Aurélie C. Lozano

81 papers A* 20A 7B 5Misc 1Journal 35Unranked 13

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Context Engineering Framework for Improving Enterprise AI Agents based on Digital-Twin MDP. CoRR Xi Yang, Aurélie C. Lozano, Naoki Abe, Bhavya, Saurabh Jha, Noah Zheutlin, Rohan R. Arora, Yu Deng, Daby M. Sow
2025	A*	`conf`	Aligning Protein Conformation Ensemble Generation with Physical Feedback. ICML Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
2025	J	`jnl`	Aligning Protein Conformation Ensemble Generation with Physical Feedback. CoRR Jiarui Lu, Xiaoyin Chen, Stephen Zhewen Lu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
2025	J	`jnl`	Can Memory-Augmented Language Models Generalize on Reasoning-in-a-Haystack Tasks? CoRR Payel Das, Ching-Yun Ko, Sihui Dai, Georgios Kollias, Subhajit Chaudhury, Aurélie C. Lozano
2024	A*	`conf`	Larimar: Large Language Models with Episodic Memory Control. ICML 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
2024	J	`jnl`	Larimar: Large Language Models with Episodic Memory Control. CoRR 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
2024	A	`conf`	Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. AISTATS Dongxia Wu, Tsuyoshi Idé, Georgios Kollias, Jirí Navrátil, Aurélie C. Lozano, Naoki Abe, Yi-An Ma, Rose Yu
2024	J	`jnl`	Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes. CoRR Dongxia Wu, Tsuyoshi Idé, Aurélie C. Lozano, Georgios Kollias, Jirí Navrátil, Naoki Abe, Yi-An Ma, Rose Yu
2024	A*	`conf`	Multi-Scale Representation Learning for Protein Fitness Prediction. NeurIPS Zuobai Zhang, Pascal Notin, Yining Huang, Aurélie C. Lozano, Vijil Chenthamarakshan, Debora S. Marks, Payel Das, Jian Tang
2024	J	`jnl`	Multi-Scale Representation Learning for Protein Fitness Prediction. CoRR Zuobai Zhang, Pascal Notin, Yining Huang, Aurélie C. Lozano, Vijil Chenthamarakshan, Debora S. Marks, Payel Das, Jian Tang
2024	—	`conf`	NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. ACL (Findings) Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
2024	J	`jnl`	NeuroPrune: A Neuro-inspired Topological Sparse Training Algorithm for Large Language Models. CoRR Amit Dhurandhar, Tejaswini Pedapati, Ronny Luss, Soham Dan, Aurélie C. Lozano, Payel Das, Georgios Kollias
2024	J	`jnl`	ProtIR: Iterative Refinement between Retrievers and Predictors for Protein Function Annotation. CoRR Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
2024	J	`jnl`	Structure-Informed Protein Language Model. CoRR Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
2023	Misc	`conf`	Direction Aware Positional and Structural Encoding for Directed Graph Neural Networks. ICASSP Yonas Sium, Georgios Kollias, Tsuyoshi Idé, Payel Das, Naoki Abe, Aurélie C. Lozano, Qi Li
2023	J	`jnl`	Enhancing Protein Language Models with Structure-based Encoder and Pre-training. CoRR Zuobai Zhang, Minghao Xu, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
2023	J	`jnl`	Physics-Inspired Protein Encoder Pre-Training via Siamese Sequence-Structure Diffusion Trajectory Prediction. CoRR Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
2023	A*	`conf`	Pre-Training Protein Encoder via Siamese Sequence-Structure Diffusion Trajectory Prediction. NeurIPS Zuobai Zhang, Minghao Xu, Aurélie C. Lozano, Vijil Chenthamarakshan, Payel Das, Jian Tang
2023	A*	`conf`	Protein Representation Learning by Geometric Structure Pretraining. ICLR Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
2023	J	`jnl`	Regularized multi-trait multi-locus linear mixed models for genome-wide association studies and genomic selection in crops. BMC Bioinform. Aurélie C. Lozano, Hantian Ding, Naoki Abe, Alexander E. Lipka
2022	A	`conf`	AdaBlock: SGD with Practical Block Diagonal Matrix Adaptation for Deep Learning. AISTATS Jihun Yun, Aurélie C. Lozano, Eunho Yang
2022	J	`jnl`	AlphaFold Distillation for Improved Inverse Protein Folding. CoRR Igor Melnyk, Aurélie C. Lozano, Payel Das, Vijil Chenthamarakshan
2022	A*	`conf`	Directed Graph Auto-Encoders. AAAI Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé, Aurélie C. Lozano, Naoki Abe
2022	J	`jnl`	Directed Graph Auto-Encoders. CoRR Georgios Kollias, Vasileios Kalantzis, Tsuyoshi Idé, Aurélie C. Lozano, Naoki Abe
