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.
Object Detection with Discriminatively Trained Part-Based Models
20096.6k citationsPedro F. Felzenszwalb, Ross Girshick et al.profile →
Countries citing papers authored by David McAllester
Since
Specialization
Citations
This map shows the geographic impact of David McAllester'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 David McAllester with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David McAllester more than expected).
Fields of papers citing papers by David McAllester
This network shows the impact of papers produced by David McAllester. 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 David McAllester. The network helps show where David McAllester may publish in the future.
Co-authorship network of co-authors of David McAllester
This figure shows the co-authorship network connecting the top 25 collaborators of David McAllester.
A scholar is included among the top collaborators of David McAllester 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 David McAllester. David McAllester 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.
Neyshabur, Behnam, Srinadh Bhojanapalli, David McAllester, & Nathan Srebro. (2017). Exploring Generalization in Deep Learning. Neural Information Processing Systems. 30. 5947–5956.139 indexed citations
2.
Keshet, Joseph & David McAllester. (2011). Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss. Neural Information Processing Systems. 24. 2205–2212.30 indexed citations
3.
Girshick, Ross, Pedro F. Felzenszwalb, & David McAllester. (2010). LSVM-MDPM Release 4 Notes.1 indexed citations
4.
Bilmes, Jeff, Andrew Y. Ng, & David McAllester. (2009). Uncertainty in artificial intelligence : proceedings of the Twenty-fifth Conference (2009) : June 18-21, 2009, Montreal, Quebec.1 indexed citations
5.
Altün, Yasemin, David McAllester, & Mikhail Belkin. (2005). Margin Semi-Supervised Learning for Structured Variables.. Neural Information Processing Systems. 33–40.6 indexed citations
6.
McAllester, David & Robert E. Schapire. (2003). Learning theory and language modeling. Morgan Kaufmann Publishers Inc. eBooks. 271–287.3 indexed citations
7.
Ortiz, Luis E. & David McAllester. (2002). Concentration Inequalities for the Missing Mass and for Histogram Rule Error. Neural Information Processing Systems. 15. 367–374.1 indexed citations
8.
McAllester, David & Robert E. Schapire. (2000). On the Convergence Rate of Good-Turing Estimators. Conference on Learning Theory. 1–6.71 indexed citations
9.
Mansour, Yishay & David McAllester. (2000). Generalization Bounds for Decision Trees. Conference on Learning Theory. 69–74.32 indexed citations
McAllester, David, Bart Selman, & Henry Kautz. (1997). Evidence for invariants in local search. National Conference on Artificial Intelligence. 321–326.184 indexed citations
14.
McAllester, David, et al.. (1997). Exploiting Variable Dependency in Local Search.10 indexed citations
15.
Kautz, Henry, David McAllester, & Bart Selman. (1996). Encoding plans in propositional logic. Principles of Knowledge Representation and Reasoning. 374–384.175 indexed citations
16.
Siskind, Jeffrey Mark & David McAllester. (1993). Nondeterministic lisp as a substrate for constraint logic programming. National Conference on Artificial Intelligence. 133–138.36 indexed citations
17.
McAllester, David & David Rosenblitt. (1991). Systematic nonlinear planning. DSpace@MIT (Massachusetts Institute of Technology). 634–639.345 indexed citations
18.
Givan, Robert, et al.. (1991). Natural Language Based Inference Procedures Applied to Schubert''s Steamroller. DSpace@MIT (Massachusetts Institute of Technology). 915–920.46 indexed citations
19.
McAllester, David. (1990). Truth maintenance. National Conference on Artificial Intelligence. 1109–1116.73 indexed citations
20.
Elkan, Charles & David McAllester. (1988). Automated Inductive Reasoning about Logic Programs.. International Conference on Lightning Protection. 876–892.6 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.