Mark J. van der Laan

57 papers A* 5A 1B 1Journal 43Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Highly Adaptive Principal Component Regression. CoRR Mingxun Wang, Alejandro Schuler, Mark J. van der Laan, Carlos García-Meixide
2026	J	`jnl`	Queueing Causal Models: Comparative Analytics in Queueing Systems. Manuf. Serv. Oper. Manag. Opher Baron, Dmitry Krass, Arik Senderovich, Mark J. van der Laan, Zhenghang Xu
2026	J	`jnl`	TarGene: A Nextflow pipeline for the estimation of genetic effects on human traits via semi-parametric methods. J. Open Source Softw. Olivier Labayle, Joshua Slaughter, Breeshey Roskams-Hieter, Kelsey Tetley-Campbell, Mark J. van der Laan, Chris P. Ponting, Ava Khamseh, Sjoerd Viktor Beentjes
2025	J	`jnl`	TMLE.jl: Targeted Minimum Loss-Based Estimation in Julia. J. Open Source Softw. Olivier Labayle, Chris P. Ponting, Mark J. van der Laan, Ava Khamseh, Sjoerd Viktor Beentjes
2025	J	`jnl`	Targeted Deep Architectures: A TMLE-Based Framework for Robust Causal Inference in Neural Networks. CoRR Yi Li, David McCoy, Nolan Gunter, Kaitlyn Lee, Alejandro Schuler, Mark J. van der Laan
2024	J	`jnl`	Highly Adaptive Ridge. CoRR Alejandro Schuler, Alexander Hagemeister, Mark J. van der Laan
2024	J	`jnl`	Large Language Models as Co-Pilots for Causal Inference in Medical Studies. CoRR Ahmed Alaa, Rachael V. Phillips, Emre Kiciman, Laura Balzer, Mark J. van der Laan, Maya Petersen
2024	A*	`conf`	Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer. ICML Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, Yuxuan Li, Mingduo Zhao, Hiroyasu Iso, Mark J. van der Laan
2024	J	`jnl`	Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer. CoRR Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, Yuxuan Li, Mingduo Zhao, Hiroyasu Iso, Mark J. van der Laan
2024	J	`jnl`	Statistical learning for constrained functional parameters in infinite-dimensional models with applications in fair machine learning. CoRR Razieh Nabi, Nima S. Hejazi, Mark J. van der Laan, David C. Benkeser
2023	J	`jnl`	CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R. J. Open Source Softw. David McCoy, Alan E. Hubbard, Mark J. van der Laan
2023	A*	`conf`	Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. NeurIPS Ahmed M. Alaa, Zaid Ahmad, Mark J. van der Laan
2023	J	`jnl`	Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. CoRR Ahmed Alaa, Zaid Ahmad, Mark J. van der Laan
2023	J	`jnl`	Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions. J. Comput. Graph. Stat. Philippe Boileau, Nima S. Hejazi, Mark J. van der Laan, Sandrine Dudoit
2023	J	`jnl`	Multi-task Highly Adaptive Lasso. CoRR Ivana Malenica, Rachael V. Phillips, Daniel Lazzareschi, Jeremy R. Coyle, Romain Pirracchio, Mark J. van der Laan
2023	J	`jnl`	SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R. J. Open Source Softw. David McCoy, Alejandro Schuler, Alan E. Hubbard, Mark J. van der Laan
2022	J	`jnl`	'haldensify': Highly adaptive lasso conditional density estimation in 'R'. J. Open Source Softw. Nima S. Hejazi, Mark J. van der Laan, David C. Benkeser
2022	J	`jnl`	The Conditional Super Learner. IEEE Trans. Pattern Anal. Mach. Intell. Gilmer Valdes, Yannet Interian, Efstathios D. Gennatas, Mark J. van der Laan
2022	J	`jnl`	The Selectively Adaptive Lasso. CoRR Alejandro Schuler, Mark J. van der Laan
2021	J	`jnl`	Adaptive Sequential Design for a Single Time-Series. CoRR Ivana Malenica, Aurélien Bibaut, Mark J. van der Laan
2021	J	`jnl`	Personalized Online Machine Learning. CoRR Ivana Malenica, Rachael V. Phillips, Romain Pirracchio, Antoine Chambaz, Alan E. Hubbard, Mark J. van der Laan
2021	A*	`conf`	Post-Contextual-Bandit Inference. NeurIPS Aurélien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark J. van der Laan
2021	J	`jnl`	Post-Contextual-Bandit Inference. CoRR Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan
2021	A*	`conf`	Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. NeurIPS Aurélien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark J. van der Laan
2021	J	`jnl`	Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. CoRR Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan
2021	J	`jnl`	Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. CoRR Mark J. van der Laan, Sherri Rose
2021	J	`jnl`	cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R. J. Open Source Softw. Philippe Boileau, Nima S. Hejazi, Brian Collica, Mark J. van der Laan, Sandrine Dudoit
2020	A	`conf`	Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. UAI Aurélien Bibaut, Antoine Chambaz, Mark J. van der Laan
