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.
This map shows the geographic impact of Ofer Dekel'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 Ofer Dekel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ofer Dekel more than expected).
This network shows the impact of papers produced by Ofer Dekel. 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 Ofer Dekel. The network helps show where Ofer Dekel may publish in the future.
Co-authorship network of co-authors of Ofer Dekel
This figure shows the co-authorship network connecting the top 25 collaborators of Ofer Dekel.
A scholar is included among the top collaborators of Ofer Dekel 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 Ofer Dekel. Ofer Dekel is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Dekel, Ofer, et al.. (2017). Online Learning with a Hint. DSpace@MIT (Massachusetts Institute of Technology). 30. 5299–5308.3 indexed citations
Dekel, Ofer & Elad Hazan. (2013). Better Rates for Any Adversarial Deterministic MDP. International Conference on Machine Learning. 675–683.7 indexed citations
4.
Dekel, Ofer, Claudio Gentile, & Karthik Sridharan. (2012). Selective sampling and active learning from single and multiple teachers. Journal of Machine Learning Research. 13(1). 2655–2697.38 indexed citations
5.
Dekel, Ofer & Ohad Shamir. (2012). There’s a Hole in My Data Space: Piecewise Predictors for Heterogeneous Learning Problems. International Conference on Artificial Intelligence and Statistics. 291–298.6 indexed citations
6.
Dekel, Ofer, et al.. (2011). Bundle Selling by Online Estimation of Valuation Functions. International Conference on Machine Learning. 1137–1144.3 indexed citations
7.
Dekel, Ofer, Ran Gilad-Bachrach, Ohad Shamir, & Lin Xiao. (2011). Optimal Distributed Online Prediction. International Conference on Machine Learning. 713–720.39 indexed citations
8.
Dekel, Ofer, Claudio Gentile, & Karthik Sridharan. (2010). Robust Selective Sampling from Single and Multiple Teachers.. Conference on Learning Theory. 346–358.27 indexed citations
9.
Agarwal, Alekh, Ofer Dekel, & Lin Xiao. (2010). Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback.. Conference on Learning Theory. 28–40.121 indexed citations
10.
Dekel, Ofer & Ohad Shamir. (2010). Multiclass-Multilabel Classification with More Classes than Examples.. International Conference on Artificial Intelligence and Statistics. 9. 137–144.35 indexed citations
11.
Dekel, Ofer & Ohad Shamir. (2009). Vox Populi: Collecting High-Quality Labels from a Crowd. Conference on Learning Theory.93 indexed citations
Dekel, Ofer. (2008). From Online to Batch Learning with Cutoff-Averaging. Neural Information Processing Systems. 21. 377–384.14 indexed citations
14.
Dekel, Ofer, et al.. (2007). A Boosting Algorithm for Label Covering in Multilabel Problems. International Conference on Artificial Intelligence and Statistics. 27–34.10 indexed citations
Dekel, Ofer & Yoram Singer. (2002). Multiclass Learning by Probabilistic Embeddings. Neural Information Processing Systems. 15. 969–1000.24 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.