2022	J	`jnl`	Protein Representation Learning by Geometric Structure Pretraining. CoRR Zuobai Zhang, Minghao Xu, Arian R. Jamasb, Vijil Chenthamarakshan, Aurélie C. Lozano, Payel Das, Jian Tang
2021	A*	`conf`	Adaptive Proximal Gradient Methods for Structured Neural Networks. NeurIPS Jihun Yun, Aurélie C. Lozano, Eunho Yang
2021	J	`jnl`	Benchmarking deep generative models for diverse antibody sequence design. CoRR Igor Melnyk, Payel Das, Vijil Chenthamarakshan, Aurélie C. Lozano
2020	J	`jnl`	A General Family of Stochastic Proximal Gradient Methods for Deep Learning. CoRR Jihun Yun, Aurélie C. Lozano, Eunho Yang
2019	J	`jnl`	A graph Laplacian prior for Bayesian variable selection and grouping. Comput. Stat. Data Anal. Sounak Chakraborty, Aurélie C. Lozano
2019	J	`jnl`	Simultaneous Parameter Learning and Bi-clustering for Multi-Response Models. Frontiers Big Data Ming Yu, Karthikeyan Natesan Ramamurthy, Addie M. Thompson, Aurélie C. Lozano
2019	J	`jnl`	Stochastic Gradient Methods with Block Diagonal Matrix Adaptation. CoRR Jihun Yun, Aurélie C. Lozano, Eunho Yang
2019	A*	`conf`	Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning. ICML Jihun Yun, Peng Zheng, Eunho Yang, Aurélie C. Lozano, Aleksandr Y. Aravkin
2018	J	`jnl`	On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm. CoRR Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel
2018	—	`conf`	On Extensions of Clever: A Neural Network Robustness Evaluation Algorithm. GlobalSIP Tsui-Wei Weng, Huan Zhang, Pin-Yu Chen, Aurélie C. Lozano, Cho-Jui Hsieh, Luca Daniel
2018	J	`jnl`	Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models. CoRR Ming Yu, Karthikeyan Natesan Ramamurthy, Addie M. Thompson, Aurélie C. Lozano
2017	J	`jnl`	Generalized Kalman smoothing: Modeling and algorithms. Autom. Aleksandr Y. Aravkin, James V. Burke, Lennart Ljung, Aurélie C. Lozano, Gianluigi Pillonetto
2017	J	`jnl`	How to foster innovation: A data-driven approach to measuring economic competitiveness. IBM J. Res. Dev. Caitlin Kuhlman, Karthikeyan Natesan Ramamurthy, Prasanna Sattigeri, Aurélie C. Lozano, Lei Cao, C. Reddy, Aleksandra Mojsilovic, Kush R. Varshney
2017	—	`conf`	Learning Task Clusters via Sparsity Grouped Multitask Learning. ECML/PKDD (2) Meghana Kshirsagar, Eunho Yang, Aurélie C. Lozano
2017	—	`conf`	Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. ICLR (Workshop) Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano
2017	A*	`conf`	Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. IJCAI Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano
2017	J	`jnl`	Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World. CoRR Sahil Garg, Irina Rish, Guillermo A. Cecchi, Aurélie C. Lozano
2017	A*	`conf`	Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity. ICML Eunho Yang, Aurélie C. Lozano
2016	J	`jnl`	An Efficient Nonlinear Regression Approach for Genome-wide Detection of Marginal and Interacting Genetic Variations. J. Comput. Biol. Seunghak Lee, Aurélie C. Lozano, Prabhanjan Kambadur, Eric P. Xing
2016	A*	`conf`	Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion. CVPR Jialei Wang, Peder A. Olsen, Andrew R. Conn, Aurélie C. Lozano
2016	J	`jnl`	Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion. CoRR Jialei Wang, Peder A. Olsen, Andrew R. Conn, Aurélie C. Lozano
2016	—	`conf`	Stable estimation of Granger-causal factors of country-level innovation. GlobalSIP Aurélie C. Lozano, Prasanna Sattigeri, Aleksandra Mojsilovic, Kush R. Varshney
2016	J	`jnl`	Understanding Innovation to Drive Sustainable Development. CoRR Prasanna Sattigeri, Aurélie C. Lozano, Aleksandra Mojsilovic, Kush R. Varshney, Mahmoud Naghshineh
2015	B	`conf`	An Efficient Nonlinear Regression Approach for Genome-Wide Detection of Marginal and Interacting Genetic Variations. RECOMB Seunghak Lee, Aurélie C. Lozano, Prabhanjan Kambadur, Eric P. Xing
2015	—	`conf`	Closed-form Estimators for High-dimensional Generalized Linear Models. NIPS Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar
2015	—	`conf`	Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso. NIPS Eunho Yang, Aurélie C. Lozano
2014	J	`jnl`	Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations. IEEE Trans. Inf. Theory Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire
