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.
Discrete temporal models of social networks
2010326 citationsSteve Hanneke, Eric P. Xing et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Steve Hanneke'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 Steve Hanneke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Steve Hanneke more than expected).
This network shows the impact of papers produced by Steve Hanneke. 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 Steve Hanneke. The network helps show where Steve Hanneke may publish in the future.
Co-authorship network of co-authors of Steve Hanneke
This figure shows the co-authorship network connecting the top 25 collaborators of Steve Hanneke.
A scholar is included among the top collaborators of Steve Hanneke 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 Steve Hanneke. Steve Hanneke is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hanneke, Steve & Liu Yang. (2021). Toward a General Theory of Online Selective Sampling: Trading Off Mistakes and Queries. International Conference on Artificial Intelligence and Statistics. 3997–4005.1 indexed citations
Hanneke, Steve, et al.. (2019). VC Classes are Adversarially Robustly Learnable, but Only Improperly. Conference on Learning Theory. 2512–2530.1 indexed citations
6.
Hanneke, Steve & Aryeh Kontorovich. (2018). A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes.. 489–505.4 indexed citations
7.
Hanneke, Steve, Adam Tauman Kalai, Gautam Kamath, & Christos Tzamos. (2018). Actively Avoiding Nonsense in Generative Models. Conference on Learning Theory. 209–227.1 indexed citations
Hanneke, Steve, et al.. (2015). A compression technique for analyzing disagreement-based active learning. Journal of Machine Learning Research. 16(1). 713–745.4 indexed citations
11.
Hanneke, Steve & Lin F. Yang. (2015). Statistical Learning under Nonstationary Mixing Processes. International Conference on Artificial Intelligence and Statistics. 1678–1686.1 indexed citations
12.
Hanneke, Steve & Lin F. Yang. (2015). Minimax analysis of active learning. arXiv (Cornell University). 16(1). 3487–3602.15 indexed citations
Yang, Lin F. & Steve Hanneke. (2013). Activized Learning with Uniform Classification Noise. International Conference on Machine Learning. 370–378.2 indexed citations
15.
Hanneke, Steve & Lin F. Yang. (2010). Negative Results for Active Learning with Convex Losses. International Conference on Artificial Intelligence and Statistics. 321–325.5 indexed citations
16.
Hanneke, Steve & Eric P. Xing. (2009). Network Completion and Survey Sampling. International Conference on Artificial Intelligence and Statistics. 209–215.18 indexed citations
17.
Hanneke, Steve. (2009). Adaptive Rates of Convergence in Active Learning.. Conference on Learning Theory.20 indexed citations
18.
Hanneke, Steve. (2009). Theoretical foundations of active learning.41 indexed citations
19.
Balcan, Maria-Florina, Steve Hanneke, & Jennifer R. Wortman. (2008). The True Sample Complexity of Active Learning.. Conference on Learning Theory. 45–56.36 indexed citations
20.
Hanneke, Steve & Dan Roth. (2004). Iterative Labeling for Semi-Supervised Learning. Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign). 65(1). 7–17.3 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.