Mark Schmidt
Impact in
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Artificial Intelligence top 2%
- Bayesian Modeling and Causal Inference
- Machine Learning and Algorithms
- Stochastic Gradient Optimization Techniques
- Machine Learning and Data Classification
Papers in
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- Stochastic Gradient Optimization Techniques 9
- Machine Learning and Algorithms 8
- Gaussian Processes and Bayesian Inference 6
- Bayesian Modeling and Causal Inference 5
- Domain Adaptation and Few-Shot Learning 4
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- Sparse and Compressive Sensing Techniques 9
- Co-authors
- Kevin P. Murphy (4 shared papers)Nicol N. Schraudolph (1 shared paper)Kevin Murphy (1 shared paper)S. V. N. Vishwanathan (1 shared paper)Michael P. Friedlander (2 shared papers)Kevin J. Murphy (2 shared papers)E. van den Berg (1 shared paper)Alexandru Niculescu-Mizil (1 shared paper)
- Journals
- Machine Learning (1 paper)Computational Biology and Chemistry (1 paper)Journal of Computers (1 paper)Goldschmidt2021 abstracts (1 paper)arXiv (Cornell University) (8 papers)
- Partner nations
- CanadaUnited StatesGermany
In The Last Decade
Mark Schmidt
37 papers receiving 919 citations
Peers
Comparison fields: 5 of 109
- Computer Vision and Pattern Recognition 351
- Artificial Intelligence 537
- Neurology 124
- Computer Science Applications 76
- Computational Mathematics 6
Countries citing papers authored by Mark Schmidt
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
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-authors
The 25 scholars most cited alongside Mark Schmidt, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 37 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2006 | 194 | |
| 2 | Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm | 2009 | 147 |
| 3 | Modeling annotator expertise: Learning when everybody knows a bit of something | 2010 | 113 |
| 4 | Learning graphical model structure using L1-regularization paths | 2007 | 100 |
| 5 | 2007 | 79 | |
| 6 | 2006 | 60 | |
| 7 | A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets | 2012 | 34 |
| 8 | Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials | 2010 | 32 |
| 9 | 2020 | 31 | |
| 10 | 2005 | 20 | |
| 11 | Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection | 2015 | 20 |
| 12 | Stop wasting my gradients: practical SVRG | 2015 | 15 |
| 13 | 2009 | 15 | |
| 14 | 2012 | 13 | |
| 15 | 2012 | 13 | |
| 16 | 2021 | 12 | |
| 17 | 2006 | 11 | |
| 18 | 2010 | 10 | |
| 19 | Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | 2012 | 9 |
| 20 | An interior-point stochastic approximation method and an L1-regularized delta rule | 2008 | 8 |
About Mark Schmidt
Mark Schmidt is a scholar working on Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, Statistics and Probability and Management Science and Operations Research, having authored 37 papers that have together received 1.0k indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (9 papers), Sparse and Compressive Sensing Techniques (9 papers), Machine Learning and Algorithms (8 papers), Gaussian Processes and Bayesian Inference (6 papers), Statistical Methods and Inference (5 papers), Bayesian Modeling and Causal Inference (5 papers), Medical Image Segmentation Techniques (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (351 citations), Artificial Intelligence (537 citations), Neurology (124 citations), Computer Science Applications (76 citations) and Computational Mathematics (6 citations). Mark Schmidt has collaborated with scholars based in Canada, United States and Germany. Frequent co-authors include Kevin P. Murphy, Nicol N. Schraudolph, Kevin Murphy, S. V. N. Vishwanathan, Michael P. Friedlander, Kevin J. Murphy, E. van den Berg, Alexandru Niculescu-Mizil, Albert Murtha and Dana Cobzaş. Their work appears in journals such as Machine Learning, Computational Biology and Chemistry, Journal of Computers, Goldschmidt2021 abstracts and arXiv (Cornell University).
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.