoRR (2018)
Melody Y. Guan, Heinrich Jiang
Nonparametric Stochastic Contextual Bandits. CoRR (2018)
Heinrich Jiang
Uniform Convergence Rates for Kernel Density Estimation. ICML (2017)
Heinrich Jiang
On the Consistency of Quick Shift. NIPS (2017)
Heinrich Jiang, Samory Kpotufe
Modal-set estimation with an application to clustering. AISTATS (2017)
Heinrich Jiang
On the Consistency of Quick Shift. CoRR (2017)
Heinrich Jiang
Density Level Set Estimation on Manifolds with DBSCAN. ICML (2017)
Heinrich Jiang
Rates of Uniform Consistency for k-NN Regression. CoRR (2017)
Heinrich Jiang, Samory Kpotufe
Modal-set estimation with an application to clustering. CoRR (2016)