 | Up a level |
Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
Use the mouse wheel or scroll gestures to zoom into the graph.
You can click on the nodes and links to highlight them and move the nodes by dragging them.
Hold down the "Ctrl" key or the "⌘" key while clicking on the nodes to open the list of this person's publications.
Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Gardner, J. R., Kusner, M. J., Xu, Z., Weinberger, K. Q., & Cunningham, J. P. (2014, June). Bayesian optimization with inequality constraints [Paper]. 31st International Conference on Machine Learning (ICML 2014), Beijing, China. Published in Proceedings of Machine Learning Research, 32(2). External link
Huang, G., Guo, C., Kusner, M. J., Sun, Y., Weinberger, K. Q., & Sha, F. (2016, December). Supervised word mover's distance [Paper]. 30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain. External link
Kusner, M. J., Sun, Y., Sridharan, K., & Weinberger, K. Q. (2016, May). Private causal inference [Paper]. 19th International Conference on Artificial Intelligence and Statistics (AISTATS 2016), Cadiz, Spain. External link
Kusner, M. J., Gardner, J. R., Garnett, R., & Weinberger, K. Q. (2015, July). Differentially private bayesian optimization [Paper]. 32nd International Conference on Machine Learning (ICML'15), Lile, France. External link
Kusner, M. J., Sun, Y., Kolkin, N. I., & Weinberger, K. Q. (2025, July). From word embeddings to document distances [Paper]. 32nd International Conference on Machine Learning (ICML 2015), Lile, France. External link
Kusner, M. J., Chen, W., Zhou, Q., Xu, Z., Weinberger, K. Q., & Chen, Y. (2014, July). Feature-cost sensitive learning with submodular trees of classifiers [Paper]. 28th AAAI Conference on Artificial Intelligence (AAAI 2014), Québec, Québec, Canada. External link
Kusner, M. J., Tyree, S., Weinberger, K. Q., & Agrawal, K. (2014, June). Stochastic neighbor compression [Paper]. 31st International Conference on Machine Learning (ICML 2014), Beijing, China. External link
Malkomes, G., Kusner, M. J., Chen, W., Weinberger, K. Q., & Moseley, B. (2015, December). Fast distributed k-center clustering with outliers on massive data [Paper]. 29th International Conference on Neural Information Processing Systems (NIPS 2015), Montréal, Québec, Canada. External link
Xu, Z., Kusner, M. J., Weinberger, K. Q., Chen, M., & Chapelle, O. (2014). Classifier cascades and trees for minimizing feature evaluation cost. Journal of Machine Learning Research, 15(1), 2113-2144. External link
