Quantized Frame Expansions In A Wireless Environment. - Aurélie C. Lozano; Jelena Kovacevic; Mike Andrews (DCC, 2002)

A wireless network can achieve maximum throughput without each node meeting all others. - Aurélie C. Lozano; Sanjeev R. Kulkarni (ISIT, 2005)

Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. - Aurélie C. Lozano; Sanjeev R. Kulkarni; Robert E. Schapire (NIPS, 2005)

Convergence and Consistency of Recursive Boosting. - Aurélie C. Lozano; Sanjeev R. Kulkarni (ISIT, 2006)

Throughput scaling in wireless networks with restricted mobility. - Aurélie C. Lozano; Sanjeev R. Kulkarni; Pramod Viswanath (IEEE Trans. Wirel. Commun., 2007)

Multi-class cost-sensitive boosting with p-norm loss functions. - Aurélie C. Lozano; Naoki Abe (KDD, 2008)

A data modeling approach to climate change attribution. - Aurélie C. Lozano (KDD Workshop on Knowledge Discovery from Sensor Data, 2009)

Grouped graphical Granger modeling methods for temporal causal modeling. - Aurélie C. Lozano; Naoki Abe; Yan Liu 002; Saharon Rosset (KDD, 2009)

Spatial-temporal causal modeling for climate change attribution. - Aurélie C. Lozano; Hongfei Li; Alexandru Niculescu-Mizil; Yan Liu 002; Claudia Perlich; Jonathan R. M. Hosking; Naoki Abe (KDD, 2009)

Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction. - Aurélie C. Lozano; Grzegorz Swirszcz; Naoki Abe (NIPS, 2009)

Proximity-Based Anomaly Detection Using Sparse Structure Learning. - Tsuyoshi Idé; Aurélie C. Lozano; Naoki Abe; Yan Liu 002 (SDM, 2009)

Grouped graphical Granger modeling for gene expression regulatory networks discovery. - Aurélie C. Lozano; Naoki Abe; Yan Liu 002; Saharon Rosset (Bioinform., 2009)

Learning Temporal Causal Graphs for Relational Time-Series Analysis. - Yan Liu 002; Alexandru Niculescu-Mizil; Aurélie C. Lozano; Yong Lu (ICML, 2010)

Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference. - Aurélie C. Lozano; Vikas Sindhwani (NIPS, 2010)

Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels. - Vikas Sindhwani; Aurélie C. Lozano (NIPS, 2011)

Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery. - Yan Liu 002; Alexandru Niculescu-Mizil; Aurélie C. Lozano; Yong Lu (J. Bioinform. Comput. Biol., 2011)

Group Orthogonal Matching Pursuit for Logistic Regression. - Aurélie C. Lozano; Grzegorz Swirszcz; Naoki Abe (AISTATS, 2011)

Multi-level Lasso for Sparse Multi-task Regression. - Aurélie C. Lozano; Grzegorz Swirszcz (ICML, 2012)

A Bayesian Markov-switching Model for Sparse Dynamic Network Estimation. - Huijing Jiang; Aurélie C. Lozano; Fei Liu (SDM, 2012)

Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference - Vikas Sindhwani; Aurélie C. Lozano; Ha Quang Minh (CoRR, 2012)

A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions. - Prabhanjan Kambadur; Aurélie C. Lozano (AISTATS, 2013)

Robust sparse estimation of multiresponse regression and inverse covariance matrix via the L2 distance. - Aurélie C. Lozano; Huijing Jiang; Xinwei Deng (KDD, 2013)

Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. - Vikas Sindhwani; Ha Quang Minh; Aurélie C. Lozano (UAI, 2013)

Orthogonal Matching Pursuit for Sparse Quantile Regression. - Aleksandr Y. Aravkin; Aurélie C. Lozano; Ronny Luss; Prabhanjan Kambadur (ICDM, 2014)

Elementary Estimators for High-Dimensional Linear Regression. - Eunho Yang; Aurélie C. Lozano; Pradeep Ravikumar (ICML, 2014)

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. - Eunho Yang; Aurélie C. Lozano; Pradeep Ravikumar (ICML, 2014)

