Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Statistical region merging
2004561 citationsRichard Nock, Frank NielsenIEEE Transactions on Pattern Analysis and Machine Intelligenceprofile →
This map shows the geographic impact of Richard Nock'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 Richard Nock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Nock more than expected).
This network shows the impact of papers produced by Richard Nock. 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 Richard Nock. The network helps show where Richard Nock may publish in the future.
Co-authorship network of co-authors of Richard Nock
This figure shows the co-authorship network connecting the top 25 collaborators of Richard Nock.
A scholar is included among the top collaborators of Richard Nock 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 Richard Nock. Richard Nock is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Balle, Borja, et al.. (2020). Local Differential Privacy for Sampling. International Conference on Artificial Intelligence and Statistics. 3404–3413.2 indexed citations
3.
Nock, Richard, et al.. (2020). All your loss are belong to Bayes. Neural Information Processing Systems. 33. 18505–18517.1 indexed citations
4.
Nock, Richard, et al.. (2019). Boosted Density Estimation Remastered. International Conference on Machine Learning. 1416–1425.1 indexed citations
Nock, Richard, et al.. (2019). A Primal-Dual link between GANs and Autoencoders. Neural Information Processing Systems. 32. 413–422.1 indexed citations
7.
Menon, Aditya Krishna, et al.. (2018). Monge blunts Bayes: Hardness Results for Adversarial Training.. ANU Open Research (Australian National University). 1406–1415.1 indexed citations
8.
Patrini, Giorgio, Richard Nock, Stephen Hardy, & Tibério S. Caetano. (2016). Fast learning from distributed datasets without entity matching. ANU Open Research (Australian National University). 1909–1917.
9.
Nock, Richard, et al.. (2011). On tracking portfolios with certainty equivalents on a generalization of Markowitz model: the Fool, the Wise and the Adaptive. International Conference on Machine Learning. 73–80.4 indexed citations
Nock, Richard & Frank Nielsen. (2008). On the Efficient Minimization of Classification Calibrated Surrogates. Neural Information Processing Systems. 21. 1201–1208.9 indexed citations
12.
Nock, Richard & Frank Nielsen. (2008). Bregman Divergences and Surrogates for Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 31(11). 2048–2059.21 indexed citations
13.
Nielsen, Frank & Richard Nock. (2007). Fast Graph Segmentation Based on Statistical Aggregation Phenomena.. Machine Vision and Applications. 150–153.2 indexed citations
14.
Nock, Richard & Frank Nielsen. (2006). A Real generalization of discrete AdaBoost. European Conference on Artificial Intelligence. 509–515.3 indexed citations
15.
Sebban, Marc, Richard Nock, & Stéphane Lallich. (2003). Stopping criterion for boosting based data reduction techniques: from binary to multiclass problem. Journal of Machine Learning Research. 3. 863–885.24 indexed citations
16.
Sebban, Marc, Richard Nock, & Stéphane Lallich. (2001). Boosting Neighborhood-Based Classifiers. International Conference on Machine Learning. 505–512.3 indexed citations
17.
Sebban, Marc & Richard Nock. (2000). Instance Pruning as an Information Preserving Problem. International Conference on Machine Learning. 855–862.11 indexed citations
18.
Sebban, Marc, et al.. (2000). Impact of learning set quality and size on decision tree performances.. 1. 85–105.31 indexed citations
19.
Nock, Richard, et al.. (1998). On the Power of Decision Lists. International Conference on Machine Learning. 413–420.4 indexed citations
20.
Nock, Richard, et al.. (1996). Negative Robust Learning Results from Horn Claus Programs.. International Conference on Machine Learning. 258–265.2 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.