Mark Schmidt
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- Medical Image Segmentation Techniques 4
- Artificial Intelligence top 2%
- Stochastic Gradient Optimization Techniques 9
- Machine Learning and Algorithms 8
- Gaussian Processes and Bayesian Inference 6
- Bayesian Modeling and Causal Inference 5
- Data Stream Mining Techniques 4
- Neurology top 10%
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- Sparse and Compressive Sensing Techniques 9
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- Statistical Methods and Inference 5
- Co-authors
- Kevin P. MurphyKevin MurphyS. V. N. VishwanathanNicol N. SchraudolphMichael P. FriedlanderKevin J. MurphyE. van den BergAlexandru Niculescu-Mizil
- Journals
- Machine Learning (1 paper)Journal of Computers (1 paper)Computational Biology and Chemistry (1 paper)
- 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-authorship network
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
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 2 | |
| 2 | 2022 | 4 | |
| 3 | 2020 | 31 | |
| 4 | Distributed Maximization of "Submodular plus Diversity" Functions for Multi-label Feature Selection on Huge Datasets | 2019 | 1 |
| 5 | 2019 | 3 | |
| 6 | Diffusion Independent Semi-Bandit Influence Maximization. | 2017 | 4 |
| 7 | 2017 | 6 | |
| 8 | Non-Uniform Stochastic Average Gradient Method for Training Conditional Random Fields | 2015 | 6 |
| 9 | Stop wasting my gradients: practical SVRG | 2015 | 15 |
| 10 | 2014 | 4 | |
| 11 | Stochastic Block-Coordinate Frank-Wolfe Optimization for Structural SVMs | 2012 | 9 |
| 12 | Generalized fast approximate energy minimization via graph cuts: α-expansion β-shrink moves | 2011 | 5 |
| 13 | Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials | 2010 | 32 |
| 14 | Modeling annotator expertise: Learning when everybody knows a bit of something | 2010 | 113 |
| 15 | 2010 | 10 | |
| 16 | Optimizing Costly Functions with Simple Constraints: A Limited-Memory Projected Quasi-Newton Algorithm | 2009 | 147 |
| 17 | 2009 | 15 | |
| 18 | Causal learning without DAGs | 2008 | 4 |
| 19 | An interior-point stochastic approximation method and an L1-regularized delta rule | 2008 | 8 |
| 20 | Learning graphical model structure using L1-regularization paths | 2007 | 100 |
About Mark Schmidt
Mark Schmidt is a scholar working on Statistics and Probability, Artificial Intelligence and Computational Mechanics, having authored 37 papers that have together received 1.0k indexed citations. Recurring topics across this work include Sparse and Compressive Sensing Techniques (9 papers), Stochastic Gradient Optimization Techniques (9 papers), Machine Learning and Algorithms (8 papers), Gaussian Processes and Bayesian Inference (6 papers), Bayesian Modeling and Causal Inference (5 papers), Statistical Methods and Inference (5 papers), Medical Image Segmentation Techniques (4 papers) and Data Stream Mining Techniques (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (351 citations), Artificial Intelligence (537 citations) and Neurology (124 citations). Mark Schmidt has collaborated with scholars based in Canada, United States and Germany. Frequent 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. Their work appears in journals such as Machine Learning, Journal of Computers, Computational Biology and Chemistry, 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.