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ORCIDAurélie C. Lozano
Person information
- affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY, USA
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Journal Articles
- 2023
[j10]Aurélie C. Lozano, Hantian Ding, Naoki Abe, Alexander E. Lipka:
Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops. BMC Bioinform. 24(1): 399 (2023)- 2019
- [j9]Sounak Chakraborty, Aurélie C. Lozano:
A graph Laplacian prior for Bayesian variable selection and grouping. Comput. Stat. Data Anal. 136: 72-91 (2019) - [j8]Ming Yu, Karthikeyan Natesan Ramamurthy, Addie M. Thompson, Aurélie C. Lozano:
Simultaneous Parameter Learning and Bi-clustering for Multi-Response Models. Frontiers Big Data 2: 27 (2019) - 2017
- [j7]Aleksandr Y. Aravkin, James V. Burke, Lennart Ljung, Aurélie C. Lozano, Gianluigi Pillonetto:
Generalized Kalman smoothing: Modeling and algorithms. Autom. 86: 63-86 (2017) - [j6]Caitlin Kuhlman, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Aurélie C. Lozano, Lei Cao
, C. Reddy, Aleksandra Mojsilovic, Kush R. Varshney:
How to foster innovation: A data-driven approach to measuring economic competitiveness. IBM J. Res. Dev. 61(6): 11:1-11:12 (2017) - 2016
- [j5]Seunghak Lee, Aurélie C. Lozano, Prabhanjan Kambadur, Eric P. Xing:
An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations. J. Comput. Biol. 23(5): 372-389 (2016) - 2014
- [j4]Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire:
Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations. IEEE Trans. Inf. Theory 60(1): 651-660 (2014) - 2011
- [j3]Yan Liu, Alexandru Niculescu-Mizil, Aurélie C. Lozano, Yong Lu:
Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery. J. Bioinform. Comput. Biol. 9(2): 231-250 (2011) - 2009
- [j2]Aurélie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset:
Grouped graphical Granger modeling for gene expression regulatory networks discovery. Bioinform. 25(12) (2009) - 2007
- [j1]Aurélie C. Lozano, Sanjeev R. Kulkarni, Pramod Viswanath:
Throughput scaling in wireless networks with restricted mobility. IEEE Trans. Wirel. Commun. 6(2): 670-679 (2007)
Conference and Workshop Papers
- 2024
- [c44]Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias:
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. ACL (Findings) 2024: 2416-2430 - [c43]Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu:
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. AISTATS 2024: 415-423 - [c42]Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarathkrishna Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen:
Larimar: Large Language Models with Episodic Memory Control. ICML 2024 - 2023
- [c41]Yonas Sium, Georgios Kollias, Tsuyoshi Idé, Payel Das, Naoki Abe, Aurélie C. Lozano, Qi Li:
Direction Aware Positional and Structural Encoding for Directed Graph Neural Networks. ICASSP 2023: 1-5 - [c40]Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Protein Representation Learning by Geometric Structure Pretraining. ICLR 2023 - [c39]Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang:
Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction. NeurIPS 2023 - 2022
- [c38]Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé, Aurélie C. Lozano, Naoki Abe:
Directed Graph Auto-Encoders. AAAI 2022: 7211-7219 - [c37]Jihun Yun, Aurélie C. Lozano, Eunho Yang:
AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning. AISTATS 2022: 2574-2606 - 2021
- [c36]Jihun Yun, Aurélie C. Lozano, Eunho Yang:
Adaptive Proximal Gradient Methods for Structured Neural Networks. NeurIPS 2021: 24365-24378 - 2019
- [c35]Jihun Yun, Peng Zheng, Eunho Yang, Aurélie C. Lozano, Aleksandr Y. Aravkin:
Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning. ICML 2019: 7242-7251 - 2018
- [c34]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel:
On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm. GlobalSIP 2018: 1159-1163 - 2017
- [c33]Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano:
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. ICLR (Workshop) 2017 - [c32]Eunho Yang, Aurélie C. Lozano:
Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity. ICML 2017: 3911-3920 - [c31]Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano:
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. IJCAI 2017: 1696-1702 - [c30]Meghana Kshirsagar, Eunho Yang, Aurélie C. Lozano:
Learning Task Clusters via Sparsity Grouped Multitask Learning. ECML/PKDD (2) 2017: 673-689 - 2016
- [c29]Jialei Wang, Peder A. Olsen, Andrew R. Conn, Aurélie C. Lozano:
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion. CVPR 2016: 2754-2763 - [c28]Aurélie C. Lozano, Prasanna Sattigeri, Aleksandra Mojsilovic, Kush R. Varshney:
Stable estimation of Granger-causal factors of country-level innovation. GlobalSIP 2016: 1290-1294 - 2015
- [c27]Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Closed-form Estimators for High-dimensional Generalized Linear Models. NIPS 2015: 586-594 - [c26]Eunho Yang, Aurélie C. Lozano:
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso. NIPS 2015: 2602-2610 - [c25]Seunghak Lee, Aurélie C. Lozano, Prabhanjan Kambadur, Eric P. Xing:
An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations. RECOMB 2015: 167-187 - 2014
- [c24]Aleksandr Y. Aravkin, Aurélie C. Lozano, Ronny Luss, Prabhanjan Kambadur:
Orthogonal Matching Pursuit for Sparse Quantile Regression. ICDM 2014: 11-19 - [c23]Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for High-Dimensional Linear Regression. ICML 2014: 388-396 - [c22]Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. ICML 2014: 397-405 - [c21]Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar:
Elementary Estimators for Graphical Models. NIPS 2014: 2159-2167 - 2013
- [c20]Prabhanjan Kambadur, Aurélie C. Lozano:
A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions. AISTATS 2013: 351-359 - [c19]Aurélie C. Lozano, Huijing Jiang, Xinwei Deng:
Robust sparse estimation of multiresponse regression and inverse covariance matrix via the L2 distance. KDD 2013: 293-301 - [c18]Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano:
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. UAI 2013 - 2012
- [c17]Aurélie C. Lozano, Grzegorz Swirszcz:
Multi-level Lasso for Sparse Multi-task Regression. ICML 2012 - [c16]Huijing Jiang, Aurélie C. Lozano, Fei Liu:
A Bayesian Markov-switching Model for Sparse Dynamic Network Estimation. SDM 2012: 506-515 - 2011
- [c15]Vikas Sindhwani, Aurélie C. Lozano:
Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels. NIPS 2011: 2519-2527 - [c14]Aurélie C. Lozano, Grzegorz Swirszcz, Naoki Abe:
Group Orthogonal Matching Pursuit for Logistic Regression. AISTATS 2011: 452-460 - 2010
- [c13]Yan Liu, Alexandru Niculescu-Mizil, Aurélie C. Lozano, Yong Lu:
Learning Temporal Causal Graphs for Relational Time-Series Analysis. ICML 2010: 687-694 - [c12]Aurélie C. Lozano, Vikas Sindhwani:
Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference. NIPS 2010: 1486-1494 - 2009
- [c11]Aurélie C. Lozano:
A data modeling approach to climate change attribution. KDD Workshop on Knowledge Discovery from Sensor Data 2009: 9 - [c10]Aurélie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset:
Grouped graphical Granger modeling methods for temporal causal modeling. KDD 2009: 577-586 - [c9]Aurélie C. Lozano, Hongfei Li, Alexandru Niculescu-Mizil, Yan Liu, Claudia Perlich, Jonathan R. M. Hosking, Naoki Abe:
Spatial-temporal causal modeling for climate change attribution. KDD 2009: 587-596 - [c8]Aurélie C. Lozano, Grzegorz Swirszcz, Naoki Abe:
Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction. NIPS 2009: 1150-1158 - [c7]Tsuyoshi Idé, Aurélie C. Lozano, Naoki Abe, Yan Liu:
Proximity-Based Anomaly Detection Using Sparse Structure Learning. SDM 2009: 97-108 - 2008
- [c6]Aurélie C. Lozano, Naoki Abe:
Multi-class cost-sensitive boosting with p-norm loss functions. KDD 2008: 506-514 - 2006
- [c5]Aurélie C. Lozano, Sanjeev R. Kulkarni:
Convergence and Consistency of Recursive Boosting. ISIT 2006: 2185-2189 - 2005
- [c4]Aurélie C. Lozano, Sanjeev R. Kulkarni:
