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.
Single-cell mRNA quantification and differential analysis with Census
20171.1k citationsXiaojie Qiu, Andrew J. Hill et al.Nature Methodsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Yi-An Ma'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 Yi-An Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yi-An Ma more than expected).
This network shows the impact of papers produced by Yi-An Ma. 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 Yi-An Ma. The network helps show where Yi-An Ma may publish in the future.
Co-authorship network of co-authors of Yi-An Ma
This figure shows the co-authorship network connecting the top 25 collaborators of Yi-An Ma.
A scholar is included among the top collaborators of Yi-An Ma 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 Yi-An Ma. Yi-An Ma is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bhatia, Kush, Yi-An Ma, Anca D. Dragan, Peter L. Bartlett, & Michael I. Jordan. (2023). Bayesian Robustness: A Nonasymptotic Viewpoint. Journal of the American Statistical Association. 119(546). 1112–1123.
Wu, Dongxia, Matteo Chinazzi, Alessandro Vespignani, Yi-An Ma, & Rose Yu. (2022). Multi-fidelity Hierarchical Neural Processes. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2029–2038.4 indexed citations
5.
Mou, Wenlong, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, & Michael I. Jordan. (2021). High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm. arXiv (Cornell University). 22(42). 1–41.15 indexed citations
6.
Jerfel, Ghassen, et al.. (2021). Variational Refinement for Importance Sampling Using the Forward Kullback-Leibler Divergence. arXiv (Cornell University).2 indexed citations
Pacchiano, Aldo, et al.. (2020). On Thompson Sampling with Langevin Algorithms. CaltechAUTHORS (California Institute of Technology). 1.1 indexed citations
10.
Wang, Xin, Fisher Yu, Yi-An Ma, et al.. (2020). Deep Mixture of Experts via Shallow Embedding. Uncertainty in Artificial Intelligence. 552–562.16 indexed citations
11.
Hoffman, Matthew D. & Yi-An Ma. (2020). Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics. International Conference on Machine Learning. 1. 4324–4341.1 indexed citations
12.
Pacchiano, Aldo, et al.. (2020). On Approximate Thompson Sampling with Langevin Algorithms.. International Conference on Machine Learning. 6797–6807.2 indexed citations
13.
Ma, Yi-An, Nicholas J. Foti, & Emily B. Fox. (2017). Stochastic gradient MCMC methods for hidden Markov models. International Conference on Machine Learning. 2265–2274.1 indexed citations
14.
Qiu, Xiaojie, Andrew J. Hill, Jonathan S. Packer, et al.. (2017). Single-cell mRNA quantification and differential analysis with Census. Nature Methods. 14(3). 309–315.1069 indexed citations breakdown →
15.
Ma, Yi-An, Tianqi Chen, & Emily B. Fox. (2015). A complete recipe for stochastic gradient MCMC. Neural Information Processing Systems. 28. 2917–2925.28 indexed citations
Yuan, Ruoshi, Yi-An Ma, Bo Yuan, & Ping Ao. (2011). Potential function in dynamical systems and the relation with Lyapunov function. Chinese Control Conference. 6573–6580.5 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.