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.
Fairness through awareness
20121.6k citationsCynthia Dwork, Moritz Hardt et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Toniann Pitassi
Since
Specialization
Citations
This map shows the geographic impact of Toniann Pitassi'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 Toniann Pitassi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Toniann Pitassi more than expected).
This network shows the impact of papers produced by Toniann Pitassi. 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 Toniann Pitassi. The network helps show where Toniann Pitassi may publish in the future.
Co-authorship network of co-authors of Toniann Pitassi
This figure shows the co-authorship network connecting the top 25 collaborators of Toniann Pitassi.
A scholar is included among the top collaborators of Toniann Pitassi 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 Toniann Pitassi. Toniann Pitassi is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Creager, Elliot, David Madras, Toniann Pitassi, & Richard S. Zemel. (2020). Causal Modeling for Fairness In Dynamical Systems. International Conference on Machine Learning. 1. 2185–2195.1 indexed citations
3.
Göös, Mika, et al.. (2020). Automating algebraic proof systems is NP-Hard. Electronic colloquium on computational complexity. 27(64). 64.2 indexed citations
Madras, David, Toniann Pitassi, & Richard S. Zemel. (2017). Predict Responsibly: Increasing Fairness by Learning To Defer. arXiv (Cornell University).2 indexed citations
6.
Dwork, Cynthia, Vitaly Feldman, Moritz Hardt, et al.. (2017). Guilt-free data reuse. Communications of the ACM. 60(4). 86–93.10 indexed citations
7.
Göös, Mika, T. S. Jayram, Toniann Pitassi, & Thomas Watson. (2015). Randomized Communication vs. Partition Number.. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 22. 169.6 indexed citations
8.
Göös, Mika, Toniann Pitassi, & Thomas Watson. (2014). Zero-Information Protocols and Unambiguity in Arthur-Merlin Communication.. Electronic colloquium on computational complexity. 21. 78.1 indexed citations
9.
Filmus, Yuval, et al.. (2013). Average Case Lower Bounds for Monotone Switching Networks.. Electronic colloquium on computational complexity. 20. 54.1 indexed citations
10.
Martens, James, Arkadev Chattopadhyay, Toniann Pitassi, & Richard S. Zemel. (2013). On the Expressive Power of Restricted Boltzmann Machines.. Neural Information Processing Systems. 2877–2885.4 indexed citations
11.
Chattopadhyay, Arkadev, Jeff Edmonds, Faith Ellen, & Toniann Pitassi. (2012). A little advice can be very helpful. Symposium on Discrete Algorithms. 615–625.3 indexed citations
12.
McGregor, Andrew, Ilya Mironov, Toniann Pitassi, et al.. (2011). The Limits of Two-Party Differential Privacy.. Electronic colloquium on computational complexity. 18. 106.18 indexed citations
Bacchus, Fahiem, et al.. (2008). Clause learning can effectively P-simulate general propositional resolution. National Conference on Artificial Intelligence. 283–290.20 indexed citations
15.
Pitassi, Toniann, et al.. (2007). Black-White Pebbling is PSPACE-Complete. Electronic colloquium on computational complexity. 14.1 indexed citations
16.
Sang, Tian, Fahiem Bacchus, Paul Beame, Henry Kautz, & Toniann Pitassi. (2004). Combining Component Caching and Clause Learning for Effective Model Counting..96 indexed citations
17.
Bacchus, Fahiem, et al.. (2003). DPLL with Caching: A new algorithm for #SAT and Bayesian Inference. Electronic colloquium on computational complexity. 10.14 indexed citations
18.
Galesi, Nicola, et al.. (2003). Rank Bounds and Integrality Gaps for Cutting Planes Procedures Joshua.. 318–327.
19.
Mitchell, David G. M., et al.. (2001). Linear and Negative Resolution are weaker than Resolution. Electronic colloquium on computational complexity. 8.2 indexed citations
20.
Zemel, Richard S. & Toniann Pitassi. (2000). A Gradient-Based Boosting Algorithm for Regression Problems. Neural Information Processing Systems. 13. 696–702.69 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.