Elementary Estimators for Graphical Models. - Eunho Yang; Aurélie C. Lozano; Pradeep Ravikumar (NIPS, 2014)

Sparse Quantile Huber Regression for Efficient and Robust Estimation. - Aleksandr Y. Aravkin; Anju Kambadur; Aurélie C. Lozano; Ronny Luss (CoRR, 2014)

Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations. - Aurélie C. Lozano; Sanjeev R. Kulkarni; Robert E. Schapire (IEEE Trans. Inf. Theory, 2014)

Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso. - Eunho Yang; Aurélie C. Lozano (NIPS, 2015)

Closed-form Estimators for High-dimensional Generalized Linear Models. - Eunho Yang; Aurélie C. Lozano; Pradeep Ravikumar (NIPS, 2015)

Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion. - Jialei Wang; Peder A. Olsen; Andrew R. Conn; Aurélie C. Lozano (CVPR, 2016)

Stable estimation of Granger-causal factors of country-level innovation. - Aurélie C. Lozano; Prasanna Sattigeri; Aleksandra Mojsilovic; Kush R. Varshney (GlobalSIP, 2016)

Understanding Innovation to Drive Sustainable Development. - Prasanna Sattigeri; Aurélie C. Lozano; Aleksandra Mojsilovic; Kush R. Varshney; Mahmoud Naghshineh (CoRR, 2016)

An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations. - Seunghak Lee; Aurélie C. Lozano; Prabhanjan Kambadur; Eric P. Xing (J. Comput. Biol., 2016)

Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity. - Eunho Yang; Aurélie C. Lozano (ICML, 2017)

Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. - Sahil Garg; Irina Rish; Guillermo A. Cecchi; Aurélie C. Lozano (IJCAI, 2017)

Learning Task Clusters via Sparsity Grouped Multitask Learning. - Meghana Kshirsagar 001; Eunho Yang; Aurélie C. Lozano (ECML/PKDD (2), 2017)

Generalized Kalman smoothing: Modeling and algorithms. - Aleksandr Y. Aravkin; James V. Burke; Lennart Ljung; Aurélie C. Lozano; Gianluigi Pillonetto (Autom., 2017)

How to foster innovation: A data-driven approach to measuring economic competitiveness. - Caitlin Kuhlman; Karthikeyan Natesan Ramamurthy; Prasanna Sattigeri; Aurélie C. Lozano; Lei Cao 004; C. Reddy; Aleksandra Mojsilovic; Kush R. Varshney (IBM J. Res. Dev., 2017)

On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm. - Tsui-Wei Weng; Huan Zhang 001; Pin-Yu Chen; Aurélie C. Lozano; Cho-Jui Hsieh; Luca Daniel (GlobalSIP, 2018)

Trimming the $ ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning. - Jihun Yun; Peng Zheng 002; Eunho Yang; Aurélie C. Lozano; Aleksandr Y. Aravkin (ICML, 2019)

Stochastic Gradient Methods with Block Diagonal Matrix Adaptation. - Jihun Yun; Aurélie C. Lozano; Eunho Yang (CoRR, 2019)

A graph Laplacian prior for Bayesian variable selection and grouping. - Sounak Chakraborty; Aurélie C. Lozano (Comput. Stat. Data Anal., 2019)

Simultaneous Parameter Learning and Bi-clustering for Multi-Response Models. - Ming Yu 005; Karthikeyan Natesan Ramamurthy; Addie M. Thompson; Aurélie C. Lozano (Frontiers Big Data, 2019)

A General Family of Stochastic Proximal Gradient Methods for Deep Learning. - Jihun Yun; Aurélie C. Lozano; Eunho Yang (CoRR, 2020)

Adaptive Proximal Gradient Methods for Structured Neural Networks. - Jihun Yun; Aurélie C. Lozano; Eunho Yang (NeurIPS, 2021)

Benchmarking deep generative models for diverse antibody sequence design. - Igor Melnyk; Payel Das; Vijil Chenthamarakshan; Aurélie C. Lozano (CoRR, 2021)

Directed Graph Auto-Encoders. - Georgios Kollias; Vasileios Kalantzis; Tsuyoshi Idé; Aurélie C. Lozano; Naoki Abe (AAAI, 2022)

AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning. - Jihun Yun; Aurélie C. Lozano; Eunho Yang (AISTATS, 2022)

AlphaFold Distillation for Improved Inverse Protein Folding. - Igor Melnyk; Aurélie C. Lozano; Payel Das; Vijil Chenthamarakshan (CoRR, 2022)

Direction Aware Positional and Structural Encoding for Directed Graph Neural Networks. - Yonas Sium; Georgios Kollias; Tsuyoshi Idé; Payel Das; Naoki Abe; Aurélie C. Lozano; Qi Li 012 (ICASSP, 2023)

Protein Representation Learning by Geometric Structure Pretraining. - Zuobai Zhang; Minghao Xu; Arian Rokkum Jamasb; Vijil Chenthamarakshan; Aurélie C. Lozano; Payel Das; Jian Tang 005 (ICLR, 2023)

Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction. - Zuobai Zhang; Minghao Xu; Aurélie C. Lozano; Vijil Chenthamarakshan; Payel Das; Jian Tang 005 (NeurIPS, 2023)

Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction. - Zuobai Zhang; Minghao Xu; Aurélie C. Lozano; Vijil Chenthamarakshan; Payel Das; Jian Tang 005 (CoRR, 2023)

Enhancing Protein Language Models with Structure-based Encoder and Pre-training. - Zuobai Zhang; Minghao Xu; Vijil Chenthamarakshan; Aurélie C. Lozano; Payel Das; Jian Tang 005 (CoRR, 2023)

Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops. - Aurélie C. Lozano; Hantian Ding; Naoki Abe; Alexander E. Lipka (BMC Bioinform., 2023)

NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. - Amit Dhurandhar; Tejaswini Pedapati; Ronny Luss; Soham Dan; Aurélie C. Lozano; Payel Das; Georgios Kollias (ACL (Findings), 2024)

Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. - Dongxia Wu; Tsuyoshi Idé; Georgios Kollias; Jirí Navrátil 001; Aurélie C. Lozano; Naoki Abe; Yi-An Ma; Rose Yu (AISTATS, 2024)

Larimar: Large Language Models with Episodic Memory Control. - Payel Das; Subhajit Chaudhury; Elliot Nelson; Igor Melnyk; Sarathkrishna Swaminathan; Sihui Dai; Aurélie C. Lozano; Georgios Kollias; Vijil Chenthamarakshan; Jirí Navrátil 001; Soham Dan; Pin-Yu Chen (ICML, 2024)

Multi-Scale Representation Learning for Protein Fitness Prediction. - Zuobai Zhang; Pascal Notin; Yining Huang; Aurélie C. Lozano; Vijil Chenthamarakshan; Debora S. Marks; Payel Das; Jian Tang 005 (NeurIPS, 2024)

Structure-Informed Protein Language Model. - Zuobai Zhang; Jiarui Lu; Vijil Chenthamarakshan; Aurélie C. Lozano; Payel Das; Jian Tang 005 (CoRR, 2024)

ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation. - Zuobai Zhang; Jiarui Lu; Vijil Chenthamarakshan; Aurélie C. Lozano; Payel Das; Jian Tang 005 (CoRR, 2024)

Larimar: Large Language Models with Episodic Memory Control. - Payel Das; Subhajit Chaudhury; Elliot Nelson; Igor Melnyk; Sarath Swaminathan; Sihui Dai; Aurélie C. Lozano; Georgios Kollias; Vijil Chenthamarakshan; Jirí Navrátil 001; Soham Dan; Pin-Yu Chen (CoRR, 2024)

Can Memory-Augmented Language Models Generalize on Reasoning-in-a-Haystack Tasks? - Payel Das; Ching-Yun Ko; Sihui Dai; Georgios Kollias; Subhajit Chaudhury; Aurélie C. Lozano (CoRR, 2025)

Aligning Protein Conformation Ensemble Generation with Physical Feedback. - Jiarui Lu; Xiaoyin Chen; Stephen Zhewen Lu; Aurélie C. Lozano; Vijil Chenthamarakshan; Payel Das; Jian Tang 005 (CoRR, 2025)