2014	—	`conf`	Elementary Estimators for Graphical Models. NIPS Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar
2014	A*	`conf`	Elementary Estimators for High-Dimensional Linear Regression. ICML Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar
2014	A*	`conf`	Elementary Estimators for Sparse Covariance Matrices and other Structured Moments. ICML Eunho Yang, Aurélie C. Lozano, Pradeep Ravikumar
2014	A*	`conf`	Orthogonal Matching Pursuit for Sparse Quantile Regression. ICDM Aleksandr Y. Aravkin, Aurélie C. Lozano, Ronny Luss, Prabhanjan Kambadur
2014	J	`jnl`	Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. CoRR Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano
2014	J	`jnl`	Sparse Quantile Huber Regression for Efficient and Robust Estimation. CoRR Aleksandr Y. Aravkin, Anju Kambadur, Aurélie C. Lozano, Ronny Luss
2013	A	`conf`	A Parallel, Block Greedy Method for Sparse Inverse Covariance Estimation for Ultra-high Dimensions. AISTATS Prabhanjan Kambadur, Aurélie C. Lozano
2013	A*	`conf`	Robust sparse estimation of multiresponse regression and inverse covariance matrix via the L2 distance. KDD Aurélie C. Lozano, Huijing Jiang, Xinwei Deng
2013	A	`conf`	Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality. UAI Vikas Sindhwani, Ha Quang Minh, Aurélie C. Lozano
2012	A	`conf`	A Bayesian Markov-switching Model for Sparse Dynamic Network Estimation. SDM Huijing Jiang, Aurélie C. Lozano, Fei Liu
2012	A*	`conf`	Multi-level Lasso for Sparse Multi-task Regression. ICML Aurélie C. Lozano, Grzegorz Swirszcz
2012	J	`jnl`	Scalable Matrix-valued Kernel Learning and High-dimensional Nonlinear Causal Inference CoRR Vikas Sindhwani, Aurélie C. Lozano, Ha Quang Minh
2011	A	`conf`	Group Orthogonal Matching Pursuit for Logistic Regression. AISTATS Aurélie C. Lozano, Grzegorz Swirszcz, Naoki Abe
2011	—	`conf`	Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels. NIPS Vikas Sindhwani, Aurélie C. Lozano
2011	J	`jnl`	Temporal Graphical Models for Cross-Species Gene Regulatory Network Discovery. J. Bioinform. Comput. Biol. Yan Liu, Alexandru Niculescu-Mizil, Aurélie C. Lozano, Yong Lu
2010	—	`conf`	Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference. NIPS Aurélie C. Lozano, Vikas Sindhwani
2010	A*	`conf`	Learning Temporal Causal Graphs for Relational Time-Series Analysis. ICML Yan Liu, Alexandru Niculescu-Mizil, Aurélie C. Lozano, Yong Lu
2009	—	`conf`	A data modeling approach to climate change attribution. KDD Workshop on Knowledge Discovery from Sensor Data Aurélie C. Lozano
2009	—	`conf`	Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction. NIPS Aurélie C. Lozano, Grzegorz Swirszcz, Naoki Abe
2009	J	`jnl`	Grouped graphical Granger modeling for gene expression regulatory networks discovery. Bioinform. Aurélie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset
2009	A*	`conf`	Grouped graphical Granger modeling methods for temporal causal modeling. KDD Aurélie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset
2009	A	`conf`	Proximity-Based Anomaly Detection Using Sparse Structure Learning. SDM Tsuyoshi Idé, Aurélie C. Lozano, Naoki Abe, Yan Liu
2009	A*	`conf`	Spatial-temporal causal modeling for climate change attribution. KDD Aurélie C. Lozano, Hongfei Li, Alexandru Niculescu-Mizil, Yan Liu, Claudia Perlich, Jonathan R. M. Hosking, Naoki Abe
2008	A*	`conf`	Multi-class cost-sensitive boosting with p-norm loss functions. KDD Aurélie C. Lozano, Naoki Abe
2007	J	`jnl`	Throughput scaling in wireless networks with restricted mobility. IEEE Trans. Wirel. Commun. Aurélie C. Lozano, Sanjeev R. Kulkarni, Pramod Viswanath
2006	B	`conf`	Convergence and Consistency of Recursive Boosting. ISIT Aurélie C. Lozano, Sanjeev R. Kulkarni
2005	B	`conf`	A wireless network can achieve maximum throughput without each node meeting all others. ISIT Aurélie C. Lozano, Sanjeev R. Kulkarni
2005	—	`conf`	Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. NIPS Aurélie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire
2004	B	`conf`	Throughput scaling in wireless networks with restricted mobility. ISIT Aurélie C. Lozano, Sanjeev R. Kulkarni, Pramod Viswanath
2002	B	`conf`	Quantized Frame Expansions In A Wireless Environment. DCC Aurélie C. Lozano, Jelena Kovacevic, Mike Andrews