Aurelie C. Lozano  Aurelie C. Lozano photo       

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Research Staff Member -- Machine Learning
Thomas J. Watson Research Center, Yorktown Heights, NY USA
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2017

Sparsity grouped multitask learning for drug molecular activity prediction
Meghana Kshirsagar, Eunho Yang, Aurelie C. Lozano
ICML Workshop on Computational Biology, 2017


Generalized Kalman Smoothing: Modeling and Algorithms
A.Y. Aravkin, J.V. Burke, L. Ljung, A. Lozano, G. Pillonetto
Automatica, 2017

Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World
Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurelie Lozano
International Joint Conference on Artificial Intelligence (IJCAI-2017). Extended verion arXiv:1701.06106



2016

Minimum Distance Lasso for robust high-dimensional regression
Aurelie C Lozano, Nicolai Meinshausen and Eunho Yang
Electronic Journal of Statistics 10(1), 1296--1340, The Institute of Mathematical Statistics and the Bernoulli Society, 2016

Temporal Causal Modeling
P. Kambadur, A.C. Lozano, R. Luss
Financial Signal Processing and Machine Learning, John Wiley & Sons, 2016

Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
Wang, Jialei and Olsen, Peder A and Conn, Andrew R and Lozano, Aurelie C
29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016

An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations
S. Lee, A. Lozano, P. Kambadur, and E. P. Xing
Journal of Computational Biology 23 (5), 372-389, 2016

Understanding Innovation to Drive Sustainable Development
P. Sattigeri, A.C. Lozano, A. Mojsilovic, K.R. Varshney and M. Naghshineh
ICML Workshop #Data4Good: Machine Learning in Social Good Applications, 2016


2015

Variable-Selection Emerges on Top in Empirical Comparison of Whole-Genome Complex-Trait Prediction Methods
David C. Haws, Irina Rish, Simon Teyssedre, Dan He, Aurelie C. Lozano, Prabhanjan Kambadur, Zivan Karaman, Laxmi Parida
PloS one 10(10), 2015

Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Eunho Yang and Aurelie C. Lozano
NIPS, 2015

Closed-form Estimators for High-dimensional Generalized Linear Models
Eunho Yang, Aurelie C. Lozano, Pradeep Ravikumar
NIPS (Selected as Spotlight), 2015

An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations
S. Lee, A. Lozano, P. Kambadur, and E. P. Xing
19th International Conference on Research in Computational Molecular Biology (RECOMB 2015)



2014

Orthogonal Matching Pursuit for Sparse Quantile Regression.
A.Y. Aravkin, A. Kambadur, A.C. Lozano, and R. Luss
To appear in IEEE International Conference on Data Mining (ICDM), 2014

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments
E. Yang, A.C. Lozano, P. Ravikumar
International Conference on Machine Learning (ICML), 2014

Elementary Estimators for High-Dimensional Linear Regression
E. Yang, A.C. Lozano, P. Ravikumar
International Conference on Machine Learning (ICML), 2014

Elementary Estimators for Graphical Models
E. Yang, A.C. Lozano, P. Ravikumar
Neural Information Processing Systems Conference (NIPS), 2014

Practical Applications of Sparse Modeling
A book edited by I. Rish, G. Cecchi, A.C. Lozano, A. Niculecu-Mizil
MIT Press, 2014

Bayesian Regularization via Graph Laplacian
F. Liu, S. Chakraborty, F. Li, Y. Liu and A. Lozano
Bayesian Analysis , 2014

Convergence and Consistency of Regularized Boosting with Weakly Dependent Observations
Aurelie C. Lozano, Sanjeev R. Kulkarni and Robert E. Schapire
IEEE Transactions on Information Theory , 2014


2013



Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference.
Vikas Sindhwani, Aurelie C. Lozano, Ha Quang Minh
Uncertainty in Artifical Intelligence (UAI) 2013. UAI Best Paper award. IBM Pat Goldberg Memorial Best Paper Award 2013.


2012


A Bayesian Markov-switching Model for Sparse Dynamic Network Estimation
H. Jiang, A.Lozano, F. Liu
SDM 2012: Proceedings of 2012 SIAM International Conference on Data Mining


2011

Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery
Y. Liu, A. Niculescu-Mizil, A. Lozano, Y. Lu
J Bioinform Comput Biol 8(2), 231-250, 2011

Group Orthogonal Matching Pursuit for Logistic Regression
Aurelie Lozano, Grzegorz Swirszcz, Naoki Abe;
Journal of Machine Learning Research, AISTATS15, 452-460, 2011

Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels
V Sindhwani, A C Lozano
Proc. of the Neural Information Processing Systems Conference (NIPS), 2011


2010

Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference
A C Lozano, V Sindhwani
Proc. of the Neural Information Processing Systems Conference (NIPS), 2010.

Learning Temporal Causal Graphs for Relational Time-Series Analysis
Y Liu, A Niculescu-Mizil, A Lozano
International conference on Machine Learning (ICML), 2010

Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery
Y Liu, A Niculescu-Mizil, A Lozano, Y Lu
International conference on Computational Systems Bioinformatics (CSB), 2010


2009

Grouped graphical Granger modeling methods for temporal causal modeling
Aurelie C Lozano, Naoki Abe, Yan Liu, Saharon Rosset
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 577--586, ACM, 2009
Abstract

Spatial-temporal causal modeling for climate change attribution
Aurelie C Lozano, Hongfei Li, Alexandru Niculescu-Mizil, Yan Liu, Claudia Perlich, Jonathan Hosking, Naoki Abe
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 587--596, ACM, 2009
Abstract

Proximity-Based Anomaly Detection using Sparse Structure Learning
\bf Tsuyoshi Id\'e, Aurelie C. Lozano, Naoki Abe, Yan Liu
Proceedings of 2009 SIAM International Conference on Data Mining (SDM 09), pp. 97--108

Grouped graphical Granger modeling for gene expression regulatory networks discovery
A C Lozano, N Abe, Y Liu, S Rosset
Bioinformatics 25(12), i110, Oxford Univ Press, 2009

A data modeling approach to climate change attribution
A Lozano
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, pp. 9, 2009

Group Orthogonal Matching Pursuit for Variable Selection and Prediction
A C Lozano, G Swirszcz, N Abe
Proc. of the Neural Information Processing Systems Conference (NIPS), 2009.


2008

Introduction to Boosting: Origin, Practice and Recent Developments
N Abe, A C Lozano
Forum Math for Industry, Tokyo, Japan, 2008

Multi-class cost-sensitive boosting with p-norm loss functions
Aurelie C Lozano, Naoki Abe
Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 506--514, ACM, 2008
Abstract