Laetitia Papaxanthos
Laetitia Papaxanthos
PhD Candidate, MLCB, ETH Zürich
Verified email at bsse.ethz.ch
Title
Cited by
Cited by
Year
Fast and memory-efficient significant pattern mining via permutation testing
F Llinares-López, M Sugiyama, L Papaxanthos, K Borgwardt
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015
452015
Finding significant combinations of features in the presence of categorical covariates
L Papaxanthos, F Llinares-López, D Bodenham, K Borgwardt
Advances in Neural Information Processing Systems, 2279-2287, 2016
242016
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
M Le, S Roller, L Papaxanthos, D Kiela, M Nickel
arXiv preprint arXiv:1902.00913, 2019
212019
Genome-wide genetic heterogeneity discovery with categorical covariates
F Llinares-López, L Papaxanthos, D Bodenham, D Roqueiro, ...
Bioinformatics 33 (12), 1820-1828, 2017
132017
CASMAP: detection of statistically significant combinations of SNPs in association mapping
F Llinares-López, L Papaxanthos, D Roqueiro, D Bodenham, K Borgwardt
Bioinformatics 35 (15), 2680-2682, 2019
62019
Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping
S Hoellerer, L Papaxanthos, AC Gumpinger, K Fischer, C Beisel, ...
bioRxiv, 2020
12020
The system can't perform the operation now. Try again later.
Articles 1–6