Heinrich Jiang

59 papers A* 20A 6B 1Journal 31Unranked 1

Year	Rank	Type	Title / Venue / Authors
2024	A*	`conf`	SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models. NeurIPS Jianyi Zhang, Da-Cheng Juan, Cyrus Rashtchian, Chun-Sung Ferng, Heinrich Jiang, Yiran Chen
2024	J	`jnl`	SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models. CoRR Jianyi Zhang, Da-Cheng Juan, Cyrus Rashtchian, Chun-Sung Ferng, Heinrich Jiang, Yiran Chen
2024	J	`jnl`	SpacTor-T5: Pre-training T5 Models with Span Corruption and Replaced Token Detection. CoRR Ke Ye, Heinrich Jiang, Afshin Rostamizadeh, Ayan Chakrabarti, Giulia DeSalvo, Jean-François Kagy, Lazaros Karydas, Gui Citovsky, Sanjiv Kumar
2022	A*	`conf`	Churn Reduction via Distillation. ICLR Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh
2022	J	`jnl`	Is margin all you need? An extensive empirical study of active learning on tabular data. CoRR Dara Bahri, Heinrich Jiang, Tal Schuster, Afshin Rostamizadeh
2022	J	`jnl`	Predicting on the Edge: Identifying Where a Larger Model Does Better. CoRR Taman Narayan, Heinrich Jiang, Sen Zhao, Sanjiv Kumar
2022	A*	`conf`	Scarf: Self-Supervised Contrastive Learning using Random Feature Corruption. ICLR Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
2021	A*	`conf`	Active Covering. ICML Heinrich Jiang, Afshin Rostamizadeh
2021	J	`jnl`	Active Covering. CoRR Heinrich Jiang, Afshin Rostamizadeh
2021	A*	`conf`	Bootstrapping for Batch Active Sampling. KDD Heinrich Jiang, Maya R. Gupta
2021	J	`jnl`	Churn Reduction via Distillation. CoRR Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri, Andrew Cotter, Afshin Rostamizadeh
2021	J	`jnl`	Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection. CoRR Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
2021	A	`conf`	Learning the Truth From Only One Side of the Story. AISTATS Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
2021	A*	`conf`	Locally Adaptive Label Smoothing Improves Predictive Churn. ICML Dara Bahri, Heinrich Jiang
2021	J	`jnl`	Locally Adaptive Label Smoothing for Predictive Churn. CoRR Dara Bahri, Heinrich Jiang
2021	A*	`conf`	MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking. CVPR Jennifer Jang, Heinrich Jiang
2021	J	`jnl`	MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking. CoRR Jennifer Jang, Heinrich Jiang
2021	A*	`conf`	Robustness Guarantees for Mode Estimation with an Application to Bandits. AAAI Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
2021	J	`jnl`	SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption. CoRR Dara Bahri, Heinrich Jiang, Yi Tay, Donald Metzler
2021	A	`conf`	Stochastic Bandits with Linear Constraints. AISTATS Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
2020	A*	`conf`	A General Approach to Fairness with Optimal Transport. AAAI Silvia Chiappa, Ray Jiang, Tom Stepleton, Aldo Pacchiano, Heinrich Jiang, John Aslanides
2020	A*	`conf`	Deep k-NN for Noisy Labels. ICML Dara Bahri, Heinrich Jiang, Maya R. Gupta
2020	J	`jnl`	Deep k-NN for Noisy Labels. CoRR Dara Bahri, Heinrich Jiang, Maya R. Gupta
2020	A*	`conf`	Faster DBSCAN via subsampled similarity queries. NeurIPS Heinrich Jiang, Jennifer Jang, Jakub Lacki
2020	J	`jnl`	Faster DBSCAN via subsampled similarity queries. CoRR Heinrich Jiang, Jennifer Jang, Jakub Lacki
2020	A	`conf`	Identifying and Correcting Label Bias in Machine Learning. AISTATS Heinrich Jiang, Ofir Nachum
2020	J	`jnl`	Learning the Truth From Only One Side of the Story. CoRR Heinrich Jiang, Qijia Jiang, Aldo Pacchiano
2020	J	`jnl`	Robustness Guarantees for Mode Estimation with an Application to Bandits. CoRR Aldo Pacchiano, Heinrich Jiang, Michael I. Jordan
