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.
Recurrent Neural Networks for Multivariate Time Series with Missing Values
Citations per year, relative to David Sontag David Sontag (= 1×)
peers
Bradley Malin
Countries citing papers authored by David Sontag
Since
Specialization
Citations
This map shows the geographic impact of David Sontag'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 Sontag with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Sontag more than expected).
This network shows the impact of papers produced by David Sontag. 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 Sontag. The network helps show where David Sontag may publish in the future.
Co-authorship network of co-authors of David Sontag
This figure shows the co-authorship network connecting the top 25 collaborators of David Sontag.
A scholar is included among the top collaborators of David Sontag 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 Sontag. David Sontag 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.
Agrawal, Monica, Stefan Hegselmann, Hunter Lang, Yoon Kim, & David Sontag. (2022). Large language models are few-shot clinical information extractors. 1998–2022.142 indexed citations breakdown →
Risteski, Andrej, et al.. (2019). Benefits of Overparameterization in Single-Layer Latent Variable Generative Models.. arXiv (Cornell University).1 indexed citations
7.
Krishnan, Rahul G., et al.. (2018). Max-margin learning with the Bayes factor. Uncertainty in Artificial Intelligence. 896–905.1 indexed citations
8.
Krause, Josua, Narges Razavian, Enrico Bertini, & David Sontag. (2015). Visual Exploration of Temporal Data in Electronic Medical Records.. AMIA.1 indexed citations
9.
Krishnan, Rahul G., Simon Lacoste-Julien, & David Sontag. (2015). Barrier Frank-Wolfe for marginal inference. HAL (Le Centre pour la Communication Scientifique Directe). 28. 532–540.1 indexed citations
10.
Weller, Adrian, et al.. (2014). Understanding the Bethe approximation: when and how can it go wrong?. Uncertainty in Artificial Intelligence. 868–877.10 indexed citations
11.
Jernite, Yacine, et al.. (2013). Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests. Neural Information Processing Systems. 26. 2355–2363.7 indexed citations
12.
Halpern, Yoni & David Sontag. (2013). Unsupervised learning of noisy-or Bayesian networks. Uncertainty in Artificial Intelligence. 272–281.7 indexed citations
13.
Sontag, David, et al.. (2013). SparsityBoost: a new scoring function for learning Bayesian network structure. Uncertainty in Artificial Intelligence. 112–121.6 indexed citations
14.
Sontag, David, et al.. (2011). Complexity of Inference in Latent Dirichlet Allocation. Neural Information Processing Systems. 24. 1008–1016.45 indexed citations
15.
Jaakkola, Tommi, David Sontag, Amir Globerson, & Marina Meilă. (2010). Learning Bayesian Network Structure using LP Relaxations. DSpace@MIT (Massachusetts Institute of Technology). 9. 358–365.100 indexed citations
16.
Sontag, David, Ofer Meshi, Amir Globerson, & Tommi Jaakkola. (2010). More data means less inference: A pseudo-max approach to structured learning. DSpace@MIT (Massachusetts Institute of Technology). 23. 2181–2189.10 indexed citations
17.
Meshi, Ofer, David Sontag, Amir Globerson, & Tommi Jaakkola. (2010). Learning Efficiently with Approximate Inference via Dual Losses. DSpace@MIT (Massachusetts Institute of Technology). 783–790.35 indexed citations
18.
Sontag, David & Tommi Jaakkola. (2009). Tree block coordinate descent for map in graphical models. DSpace@MIT (Massachusetts Institute of Technology). 5. 544–551.35 indexed citations
19.
Sontag, David, Amir Globerson, & Tommi Jaakkola. (2008). Clusters and Coarse Partitions in LP Relaxations. neural information processing systems. 21. 1537–1544.11 indexed citations
20.
Sontag, David & Tommi Jaakkola. (2007). New Outer Bounds on the Marginal Polytope. Neural Information Processing Systems. 20. 1393–1400.74 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.