Countries citing papers authored by Heinrich Jiang
Since
Specialization
Citations
This map shows the geographic impact of Heinrich Jiang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Heinrich Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heinrich Jiang more than expected).
This network shows the impact of papers produced by Heinrich Jiang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Heinrich Jiang. The network helps show where Heinrich Jiang may publish in the future.
Co-authorship network of co-authors of Heinrich Jiang
This figure shows the co-authorship network connecting the top 25 collaborators of Heinrich Jiang.
A scholar is included among the top collaborators of Heinrich Jiang based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Heinrich Jiang. Heinrich Jiang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
16 of 16 papers shown
1.
Bahri, Dara & Heinrich Jiang. (2021). Locally Adaptive Label Smoothing Improves Predictive Churn. International Conference on Machine Learning. 532–542.1 indexed citations
2.
Pacchiano, Aldo, Mohammad Ghavamzadeh, Peter L. Bartlett, & Heinrich Jiang. (2021). Stochastic Bandits with Linear Constraints. International Conference on Artificial Intelligence and Statistics. 2827–2835.2 indexed citations
Pacchiano, Aldo, et al.. (2019). Wasserstein Fair Classification. Uncertainty in Artificial Intelligence. 862–872.5 indexed citations
7.
Cotter, Andrew, et al.. (2019). Shape Constraints for Set Functions. International Conference on Machine Learning. 1388–1396.5 indexed citations
8.
Jiang, Heinrich & Ofir Nachum. (2019). Identifying and Correcting Label Bias in Machine Learning. International Conference on Artificial Intelligence and Statistics. 702–712.9 indexed citations
9.
Jiang, Heinrich, et al.. (2019). Robustness Guarantees for Density Clustering. 3342–3351.3 indexed citations
Jiang, Heinrich, Been Kim, Melody Y. Guan, & Maya R. Gupta. (2018). To Trust Or Not To Trust A Classifier. arXiv (Cornell University). 31. 5541–5552.35 indexed citations
12.
Jiang, Heinrich, et al.. (2018). Quickshift++: Provably Good Initializations for Sample-Based Mean Shift. International Conference on Machine Learning. 2294–2303.5 indexed citations
13.
Guan, Melody Y. & Heinrich Jiang. (2018). Nonparametric Stochastic Contextual Bandits. Proceedings of the AAAI Conference on Artificial Intelligence. 32(1).4 indexed citations
14.
Jiang, Heinrich. (2017). Uniform Convergence Rates for Kernel Density Estimation. International Conference on Machine Learning. 1694–1703.19 indexed citations
15.
Jiang, Heinrich. (2017). On the Consistency of Quick Shift. Neural Information Processing Systems. 30. 46–55.3 indexed citations
16.
Jiang, Heinrich. (2017). Rates of Uniform Consistency for k-NN Regression.. arXiv (Cornell University).1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.