Published in ISMB 2020, 2020
This paper demonstrate that a case of fully interpretable Deep Learning Model
Recommended citation: Liu Y, Barr K, Reinitz J. (2019). " Fully Interpretable Deep Learning Model of Transcriptional Control[J]." Bioinformatics, Volume 36, Issue Supplement_1, July 2020, Pages i499–i507, Published . 1(3). https://academic.oup.com/bioinformatics/article/36/Supplement_1/i499/5870526
Published in Jounal of America Statistical Association (In Press), Winner of SBSS 2020 Student Paper Competition awarded by ASA, 2020
TVS brings together Bayesian reinforcement and machine learning in order to extend the reach of Bayesian subset selection to non-parametric models and large datasets with very many predictors and/or very many observations.
Recommended citation: Liu Y., Rockova, V. . Variable Selection via Thompson Sampling[J]." arXiv preprint arXiv:2007.00187, 2020. . 1(3). https://yiliu9090.github.io/files/VS_TS.pdf
Published in Journal of Royal Statistical Society Series B (in Press), 2020
This paper develops the methodology for variable selection under non-parametric setting using ABC.
Recommended citation: Liu Y, Ročková V, Wang Y (2020). "Variable Selection with ABC Bayesian Forests." Journal of Royal Statistical Society Series B, In Press . https://arxiv.org/abs/1806.02304
Published in Procedia Computer Science, 2016
we come up with a fast probabilistic algorithm,…, that can determine the similarity between large segments with a higher degree of accuracy than other known methods.
Recommended citation: Javangula, P., Modarre, K., Shenoy, P., Liu, Y., & Nayebi, A. (2017). " Efficient Hybrid Algorithms for Computing Clusters Overlap 1." Procedia Computer Science. 108: 1050-1059. https://www.sciencedirect.com/science/article/pii/S1877050917308050