Mark J. van der Laan

Publication Activity (10 Years)

Years Active: 2002-2024
Publications (10 Years): 31

Top Topics

Maximum Likelihood

Causal Inference

Conditional Density Estimation

Top Venues

J. Open Source Softw.

J. Comput. Graph. Stat.

Publications

Toru Shirakawa, Yi Li, Yulun Wu, Sky Qiu, Yuxuan Li, Mingduo Zhao, Hiroyasu Iso, Mark J. van der Laan
Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformer. CoRR (2024)
Ahmed Alaa, Rachael V. Phillips, Emre Kiciman, Laura Balzer, Mark J. van der Laan, Maya Petersen
Large Language Models as Co-Pilots for Causal Inference in Medical Studies. CoRR (2024)
Razieh Nabi, Nima S. Hejazi, Mark J. van der Laan, David C. Benkeser
Statistical learning for constrained functional parameters in infinite-dimensional models with applications in fair machine learning. CoRR (2024)
David McCoy, Alejandro Schuler, Alan E. Hubbard, Mark J. van der Laan
SuperNOVA: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in R. J. Open Source Softw. 8 (91) (2023)
David McCoy, Alan E. Hubbard, Mark J. van der Laan
CVtreeMLE: Efficient Estimation of Mixed Exposures using Data Adaptive Decision Trees and Cross-Validated Targeted Maximum Likelihood Estimation in R. J. Open Source Softw. 8 (82) (2023)
Ivana Malenica, Rachael V. Phillips, Daniel Lazzareschi, Jeremy R. Coyle, Romain Pirracchio, Mark J. van der Laan
Multi-task Highly Adaptive Lasso. CoRR (2023)
Philippe Boileau, Nima S. Hejazi, Mark J. van der Laan, Sandrine Dudoit
Cross-Validated Loss-based Covariance Matrix Estimator Selection in High Dimensions. J. Comput. Graph. Stat. 32 (2) (2023)
Ahmed Alaa, Zaid Ahmad, Mark J. van der Laan
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. CoRR (2023)
Ahmed M. Alaa, Zaid Ahmad, Mark J. van der Laan
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects. NeurIPS (2023)
Nima S. Hejazi, Mark J. van der Laan, David C. Benkeser
'haldensify': Highly adaptive lasso conditional density estimation in 'R'. J. Open Source Softw. 7 (78) (2022)
Gilmer Valdes, Yannet Interian, Efstathios D. Gennatas, Mark J. van der Laan
The Conditional Super Learner. IEEE Trans. Pattern Anal. Mach. Intell. 44 (12) (2022)
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. CoRR (2021)
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark J. van der Laan
Post-Contextual-Bandit Inference. CoRR (2021)
Mark J. van der Laan, Sherri Rose
Why Machine Learning Cannot Ignore Maximum Likelihood Estimation. CoRR (2021)
Aurélien Bibaut, Maria Dimakopoulou, Nathan Kallus, Antoine Chambaz, Mark J. van der Laan
Post-Contextual-Bandit Inference. NeurIPS (2021)
Philippe Boileau, Nima S. Hejazi, Brian Collica, Mark J. van der Laan, Sandrine Dudoit
cvCovEst: Cross-validated covariance matrix estimator selection and evaluation in R. J. Open Source Softw. 6 (63) (2021)
Aurélien Bibaut, Nathan Kallus, Maria Dimakopoulou, Antoine Chambaz, Mark J. van der Laan
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. NeurIPS (2021)
Ivana Malenica, Rachael V. Phillips, Romain Pirracchio, Antoine Chambaz, Alan E. Hubbard, Mark J. van der Laan
Personalized Online Machine Learning. CoRR (2021)
Ivana Malenica, Aurélien Bibaut, Mark J. van der Laan
Adaptive Sequential Design for a Single Time-Series. CoRR (2021)
Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms. CoRR (2020)
Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. CoRR (2020)
Aurélien Bibaut, Antoine Chambaz, Mark J. van der Laan
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. UAI (2020)
Nima S. Hejazi, Jeremy R. Coyle, Mark J. van der Laan