A wireless network can achieve maximum throughput without each node meeting all others. ISIT 2005: 2119-2123 - [c3]Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire:
Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. NIPS 2005: 819-826 - 2004
- [c2]Aurélie C. Lozano, Sanjeev R. Kulkarni, Pramod Viswanath:
Throughput scaling in wireless networks with restricted mobility. ISIT 2004: 437 - 2002
- [c1]Aurélie C. Lozano, Jelena Kovacevic, Mike Andrews:
Quantized Frame Expansions In A Wireless Environment. DCC 2002: 232-241
Informal and Other Publications
- 2024
- [i21]Dongxia Wu, Tsuyoshi Idé, Aurélie C. Lozano, Georgios Kollias, Jirí Navrátil, Naoki Abe, Yi-An Ma, Rose Yu:
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. CoRR abs/2402.03726 (2024) - [i20]Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Structure-Informed Protein Language Model. CoRR abs/2402.05856 (2024) - [i19]Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation. CoRR abs/2402.07955 (2024) - [i18]Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarath Swaminathan, Sihui Dai, Aurélie C. Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jirí Navrátil, Soham Dan, Pin-Yu Chen:
Larimar: Large Language Models with Episodic Memory Control. CoRR abs/2403.11901 (2024) - [i17]Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias:
NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. CoRR abs/2404.01306 (2024) - 2023
- [i16]Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang:
Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction. CoRR abs/2301.12068 (2023) - [i15]Zuobai Zhang, Minghao Xu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Enhancing Protein Language Models with Structure-based Encoder and Pre-training. CoRR abs/2303.06275 (2023) - 2022
- [i14]Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé, Aurélie C. Lozano, Naoki Abe:
Directed Graph Auto-Encoders. CoRR abs/2202.12449 (2022) - [i13]Zuobai Zhang, Minghao Xu, Arian R. Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang:
Protein Representation Learning by Geometric Structure Pretraining. CoRR abs/2203.06125 (2022) - [i12]Igor Melnyk, Aurélie C. Lozano, Payel Das, Vijil Chenthamarakshan:
AlphaFold Distillation for Improved Inverse Protein Folding. CoRR abs/2210.03488 (2022) - 2021
- [i11]Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurélie C. Lozano:
Benchmarking deep generative models for diverse antibody sequence design. CoRR abs/2111.06801 (2021) - 2020
- [i10]Jihun Yun, Aurélie C. Lozano, Eunho Yang:
A General Family of Stochastic Proximal Gradient Methods for Deep Learning. CoRR abs/2007.07484 (2020) - 2019
- [i9]Jihun Yun, Aurélie C. Lozano, Eunho Yang:
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation. CoRR abs/1905.10757 (2019) - 2018
- [i8]Ming Yu, Karthikeyan Natesan Ramamurthy, Addie M. Thompson, Aurélie C. Lozano:
Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models. CoRR abs/1804.10961 (2018) - [i7]Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel:
On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm. CoRR abs/1810.08640 (2018) - 2017
- [i6]Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano:
Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. CoRR abs/1701.06106 (2017) - 2016
- [i5]Jialei Wang, Peder A. Olsen, Andrew R. Conn, Aurélie C. Lozano:
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion. CoRR abs/1604.03915 (2016) - [i4]Prasanna Sattigeri, Aurélie C. Lozano, Aleksandra Mojsilovic, Kush R. Varshney, Mahmoud Naghshineh:
Understanding Innovation to Drive Sustainable Development. CoRR abs/1606.06177 (2016) - 2014
- [i3]Aleksandr Y. Aravkin, Anju Kambadur, Aurélie C. Lozano, Ronny Luss:
Sparse Quantile Huber Regression for Efficient and Robust Estimation. CoRR abs/1402.4624 (2014) - [i2]Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano:
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. CoRR abs/1408.2066 (2014) - 2012
- [i1]Vikas Sindhwani, Aurélie C. Lozano, Ha Quang Minh:
Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference. CoRR abs/1210.4792 (2012)
Coauthor Index
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