2020	J	`jnl`	Stochastic Bandits with Linear Constraints. CoRR Aldo Pacchiano, Mohammad Ghavamzadeh, Peter L. Bartlett, Heinrich Jiang
2019	A*	`conf`	DBSCAN++: Towards fast and scalable density clustering. ICML Jennifer Jang, Heinrich Jiang
2019	J	`jnl`	Group-based Fair Learning Leads to Counter-intuitive Predictions. CoRR Ofir Nachum, Heinrich Jiang
2019	J	`jnl`	Identifying and Correcting Label Bias in Machine Learning. CoRR Heinrich Jiang, Ofir Nachum
2019	J	`jnl`	Minimum-Margin Active Learning. CoRR Heinrich Jiang, Maya R. Gupta
2019	A*	`conf`	Non-Asymptotic Uniform Rates of Consistency for k-NN Regression. AAAI Heinrich Jiang
2019	J	`jnl`	Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. J. Mach. Learn. Res. Andrew Cotter, Heinrich Jiang, Maya R. Gupta, Serena Lutong Wang, Taman Narayan, Seungil You, Karthik Sridharan
2019	A	`conf`	Robustness Guarantees for Density Clustering. AISTATS Heinrich Jiang, Jennifer Jang, Ofir Nachum
2019	A*	`conf`	Shape Constraints for Set Functions. ICML Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Erez Louidor, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu
2019	A*	`conf`	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You
2019	B	`conf`	Two-Player Games for Efficient Non-Convex Constrained Optimization. ALT Andrew Cotter, Heinrich Jiang, Karthik Sridharan
2019	A	`conf`	Wasserstein Fair Classification. UAI Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
2019	J	`jnl`	Wasserstein Fair Classification. CoRR Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, Silvia Chiappa
2018	J	`jnl`	DBSCAN++: Towards fast and scalable density clustering. CoRR Jennifer Jang, Heinrich Jiang
2018	J	`jnl`	Interpretable Set Functions. CoRR Andrew Cotter, Maya R. Gupta, Heinrich Jiang, James Muller, Taman Narayan, Serena Lutong Wang, Tao Zhu
2018	A*	`conf`	Nonparametric Stochastic Contextual Bandits. AAAI Melody Y. Guan, Heinrich Jiang
2018	J	`jnl`	Nonparametric Stochastic Contextual Bandits. CoRR Melody Y. Guan, Heinrich Jiang
2018	J	`jnl`	Optimization with Non-Differentiable Constraints with Applications to Fairness, Recall, Churn, and Other Goals. CoRR Andrew Cotter, Heinrich Jiang, Serena Lutong Wang, Taman Narayan, Maya R. Gupta, Seungil You, Karthik Sridharan
2018	A*	`conf`	Quickshift++: Provably Good Initializations for Sample-Based Mean Shift. ICML Heinrich Jiang, Jennifer Jang, Samory Kpotufe
2018	J	`jnl`	Quickshift++: Provably Good Initializations for Sample-Based Mean Shift. CoRR Heinrich Jiang, Jennifer Jang, Samory Kpotufe
2018	A*	`conf`	To Trust Or Not To Trust A Classifier. NeurIPS Heinrich Jiang, Been Kim, Melody Y. Guan, Maya R. Gupta
2018	J	`jnl`	To Trust Or Not To Trust A Classifier. CoRR Heinrich Jiang, Been Kim, Maya R. Gupta
2018	J	`jnl`	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You
2018	J	`jnl`	Two-Player Games for Efficient Non-Convex Constrained Optimization. CoRR Andrew Cotter, Heinrich Jiang, Karthik Sridharan
2017	A*	`conf`	Density Level Set Estimation on Manifolds with DBSCAN. ICML Heinrich Jiang
2017	A	`conf`	Modal-set estimation with an application to clustering. AISTATS Heinrich Jiang, Samory Kpotufe
2017	—	`conf`	On the Consistency of Quick Shift. NIPS Heinrich Jiang
2017	J	`jnl`	On the Consistency of Quick Shift. CoRR Heinrich Jiang
2017	J	`jnl`	Rates of Uniform Consistency for k-NN Regression. CoRR Heinrich Jiang
2017	A*	`conf`	Uniform Convergence Rates for Kernel Density Estimation. ICML Heinrich Jiang
2016	J	`jnl`	Modal-set estimation with an application to clustering. CoRR Heinrich Jiang, Samory Kpotufe