Mark Schmidt
Impact in
- Artificial Intelligence top 1%
- Stochastic Gradient Optimization Techniques
- Privacy-Preserving Technologies in Data
- Bayesian Modeling and Causal Inference
- Neurology top 5%
- Brain Tumor Detection and Classification
Papers in
-
- Stochastic Gradient Optimization Techniques 16
- Bayesian Modeling and Causal Inference 6
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- Brain Tumor Detection and Classification 6
- Co-authors
- Francis Bach (3 shared papers)Nicolas Le Roux (2 shared papers)Michael P. Friedlander (2 shared papers)Hamed Karimi (1 shared paper)Julie Nutini (1 shared paper)R. Rosales (2 shared papers)Glenn Fung (2 shared papers)Kevin P. Murphy (2 shared papers)
- Journals
- Machine Learning (2 papers)Lecture notes in computer science (10 papers)Proceedings of the IEEE (1 paper)Mathematical Programming (1 paper)Computational Biology and Chemistry (1 paper)
- Partner nations
- CanadaUnited StatesFrance
In The Last Decade
Mark Schmidt
40 papers receiving 2.7k citations
Mark Schmidt's Hit Papers
Peers
Comparison fields: 5 of 112
- Artificial Intelligence 1.3k
- Neurology 211
- Numerical Analysis 124
- Computer Vision and Pattern Recognition 551
- Computational Mathematics 14
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 41 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Minimizing finite sums with the stochastic average gradient Hit paper breakdown → | 2016 | 470 |
| 2 | Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition Hit paper breakdown → | 2016 | 424 |
| 3 | 2007 | 250 | |
| 4 | Hybrid deterministic-stochastic methods for data fitting | 2011 | 213 |
| 5 | 2006 | 210 | |
| 6 | 2018 | 168 | |
| 7 | Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm | 2009 | 153 |
| 8 | Modeling annotator expertise: Learning when everybody knows a bit of something | 2010 | 136 |
| 9 | Learning graphical model structure using L1-regularization paths | 2007 | 112 |
| 10 | 2005 | 106 | |
| 11 | 2007 | 88 | |
| 12 | 2016 | 77 | |
| 13 | 2006 | 61 | |
| 14 | 2005 | 48 | |
| 15 | 2020 | 45 | |
| 16 | Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials | 2010 | 37 |
| 17 | 2020 | 37 | |
| 18 | 2011 | 36 | |
| 19 | A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets | 2012 | 34 |
| 20 | 2005 | 24 |
About Mark Schmidt
Mark Schmidt is a scholar working on Artificial Intelligence, Neurology, Computer Vision and Pattern Recognition, Modeling and Simulation and Genetics, having authored 41 papers that have together received 2.9k indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (16 papers), Brain Tumor Detection and Classification (6 papers), Bayesian Modeling and Causal Inference (6 papers), Medical Image Segmentation Techniques (4 papers), RNA and protein synthesis mechanisms (3 papers), Advanced Neural Network Applications (3 papers), DNA and Biological Computing (3 papers) and Mathematical Biology Tumor Growth (2 papers). The work is most often cited by research in Artificial Intelligence (1.3k citations), Neurology (211 citations), Numerical Analysis (124 citations), Computer Vision and Pattern Recognition (551 citations) and Computational Mathematics (14 citations). Mark Schmidt has collaborated with scholars based in Canada, United States and France. Frequent co-authors include Francis Bach, Nicolas Le Roux, Michael P. Friedlander, Hamed Karimi, Julie Nutini, R. Rosales, Glenn Fung, Kevin P. Murphy, S. V. N. Vishwanathan and Kevin P. Murphy. Their work appears in journals such as Machine Learning, Lecture notes in computer science, Proceedings of the IEEE, Mathematical Programming and Computational Biology and Chemistry.
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.