| human | Q5 |
| P864 | ACM Digital Library author ID | 81100612037 |
| P5361 | BNB person ID | MarchandMario1956- |
| P2456 | DBLP author ID | 01/4590 |
| P1960 | Google Scholar author ID | M792u2sAAAAJ |
| P6479 | IEEE Xplore author ID | 38224163100 |
| P213 | ISNI | 0000000084590968 |
| P8189 | J9U ID | 987007459993405171 |
| P244 | Library of Congress authority ID | nb2006016578 |
| P6634 | LinkedIn personal profile ID | mario-marchand-50460913 |
| P549 | Mathematics Genealogy Project ID | 73000 |
| P1006 | Nationale Thesaurus voor Auteursnamen ID | 321263545 |
| P691 | NL CR AUT ID | xx0051992 |
| P496 | ORCID iD | 0000-0002-7078-7393 |
| P2038 | ResearchGate profile ID | Mario_Marchand |
| P1153 | Scopus author ID | 7102059694 |
| P4012 | Semantic Scholar author ID | 143858557 |
| P214 | VIAF cluster ID | 264089637 |
| P10832 | WorldCat Entities ID | E39PBJdMcdF9cmYhDrdBtJ94bd |
| P27 | country of citizenship | Canada | Q16 |
| P185 | doctoral student | Pascal Germain | Q83497325 |
| Saeed Hadjifaradji | Q102252632 | ||
| Mohak Shah | Q102336809 | ||
| P69 | educated at | University of Sherbrooke | Q2579532 |
| P108 | employer | University of Ottawa | Q627969 |
| Laval University | Q1067935 | ||
| P734 | family name | Marchand | Q12795667 |
| P101 | field of work | machine learning | Q2539 |
| data mining | Q172491 | ||
| bioinformatics | Q128570 | ||
| P735 | given name | Mario | Q3362622 |
| Mario | Q3362622 | ||
| P106 | occupation | university teacher | Q1622272 |
| P21 | sex or gender | male | Q6581097 |
| Q77683906 | A PAC-Bayes Risk Bound for General Loss Functions |
| Q77685612 | A PAC-Bayes approach to the Set Covering Machine |
| Q77678268 | From PAC-Bayes Bounds to KL Regularization |
| Q37087171 | HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels |
| Q64110185 | Interpretable genotype-to-phenotype classifiers with performance guarantees |
| Q77617612 | Learning Stochastic Perceptrons Under k-Blocking Distributions |
| Q34622472 | Learning a peptide-protein binding affinity predictor with kernel ridge regression |
| Q45958426 | MHC-NP: predicting peptides naturally processed by the MHC. |
| Q28545910 | Machine learning assisted design of highly active peptides for drug discovery |
| Q77652063 | Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks |
| Q77591906 | On Learning µ-Perceptron Networks with Binary Weights |
| Q77684028 | PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier |
| Q77688276 | PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data |
| Q64112118 | Predicting Ion Mobility Collision Cross-Sections Using a Deep Neural Network: DeepCCS |
| Q36145457 | Predictive computational phenotyping and biomarker discovery using reference-free genome comparisons. |
| Q77691354 | The Decision List Machine |
Search more.