Mario Marchand
About
In The Last Decade
Mario Marchand
55 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 135
- Artificial Intelligence 607
- Molecular Biology 356
- Computer Vision and Pattern Recognition 175
- Computational Theory and Mathematics 116
- Spectroscopy 108
Countries citing papers authored by Mario Marchand
This map shows the geographic impact of Mario Marchand'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 Mario Marchand with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mario Marchand more than expected).
Fields of papers citing papers by Mario Marchand
This network shows the impact of papers produced by Mario Marchand. 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 Mario Marchand. The network helps show where Mario Marchand may publish in the future.
Co-authorship network of co-authors of Mario Marchand
This figure shows the co-authorship network connecting the top 25 collaborators of Mario Marchand. A scholar is included among the top collaborators of Mario Marchand 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 Mario Marchand. Mario Marchand is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees | 2 |
| 2 | Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction | 2 |
| 3 | Sequential model-based ensemble optimization | 2 |
| 4 | Agnostic Bayesian Learning of Ensembles | 14 |
| 5 | Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output Prediction | 5 |
| 6 | A PAC-Bayes Sample-compression Approach to Kernel Methods | 8 |
| 7 | From PAC-Bayes Bounds to Quadratic Programs for Majority Votes | 21 |
| 8 | From PAC-Bayes Bounds to KL Regularization | 7 |
| 9 | Comparing GPLVM approaches for dimensionality reduction in character animation | 10 |
| 10 | 2 | |
| 11 | PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers | 16 |
| 12 | Learning with Decision Lists of Data-Dependent Features | 25 |
| 13 | A PAC-Bayes approach to the Set Covering Machine | 3 |
| 14 | PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data | 9 |
| 15 | The set covering machine with data-dependent half-spaces | 5 |
| 16 | 68 | |
| 17 | The Decision List Machine | 14 |
| 18 | On learning simple deterministic and probabilistic neural concepts | 0 |
| 19 | On Learning µ-Perceptron Networks with Binary Weights | 7 |
| 20 | Learning by Minimizing Resources in Neural Networks | 15 |
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.