Heinrich Jiang
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Safety Research
- Signal Processing
- Management Science and Operations Research
- Co-authors
- Maya R. GuptaMelody Y. GuanBeen KimOfir NachumAldo PacchianoTom StepletonSilvia ChiappaJohn Aslanides
- Topics
- Machine Learning and Algorithms (3 papers)Statistical Methods and Inference (3 papers)Machine Learning and Data Classification (3 papers)
- Journals
- arXiv (Cornell University)Neural Information Processing SystemsInternational Conference on Machine Learning
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
Heinrich Jiang
15 papers receiving 128 citations
Peers
Comparison fields: 5 of 57
- Artificial Intelligence 88
- Computer Vision and Pattern Recognition 35
- Safety Research 14
- Signal Processing 13
- Management Science and Operations Research 12
Countries citing papers authored by Heinrich Jiang
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).
Fields of papers citing papers by Heinrich Jiang
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | Stochastic Bandits with Linear Constraints | 2 |
| 2 | Locally Adaptive Label Smoothing Improves Predictive Churn | 1 |
| 3 | 3 | |
| 4 | 13 | |
| 5 | 19 | |
| 6 | Identifying and Correcting Label Bias in Machine Learning | 9 |
| 7 | Wasserstein Fair Classification | 5 |
| 8 | Shape Constraints for Set Functions | 5 |
| 9 | Robustness Guarantees for Density Clustering | 3 |
| 10 | 8 | |
| 11 | Quickshift++: Provably Good Initializations for Sample-Based Mean Shift | 5 |
| 12 | To Trust Or Not To Trust A Classifier | 35 |
| 13 | 4 | |
| 14 | Rates of Uniform Consistency for k-NN Regression. | 1 |
| 15 | On the Consistency of Quick Shift | 3 |
| 16 | Uniform Convergence Rates for Kernel Density Estimation | 19 |
About Heinrich Jiang
Heinrich Jiang is a scholar working on Statistics and Probability, Artificial Intelligence and Management Science and Operations Research, having authored 16 papers that have together received 135 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (3 papers), Statistical Methods and Inference (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Artificial Intelligence (88 citations), Health Informatics (3 citations) and Computer Vision and Pattern Recognition (35 citations). Heinrich Jiang has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Maya R. Gupta, Melody Y. Guan, Been Kim, Ofir Nachum, Aldo Pacchiano, Tom Stepleton, Silvia Chiappa, John Aslanides, Samory Kpotufe and Andrew Cotter. Their work appears in journals such as arXiv (Cornell University), Neural Information Processing Systems and International Conference on Machine Learning.
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.