Heinrich Jiang

According to our database, Heinrich Jiang authored at least 38 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection.

Ke Ye, Heinrich Jiang, Afshin Rostamizadeh, Ayan Chakrabarti, Giulia DeSalvo, Jean-François Kagy, Lazaros Karydas, Gui Citovsky, Sanjiv Kumar

CoRR, 2024

2022

Is margin all you need? An extensive empirical study of active learning on tabular data.

Dara Bahri, Heinrich Jiang, Tal Schuster, Afshin Rostamizadeh

CoRR, 2022

Predicting on the Edge: Identifying Where a Larger Model Does Better.

Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar

CoRR, 2022

Churn Reduction via Distillation.

Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh

Proceedings of the Tenth International Conference on Learning Representations, 2022

Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption.

Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Locally Adaptive Label Smoothing for Predictive Churn.

Dara Bahri, Heinrich Jiang

CoRR, 2021

Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection.

Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler

CoRR, 2021

Bootstrapping for Batch Active Sampling.

Heinrich Jiang, Maya R. Gupta

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Active Covering.

Heinrich Jiang, Afshin Rostamizadeh

Proceedings of the 38th International Conference on Machine Learning, 2021

Locally Adaptive Label Smoothing Improves Predictive Churn.

Dara Bahri, Heinrich Jiang

Proceedings of the 38th International Conference on Machine Learning, 2021

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

Jennifer Jang, Heinrich Jiang

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Stochastic Bandits with Linear Constraints.

Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Learning the Truth From Only One Side of the Story.

Heinrich Jiang, Qijia Jiang, Aldo Pacchiano

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Robustness Guarantees for Mode Estimation with an Application to Bandits.

Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Faster DBSCAN via subsampled similarity queries.

Heinrich Jiang, Jennifer Jang, Jakub Lacki

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Deep k-NN for Noisy Labels.

Dara Bahri, Heinrich Jiang, Maya R. Gupta

Proceedings of the 37th International Conference on Machine Learning, 2020

Identifying and Correcting Label Bias in Machine Learning.

Heinrich Jiang, Ofir Nachum

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A General Approach to Fairness with Optimal Transport.

Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

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

Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Lutong Wang, Taman Narayan, Seungil You, Karthik Sridharan

J. Mach. Learn. Res., 2019

Group-based Fair Learning Leads to Counter-intuitive Predictions.

Ofir Nachum, Heinrich Jiang

CoRR, 2019

Minimum-Margin Active Learning.

Heinrich Jiang, Maya R. Gupta

CoRR, 2019

Wasserstein Fair Classification.

Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa

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

DBSCAN++: Towards fast and scalable density clustering.

Jennifer Jang, Heinrich Jiang

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You

Proceedings of the 36th International Conference on Machine Learning, 2019

Shape Constraints for Set Functions.

Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu

Proceedings of the 36th International Conference on Machine Learning, 2019

Two-Player Games for Efficient Non-Convex Constrained Optimization.

Andrew Cotter, Heinrich Jiang, Karthik Sridharan

Proceedings of the Algorithmic Learning Theory, 2019

Robustness Guarantees for Density Clustering.

Heinrich Jiang, Jennifer Jang, Ofir Nachum

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

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

Heinrich Jiang

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Interpretable Set Functions.

Andrew Cotter, Maya R. Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu

CoRR, 2018

To Trust Or Not To Trust A Classifier.

Heinrich Jiang, Been Kim, Maya R. Gupta

CoRR, 2018

To Trust Or Not To Trust A Classifier.

Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

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

Heinrich Jiang, Jennifer Jang, Samory Kpotufe

Proceedings of the 35th International Conference on Machine Learning, 2018

Nonparametric Stochastic Contextual Bandits.

Melody Y. Guan, Heinrich Jiang

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

Rates of Uniform Consistency for k-NN Regression.

Heinrich Jiang

CoRR, 2017

On the Consistency of Quick Shift.

Heinrich Jiang

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Uniform Convergence Rates for Kernel Density Estimation.

Heinrich Jiang

Proceedings of the 34th International Conference on Machine Learning, 2017

Density Level Set Estimation on Manifolds with DBSCAN.

Heinrich Jiang

Proceedings of the 34th International Conference on Machine Learning, 2017

Modal-set estimation with an application to clustering.

Heinrich Jiang, Samory Kpotufe

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017