Mark Schmidt

5.7k total citations
37 papers, 1.0k citations indexed

About

Mark Schmidt is a scholar working on Artificial Intelligence, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mark Schmidt has authored 37 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Artificial Intelligence, 9 papers in Computational Mechanics and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mark Schmidt's work include Sparse and Compressive Sensing Techniques (9 papers), Stochastic Gradient Optimization Techniques (9 papers) and Machine Learning and Algorithms (8 papers). Mark Schmidt is often cited by papers focused on Sparse and Compressive Sensing Techniques (9 papers), Stochastic Gradient Optimization Techniques (9 papers) and Machine Learning and Algorithms (8 papers). Mark Schmidt collaborates with scholars based in Canada, United States and Germany. Mark Schmidt's co-authors include Kevin P. Murphy, Kevin Murphy, S. V. N. Vishwanathan, Nicol N. Schraudolph, Michael P. Friedlander, Kevin J. Murphy, E. van den Berg, Alexandru Niculescu-Mizil, Albert Murtha and Dana Cobzaş and has published in prestigious journals such as Machine Learning, Computational Biology and Chemistry and Journal of Computers.

In The Last Decade

Mark Schmidt

37 papers receiving 919 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mark Schmidt Canada	14	537	351	144	124	76	37	1.0k
Yuhui Zheng China	20	412 0.8×	959 2.7×	89 0.6×	53 0.4×	15 0.2×	97	1.6k
Dorina Thanou Switzerland	14	775 1.4×	220 0.6×	140 1.0×	13 0.1×	10 0.1×	34	1.2k
Nicolas Courty France	17	514 1.0×	580 1.7×	57 0.4×	20 0.2×	6 0.1×	52	1.3k
Chi Wang United States	19	878 1.6×	703 2.0×	49 0.3×	19 0.2×	17 0.2×	72	1.9k
Jyri Kivinen United Kingdom	6	646 1.2×	663 1.9×	62 0.4×	17 0.1×	6 0.1×	8	1.5k
Zengchang Qin China	20	735 1.4×	850 2.4×	41 0.3×	43 0.3×	10 0.1×	77	1.6k
Dan Feldman Israel	17	472 0.9×	283 0.8×	119 0.8×	19 0.2×	41 0.5×	87	1.0k
Alexander Ilin Finland	18	531 1.0×	390 1.1×	79 0.5×	9 0.1×	9 0.1×	53	1.3k
Thanh Minh Nguyen Canada	22	493 0.9×	782 2.2×	140 1.0×	34 0.3×	3 0.0×	51	1.3k
Francesco Masulli Italy	18	786 1.5×	491 1.4×	24 0.2×	34 0.3×	6 0.1×	113	1.5k

Countries citing papers authored by Mark Schmidt

Since Specialization

Citations

This map shows the geographic impact of Mark Schmidt'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 Mark Schmidt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark Schmidt more than expected).

Fields of papers citing papers by Mark Schmidt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark Schmidt. 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 Mark Schmidt. The network helps show where Mark Schmidt may publish in the future.

Co-authorship network of co-authors of Mark Schmidt

This figure shows the co-authorship network connecting the top 25 collaborators of Mark Schmidt. A scholar is included among the top collaborators of Mark Schmidt 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 Mark Schmidt. Mark Schmidt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Dannenberg, Frits, et al.. (2023). Predicting DNA kinetics with a truncated continuous-time Markov chain method. Computational Biology and Chemistry. 104. 107837–107837. 2 indexed citations

Vaswani, Sharan, et al.. (2022). SVRG meets AdaGrad: painless variance reduction. Machine Learning. 111(12). 4359–4409. 4 indexed citations

Geilert, Sonja, et al.. (2021). Kinetics of olivine weathering in seawater: an experimental study. Goldschmidt2021 abstracts. 5 indexed citations

Laradji, Issam, Negar Rostamzadeh, Pedro O. Pinheiro, David Vázquez, & Mark Schmidt. (2020). Proposal-Based Instance Segmentation With Point Supervision. 2126–2130. 31 indexed citations

Schmidt, Mark, et al.. (2019). Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets. International Conference on Artificial Intelligence and Statistics. 2077–2086. 1 indexed citations

Vaswani, Sharan, Branislav Kveton, Zheng Wen, et al.. (2017). Diffusion Independent Semi-Bandit Influence Maximization.. arXiv (Cornell University). 4 indexed citations

Vaswani, Sharan, Branislav Kveton, Zheng Wen, et al.. (2017). Model-Independent Online Learning for Influence Maximization. arXiv (Cornell University). 3530–3539. 6 indexed citations

Schmidt, Mark, et al.. (2015). Stop wasting my gradients: practical SVRG. Neural Information Processing Systems. 28. 2251–2259. 15 indexed citations

Schmidt, Mark, et al.. (2015). Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields. ANU Open Research (Australian National University). 819–828. 6 indexed citations

10.

Schmidt, Mark. (2014). Convergence rate of stochastic gradient with constant step size. Open Collections. 4 indexed citations

11.

Lacoste-Julien, Simon, Martin Jaggi, Mark Schmidt, & Patrick Pletscher. (2012). Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs. arXiv (Cornell University). 9 indexed citations

12.

Schmidt, Mark & Karteek Alahari. (2011). Generalized fast approximate energy minimization via graph cuts: α-expansion β-shrink moves. Uncertainty in Artificial Intelligence. 653–660. 5 indexed citations

13.

Schmidt, Mark & Kevin P. Murphy. (2010). Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials. International Conference on Artificial Intelligence and Statistics. 709–716. 32 indexed citations

14.

Yan, Yan, Rómer Rosales, Glenn Fung, et al.. (2010). Modeling annotator expertise: Learning when everybody knows a bit of something. International Conference on Artificial Intelligence and Statistics. 932–939. 113 indexed citations

15.

Schmidt, Mark. (2010). Graphical model structure learning using L₁-regularization. Open Collections. 10 indexed citations

16.

Schmidt, Mark, E. van den Berg, Michael P. Friedlander, & Kevin P. Murphy. (2009). Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm. International Conference on Artificial Intelligence and Statistics. 456–463. 147 indexed citations

17.

Cobzaş, Dana & Mark Schmidt. (2009). Increased discrimination in level set methods with embedded conditional random fields. 2009 IEEE Conference on Computer Vision and Pattern Recognition. b 36. 328–335. 15 indexed citations

18.

Carbonetto, Peter, Mark Schmidt, & Nando de Freitas. (2008). An interior-point stochastic approximation method and an L1-regularized delta rule. Oxford University Research Archive (ORA) (University of Oxford). 21. 233–240. 8 indexed citations

19.

Duvenaud, David, Daniel Eaton, Kevin P. Murphy, & Mark Schmidt. (2008). Causal learning without DAGs. Neural Information Processing Systems. 177–190. 4 indexed citations

20.

Schmidt, Mark, Alexandru Niculescu-Mizil, & Kevin J. Murphy. (2007). Learning graphical model structure using L1-regularization paths. National Conference on Artificial Intelligence. 1278–1283. 100 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.

Explore authors with similar magnitude of impact