Lisa Hellerstein

1.7k total citations
62 papers, 897 citations indexed

About

Lisa Hellerstein is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Lisa Hellerstein has authored 62 papers receiving a total of 897 indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Artificial Intelligence, 39 papers in Computational Theory and Mathematics and 14 papers in Computer Networks and Communications. Recurrent topics in Lisa Hellerstein's work include Machine Learning and Algorithms (43 papers), Algorithms and Data Compression (24 papers) and Complexity and Algorithms in Graphs (23 papers). Lisa Hellerstein is often cited by papers focused on Machine Learning and Algorithms (43 papers), Algorithms and Data Compression (24 papers) and Complexity and Algorithms in Graphs (23 papers). Lisa Hellerstein collaborates with scholars based in United States, Canada and Israel. Lisa Hellerstein's co-authors include Marek Karpiński, Nader H. Bshouty, Dana Angluin, Richard M. Karp, Garth A. Gibson, David A. Patterson, Thomas R. Hancock, Amol Deshpande, Vijay Raghavan and Randy H. Katz and has published in prestigious journals such as Operations Research, Journal of the ACM and Machine Learning.

In The Last Decade

Lisa Hellerstein

60 papers receiving 825 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Lisa Hellerstein United States 18 594 421 311 79 45 62 897
Anooshiravan Saboori United States 15 379 0.6× 783 1.9× 557 1.8× 79 1.0× 31 0.7× 19 966
Hans van Maaren Netherlands 11 274 0.5× 294 0.7× 174 0.6× 34 0.4× 31 0.7× 33 520
Marijn J. H. Heule United States 13 346 0.6× 325 0.8× 159 0.5× 77 1.0× 19 0.4× 61 617
Jeffery Westbrook United States 17 223 0.4× 356 0.8× 479 1.5× 50 0.6× 56 1.2× 34 766
Robert Mateescu United States 11 401 0.7× 165 0.4× 386 1.2× 35 0.4× 35 0.8× 31 672
Gérard Tel Netherlands 9 172 0.3× 174 0.4× 517 1.7× 107 1.4× 29 0.6× 20 662
Tsunehiko Kameda Canada 15 217 0.4× 255 0.6× 524 1.7× 163 2.1× 17 0.4× 56 817
Kurt Rohloff United States 19 521 0.9× 314 0.7× 353 1.1× 37 0.5× 22 0.5× 56 887
J. Bitner United States 7 266 0.4× 218 0.5× 243 0.8× 88 1.1× 56 1.2× 18 585
Y. Bartal Israel 13 179 0.3× 354 0.8× 654 2.1× 88 1.1× 82 1.8× 24 938

Countries citing papers authored by Lisa Hellerstein

Since Specialization
Citations

This map shows the geographic impact of Lisa Hellerstein'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 Lisa Hellerstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lisa Hellerstein more than expected).

Fields of papers citing papers by Lisa Hellerstein

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Lisa Hellerstein. 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 Lisa Hellerstein. The network helps show where Lisa Hellerstein may publish in the future.

Co-authorship network of co-authors of Lisa Hellerstein

This figure shows the co-authorship network connecting the top 25 collaborators of Lisa Hellerstein. A scholar is included among the top collaborators of Lisa Hellerstein 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 Lisa Hellerstein. Lisa Hellerstein 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.
Hellerstein, Lisa, et al.. (2021). A Tight Bound for Stochastic Submodular Cover. Journal of Artificial Intelligence Research. 71. 347–370. 1 indexed citations
2.
Hellerstein, Lisa, et al.. (2018). Recursive Feature Elimination by Sensitivity Testing. PubMed. 2018. 40–47. 42 indexed citations
3.
Hellerstein, Lisa, et al.. (2018). Revisiting the Approximation Bound for Stochastic Submodular Cover. Journal of Artificial Intelligence Research. 63. 265–279. 3 indexed citations
4.
Deshpande, Amol, et al.. (2016). Approximation Algorithms for Stochastic Submodular Set Cover with Applications to Boolean Function Evaluation and Min-Knapsack. ACM Transactions on Algorithms. 12(3). 1–28. 14 indexed citations
5.
Hellerstein, Lisa, et al.. (2014). Evaluation of DNF Formulas. Sabanci University. 1 indexed citations
6.
Deshpande, Amol, et al.. (2014). Approximation algorithms for stochastic boolean function evaluation and stochastic submodular set cover. Symposium on Discrete Algorithms. 1453–1467. 20 indexed citations
7.
Hellerstein, Lisa, et al.. (2012). On the gap betweeness(f)andcnf_size(f). Discrete Applied Mathematics. 161(1-2). 19–27. 2 indexed citations
8.
Hellerstein, Lisa, et al.. (2009). Exploiting Product Distributions to Identify Relevant Variables of Correlation Immune Functions. Journal of Machine Learning Research. 10(83). 2374–2411. 1 indexed citations
9.
Hellerstein, Lisa & Rocco A. Servedio. (2007). On PAC learning algorithms for rich Boolean function classes. Theoretical Computer Science. 384(1). 66–76. 4 indexed citations
10.
Allender, Eric, et al.. (2005). Minimizing DNF Formulas and AC0 Circuits Given a Truth Table. Electronic colloquium on computational complexity. 7 indexed citations
11.
Hellerstein, Lisa & Vijay Raghavan. (2004). Exact learning of DNF formulas using DNF hypotheses. Journal of Computer and System Sciences. 70(4). 435–470. 8 indexed citations
12.
Hellerstein, Lisa. (2001). On generalized constraints and certificates. Discrete Mathematics. 226(1-3). 211–232. 12 indexed citations
13.
Foldes, Stephan, et al.. (2000). Equational characterizations of Boolean function classes. Discrete Mathematics. 211(1-3). 27–51. 31 indexed citations
14.
Bshouty, Nader H., Thomas R. Hancock, & Lisa Hellerstein. (1995). Learning Boolean Read-Once Formulas over Generalized Bases. Journal of Computer and System Sciences. 50(3). 521–542. 23 indexed citations
15.
Blum, Avrim, Lisa Hellerstein, & N. Littlestone. (1995). Learning in the Presence of Finitely or Infinitely Many Irrelevant Attributes. Journal of Computer and System Sciences. 50(1). 32–40. 36 indexed citations
16.
Hellerstein, Lisa & Collette R. Coullard. (1994). Learning binary matroid ports. Symposium on Discrete Algorithms. 328–335. 2 indexed citations
17.
Blum, Avrim, Lisa Hellerstein, & Nick Littlestone. (1991). Learning in the presence of finitely or infinitely many irrelevant attributes. Conference on Learning Theory. 157–166. 11 indexed citations
18.
Hellerstein, Lisa & Marek Karpiński. (1989). Learning read-once formulas using membership queries. Conference on Learning Theory. 146–161. 14 indexed citations
19.
Gibson, Garth A., Lisa Hellerstein, Richard M. Karp, & David A. Patterson. (1989). Failure correction techniques for large disk arrays. ACM SIGARCH Computer Architecture News. 17(2). 123–132. 47 indexed citations
20.
Hellerstein, Lisa & Ehud Shapiro. (1984). Implementing Parallel Algorithms in Concurrent Prolog: The MAXFLOW Experience.. MIT Press eBooks. 258–115. 5 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.

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