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-dispersed polyoxometalate clusters embedded on multilayer graphene as a bifunctional electrocatalyst for efficient Li-S batteries
2022239 citationsXiaoxiang Fan, Pan Xu et al.profile →
A dicarbonate solvent electrolyte for high performance 5 V-Class Lithium-based batteries
202478 citationsPan Xu, Xiaodong Lin et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Pan Xu'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 Pan Xu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pan Xu more than expected).
This network shows the impact of papers produced by Pan Xu. 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 Pan Xu. The network helps show where Pan Xu may publish in the future.
Co-authorship network of co-authors of Pan Xu
This figure shows the co-authorship network connecting the top 25 collaborators of Pan Xu.
A scholar is included among the top collaborators of Pan Xu 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 Pan Xu. Pan Xu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Tang, Jing, et al.. (2021). Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5065–5073.5 indexed citations
9.
Zou, Difan, Pan Xu, & Quanquan Gu. (2019). Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction. Neural Information Processing Systems. 32. 3830–3841.2 indexed citations
Xu, Pan, Tianhao Wang, & Quanquan Gu. (2018). Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions. International Conference on Machine Learning. 5492–5501.5 indexed citations
15.
Xu, Pan, Tianhao Wang, & Quanquan Gu. (2018). Accelerated Stochastic Mirror Descent: From Continuous-time Dynamics to Discrete-time Algorithms.. International Conference on Artificial Intelligence and Statistics. 1087–1096.2 indexed citations
16.
Zhou, Dongruo, Pan Xu, & Quanquan Gu. (2018). Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization.. Neural Information Processing Systems. 3925–3936.11 indexed citations
17.
Xu, Pan, Jian Ma, & Quanquan Gu. (2017). Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization. Neural Information Processing Systems. 30. 1933–1944.4 indexed citations
18.
Xu, Pan, et al.. (2017). Uncertainty Assessment and False Discovery Rate Control in High-Dimensional Granger Causal Inference. International Conference on Machine Learning. 684–693.3 indexed citations
19.
Xu, Pan, Tingting Zhang, & Quanquan Gu. (2017). Efficient Algorithm for Sparse Tensor-variate Gaussian Graphical Models via Gradient Descent.. International Conference on Artificial Intelligence and Statistics. 923–932.4 indexed citations
20.
Xu, Pan & Quanquan Gu. (2016). Semiparametric Differential Graph Models. Neural Information Processing Systems. 29. 1064–1072.12 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.