2020	J	`jnl`	Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. CoRR Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan
2020	J	`jnl`	Rate-adaptive model selection over a collection of black-box contextual bandit algorithms. CoRR Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan
2020	J	`jnl`	hal9001: Scalable highly adaptive lasso regression in R. J. Open Source Softw. Nima S. Hejazi, Jeremy R. Coyle, Mark J. van der Laan
2019	J	`jnl`	Conditional Super Learner. CoRR Gilmer Valdes, Yannet Interian, Efstathios D. Gennatas, Mark J. van der Laan
2019	J	`jnl`	Expert-Augmented Machine Learning. CoRR Efstathios D. Gennatas, Jerome H. Friedman, Lyle H. Ungar, Romain Pirracchio, Eric Eaton, L. Reichman, Yannet Interian, Charles B. Simone II, A. Auerbach, E. Delgado, Mark J. van der Laan, Timothy D. Solberg, Gilmer Valdes
2019	A*	`conf`	More Efficient Off-Policy Evaluation through Regularized Targeted Learning. ICML Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan
2019	J	`jnl`	More Efficient Off-Policy Evaluation through Regularized Targeted Learning. CoRR Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan
2018	J	`jnl`	Robust Estimation of Data-Dependent Causal Effects based on Observing a Single Time-Series. CoRR Mark J. van der Laan, Ivana Malenica
2017	J	`jnl`	The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification. CoRR Cheng Ju, Aurélien Bibaut, Mark J. van der Laan
2016	—	`book`	Handbook of Big Data. Peter Bühlmann, Petros Drineas, Michael J. Kane, Mark J. van der Laan
2016	—	`ch.`	Mining with Inference: Data-Adaptive Target Parameters. Handbook of Big Data Alan E. Hubbard, Mark J. van der Laan
2016	B	`conf`	The Highly Adaptive Lasso Estimator. DSAA David C. Benkeser, Mark J. van der Laan
2016	—	`ch.`	Tutorial for Causal Inference. Handbook of Big Data Laura Balzer, Maya Petersen, Mark J. van der Laan
2011	J	`jnl`	Dimension Reduction with Gene Expression Data Using Targeted Variable Importance Measurement. BMC Bioinform. Hui Wang, Mark J. van der Laan
2010	J	`jnl`	A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models. Comput. Stat. Data Anal. Thaddeus J. Haight, Yue Wang, Mark J. van der Laan, Ira B. Tager
2006	J	`jnl`	A semiparametric model selection criterion with applications to the marginal structural model. Comput. Stat. Data Anal. M. Alan Brookhart, Mark J. van der Laan
2006	J	`jnl`	Causal effects in longitudinal studies: Definition and maximum likelihood estimation. Comput. Stat. Data Anal. Romain Neugebauer, Mark J. van der Laan
2006	J	`jnl`	G-computation estimation for causal inference with complex longitudinal data. Comput. Stat. Data Anal. Romain Neugebauer, Mark J. van der Laan
2006	J	`jnl`	Multiple Testing Methods For ChIP - Chip High Density Oligonucleotide Array Data. J. Comput. Biol. Sündüz Keles, Mark J. van der Laan, Sandrine Dudoit, Simon E. Cawley
2005	J	`jnl`	A Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data. J. Comput. Biol. Biao Xing, Mark J. van der Laan
2005	J	`jnl`	A causal inference approach for constructing transcriptional regulatory networks. Bioinform. Biao Xing, Mark J. van der Laan
2005	—	`conf`	Data-adaptive test statistics for microarray data. ECCB/JBI Sach Mukherjee, Stephen J. Roberts, Mark J. van der Laan
2004	J	`jnl`	Regulatory motif finding by logic regression. Bioinform. Sündüz Keles, Mark J. van der Laan, Chris Vulpe
2003	—	`ch.`	Current Status Data: Review, Recent Developments and Open Problems. Advances in Survival Analysis Nicholas P. Jewell, Mark J. van der Laan
2003	—	`ch.`	Estimation of the Bivariate Survival Function with Generalized Bivariate Right Censored Data Structures. Advances in Survival Analysis Sündüz Keles, Mark J. van der Laan, James M. Robins
2003	J	`jnl`	Fitting of mixtures with unspecified number of components using cross validation distance estimate. Comput. Stat. Data Anal. Maja Miloslavsky, Mark J. van der Laan
2003	J	`jnl`	Loss-based estimation with cross-validation: applications to microarray data analysis. SIGKDD Explor. Sandrine Dudoit, Mark J. van der Laan, Sündüz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng
2003	—	`conf`	Multiple Testing for Gene Expression Data: An Investigation of Null Distributions with Consequences for the Permutation Test. METMBS Katherine S. Pollard, Mark J. van der Laan
2002	J	`jnl`	Identification of regulatory elements using a feature selection method. Bioinform. Sündüz Keles, Mark J. van der Laan, Michael B. Eisen