Mark Schmidt
Impact in
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- Medical Image Segmentation Techniques
- Advanced Neural Network Applications
- Advanced Image and Video Retrieval Techniques
- Artificial Intelligence top 1%
- Stochastic Gradient Optimization Techniques
- Machine Learning and Algorithms
Papers in
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- Stochastic Gradient Optimization Techniques 12
- Machine Learning and Algorithms 9
- Domain Adaptation and Few-Shot Learning 6
- Gaussian Processes and Bayesian Inference 6
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- Sparse and Compressive Sensing Techniques 13
- Co-authors
- Julie Nutini (4 shared papers)Hamed Karimi (1 shared paper)Rómer Rosales (2 shared papers)Glenn Fung (2 shared papers)Kevin P. Murphy (3 shared papers)Kevin Murphy (1 shared paper)S. V. N. Vishwanathan (1 shared paper)Nicol N. Schraudolph (1 shared paper)
- Journals
- Lecture notes in computer science (10 papers)Machine Learning (1 paper)Computational Biology and Chemistry (1 paper)Journal of Computers (1 paper)National Conference on Artificial Intelligence (1 paper)
- Partner nations
- CanadaUnited StatesGermany
In The Last Decade
Mark Schmidt
48 papers receiving 2.0k citations
Mark Schmidt's Hit Papers
Peers
Comparison fields: 5 of 131
- Computer Vision and Pattern Recognition 728
- Artificial Intelligence 1.0k
- Computational Mathematics 16
- Numerical Analysis 144
- Neurology 189
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 49 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hit paper breakdown → | 2016 | 366 |
| 2 | 2007 | 247 | |
| 3 | 2006 | 219 | |
| 4 | Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm | 2009 | 156 |
| 5 | 2018 | 152 | |
| 6 | Modeling annotator expertise: Learning when everybody knows a bit of something | 2010 | 135 |
| 7 | Learning graphical model structure using L1-regularization paths | 2007 | 114 |
| 8 | 2005 | 103 | |
| 9 | 2007 | 89 | |
| 10 | 2006 | 63 | |
| 11 | 2005 | 48 | |
| 12 | 2011 | 38 | |
| 13 | Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials | 2010 | 37 |
| 14 | 2020 | 35 | |
| 15 | A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets | 2012 | 35 |
| 16 | 2005 | 24 | |
| 17 | Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection | 2015 | 20 |
| 18 | 2009 | 18 | |
| 19 | Stop wasting my gradients: practical SVRG | 2015 | 17 |
| 20 | 2012 | 16 |
About Mark Schmidt
Mark Schmidt is a scholar working on Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition, Statistics and Probability and Molecular Biology, having authored 49 papers that have together received 2.1k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (13 papers), Stochastic Gradient Optimization Techniques (12 papers), Machine Learning and Algorithms (9 papers), Domain Adaptation and Few-Shot Learning (6 papers), Gaussian Processes and Bayesian Inference (6 papers), Medical Image Segmentation Techniques (5 papers), Statistical Methods and Inference (5 papers) and Advanced Neural Network Applications (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (728 citations), Artificial Intelligence (1.0k citations), Computational Mathematics (16 citations), Numerical Analysis (144 citations) and Neurology (189 citations). Mark Schmidt has collaborated with scholars based in Canada, United States and Germany. Frequent co-authors include Julie Nutini, Hamed Karimi, Rómer Rosales, Glenn Fung, Kevin P. Murphy, Kevin Murphy, S. V. N. Vishwanathan, Nicol N. Schraudolph, Albert Murtha and Russell Greiner. Their work appears in journals such as Lecture notes in computer science, Machine Learning, Computational Biology and Chemistry, Journal of Computers and National Conference on Artificial Intelligence.
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.