François Laviolette
About
In The Last Decade
François Laviolette
68 papers receiving 2.8k citations
Hit Papers
Peers
Comparison fields: 5 of 177
- Molecular Biology 921
- Biomedical Engineering 863
- Artificial Intelligence 549
- Cognitive Neuroscience 483
- Human-Computer Interaction 401
Countries citing papers authored by François Laviolette
This map shows the geographic impact of François Laviolette'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 François Laviolette with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites François Laviolette more than expected).
Fields of papers citing papers by François Laviolette
This network shows the impact of papers produced by François Laviolette. 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 François Laviolette. The network helps show where François Laviolette may publish in the future.
Co-authorship network of co-authors of François Laviolette
This figure shows the co-authorship network connecting the top 25 collaborators of François Laviolette. A scholar is included among the top collaborators of François Laviolette 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 François Laviolette. François Laviolette is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 75 | |
| 3 | A Column Generation Bound Minimization Approach with PAC-Bayesian Generalization Guarantees | 2 |
| 4 | 5 | |
| 5 | PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers PAC-Bayesian Theorems for Multiview Learning | 1 |
| 6 | Algorithms for the Hard Pre-Image Problem of String Kernels and the General Problem of String Prediction | 2 |
| 7 | Optimizing Question-Answering Systems Using Genetic Algorithms | 0 |
| 8 | 41 | |
| 9 | {PAC-Bayesian Theory for Transductive Learning} | 8 |
| 10 | Sequential model-based ensemble optimization | 2 |
| 11 | Accelerated robust point cloud registration in natural environments through positive and unlabeled learning | 6 |
| 12 | 1 | |
| 13 | 381 | |
| 14 | From PAC-Bayes Bounds to KL Regularization | 7 |
| 15 | A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning | 11 |
| 16 | 2 | |
| 17 | PAC-Bayes Risk Bounds for Stochastic Averages and Majority Votes of Sample-Compressed Classifiers | 16 |
| 18 | A PAC-Bayes approach to the Set Covering Machine | 3 |
| 19 | 14 | |
| 20 | 1 |
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.