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.
Learning with Kernels
20016.2k citationsBernhard Schölkopf, Alexander J. SmolaThe MIT Press eBooksprofile →
Nonlinear Component Analysis as a Kernel Eigenvalue Problem
19985.3k citationsBernhard Schölkopf, Alexander J. Smola et al.profile →
Advances in kernel methods: support vector learning
19994.1k citationsBernhard Schölkopf, Christopher J. C. Burges et al.International Conference on Neural Information Processingprofile →
A kernel two-sample test
20121.0k citationsArthur Gretton, Karsten Borgwardt et al.Journal of Machine Learning Researchprofile →
Input space versus feature space in kernel-based methods
1999823 citationsBernhard Schölkopf, Mika Sirén et al.IEEE Transactions on Neural Networksprofile →
ResNeSt: Split-Attention Networks
2022629 citationsHang Zhang, Chongruo Wu et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)profile →
Protein function prediction via graph kernels
2005620 citationsKarsten Borgwardt, Cheng Soon Ong et al.Computer applications in the biosciencesprofile →
Scaling distributed machine learning with the parameter server
2014606 citationsMu Li, David G. Andersen et al.Operating Systems Design and Implementationprofile →
Efficient mini-batch training for stochastic optimization
2014472 citationsAlexander J. Smola et al.profile →
Recurrent Recommender Networks
2017416 citationsAmr Ahmed, Alexander J. Smola et al.profile →
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)
2014297 citationsAlexander J. Smola et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
Countries citing papers authored by Alexander J. Smola
Since
Specialization
Citations
This map shows the geographic impact of Alexander J. Smola'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 Alexander J. Smola with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alexander J. Smola more than expected).
Fields of papers citing papers by Alexander J. Smola
This network shows the impact of papers produced by Alexander J. Smola. 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 Alexander J. Smola. The network helps show where Alexander J. Smola may publish in the future.
Co-authorship network of co-authors of Alexander J. Smola
This figure shows the co-authorship network connecting the top 25 collaborators of Alexander J. Smola.
A scholar is included among the top collaborators of Alexander J. Smola 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 Alexander J. Smola. Alexander J. Smola 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.
Zhang, Hang, Chongruo Wu, Zhongyue Zhang, et al.. (2022). ResNeSt: Split-Attention Networks. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2735–2745.629 indexed citations breakdown →
2.
Zhao, Han, Otilia Stretcu, Alexander J. Smola, & Geoffrey J. Gordon. (2017). Efficient Multitask Feature and Relationship Learning. Uncertainty in Artificial Intelligence. 777–787.1 indexed citations
3.
Zaheer, Manzil, Satwik Kottur, Amr Ahmed, José M. F. Moura, & Alexander J. Smola. (2017). Canopy --- Fast Sampling with Cover Trees. International Conference on Machine Learning. 3977–3986.2 indexed citations
4.
Reddi, Sashank J., Suvrit Sra, Barnabás Póczos, & Alexander J. Smola. (2016). Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization. Neural Information Processing Systems. 29. 1145–1153.43 indexed citations
5.
Wang, Yuxiang, James Sharpnack, Alexander J. Smola, & Ryan J. Tibshirani. (2015). Trend Filtering on Graphs. Journal of Machine Learning Research. 17(1). 1042–1050.10 indexed citations
6.
Li, Mu, David G. Andersen, Jun Woo Park, et al.. (2014). Scaling distributed machine learning with the parameter server. Operating Systems Design and Implementation. 583–598.606 indexed citations breakdown →
7.
Ahmed, Amr, Alexander J. Smola, & Markus Weimer. (2012). WWW 2012 Tutorial: New Templates for Scalable Data Analysis.2 indexed citations
8.
Gretton, Arthur, Karsten Borgwardt, Malte J. Rasch, Bernhard Schölkopf, & Alexander J. Smola. (2012). A kernel two-sample test. Journal of Machine Learning Research. 13(1). 723–773.1025 indexed citations breakdown →
9.
Agarwal, Deepak, Lihong Li, & Alexander J. Smola. (2011). Linear-Time Estimators for Propensity Scores. International Conference on Artificial Intelligence and Statistics. 93–100.11 indexed citations
10.
Karatzoglou, Alexandros, Alexander J. Smola, & Markus Weimer. (2010). Collaborative Filtering on a Budget. International Conference on Artificial Intelligence and Statistics. 389–396.28 indexed citations
11.
Smola, Alexander J., Le Song, & Choon Hui Teo. (2009). Relative Novelty Detection. ANU Open Research (Australian National University). 536–543.32 indexed citations
12.
Hofmann, Thomas, et al.. (2007). Predicting Structured Data (Neural Information Processing). The MIT Press eBooks. 20(4). 630–6.70 indexed citations
Borgwardt, Karsten, Cheng Soon Ong, Stefan Schönauer, et al.. (2005). Protein function prediction via graph kernels. Computer applications in the biosciences. 21(Suppl 1). i47–i56.620 indexed citations breakdown →
15.
Canu, Stéphane & Alexander J. Smola. (2005). Kernel methods and the exponential family. ANU Open Research (Australian National University). 447–454.4 indexed citations
16.
Gretton, Arthur, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, & Bernhard Schölkopf. (2005). Kernel Methods for Measuring Independence. Journal of Machine Learning Research. 6(70). 2075–2129.187 indexed citations
17.
Canu, Stéphane, et al.. (2004). Une boite a outils rapide et simple pour les SVM. 29(1). 58–63.4 indexed citations
18.
Ong, Cheng Soon & Alexander J. Smola. (2003). Machine learning using hyperkernels. ANU Open Research (Australian National University). 568–575.14 indexed citations
19.
Schölkopf, Bernhard, Mika Sirén, Christopher J. C. Burges, et al.. (1999). Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks. 10(5). 1000–1017.823 indexed citations breakdown →
20.
Schölkopf, Bernhard, Alexander J. Smola, & Klaus‐Robert Müller. (1999). Kernel principal component analysis. International Conference on Neural Information Processing. 327–352.278 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.