hal9001: Scalable highly adaptive lasso regression in R. J. Open Source Softw. 5 (53) (2020)
Efstathios D. Gennatas, J. 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
Expert-Augmented Machine Learning. CoRR (2019)
Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan
More Efficient Off-Policy Evaluation through Regularized Targeted Learning. CoRR (2019)
Gilmer Valdes, Yannet Interian, Efstathios D. Gennatas, Mark J. van der Laan
Conditional Super Learner. CoRR (2019)
Aurélien Bibaut, Ivana Malenica, Nikos Vlassis, Mark J. van der Laan
More Efficient Off-Policy Evaluation through Regularized Targeted Learning. ICML (2019)
Nima S. Hejazi, Rachael V. Phillips, Alan E. Hubbard, Mark J. van der Laan
: Targeted, robust, and model-free differential methylation analysis in R. F1000Research 7 (2018)
Mark J. van der Laan, Ivana Malenica
Robust Estimation of Data-Dependent Causal Effects based on Observing a Single Time-Series. CoRR (2018)
Cheng Ju, Aurélien Bibaut, Mark J. van der Laan
The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification. CoRR (2017)
David C. Benkeser, Mark J. van der Laan
The Highly Adaptive Lasso Estimator. DSAA (2016)
Hui Wang, Mark J. van der Laan
Dimension Reduction with Gene Expression Data Using Targeted Variable Importance Measurement. BMC Bioinform. 12 (2011)
Annette M. Molinaro, Karen Lostritto, Mark J. van der Laan
: deletion/substitution/addition algorithm for partitioning the covariate space in prediction. Bioinform. 26 (10) (2010)
Thaddeus J. Haight, Yue Wang, Mark J. van der Laan, Ira B. Tager
A cross-validation deletion-substitution-addition model selection algorithm: Application to marginal structural models. Comput. Stat. Data Anal. 54 (12) (2010)
M. Alan Brookhart, Mark J. van der Laan
A semiparametric model selection criterion with applications to the marginal structural model. Comput. Stat. Data Anal. 50 (2) (2006)
Romain Neugebauer, Mark J. van der Laan
Causal effects in longitudinal studies: Definition and maximum likelihood estimation. Comput. Stat. Data Anal. 51 (3) (2006)
Sündüz Keles, Mark J. van der Laan, Sandrine Dudoit, Simon E. Cawley
Multiple Testing Methods For ChIP - Chip High Density Oligonucleotide Array Data. J. Comput. Biol. 13 (3) (2006)
Romain Neugebauer, Mark J. van der Laan
G-computation estimation for causal inference with complex longitudinal data. Comput. Stat. Data Anal. 51 (3) (2006)
Sach Mukherjee, Stephen J. Roberts, Mark J. van der Laan
Data-adaptive test statistics for microarray data. ECCB/JBI (2005)
Biao Xing, Mark J. van der Laan
A causal inference approach for constructing transcriptional regulatory networks. Bioinform. 21 (21) (2005)
Biao Xing, Mark J. van der Laan
A Statistical Method for Constructing Transcriptional Regulatory Networks Using Gene Expression and Sequence Data. J. Comput. Biol. 12 (2) (2005)
Sündüz Keles, Mark J. van der Laan, Chris Vulpe
Regulatory motif finding by logic regression. Bioinform. 20 (16) (2004)
Sandrine Dudoit, Mark J. van der Laan, Sündüz Keles, Annette M. Molinaro, Sandra E. Sinisi, Siew Leng Teng
Loss-based estimation with cross-validation: applications to microarray data analysis. SIGKDD Explor. 5 (2) (2003)
Katherine S. Pollard, Mark J. van der Laan
Multiple Testing for Gene Expression Data: An Investigation of Null Distributions with Consequences for the Permutation Test. METMBS (2003)
Maja Miloslavsky, Mark J. van der Laan
Fitting of mixtures with unspecified number of components using cross validation distance estimate. Comput. Stat. Data Anal. 41 (3-4) (2003)
Sündüz Keles, Mark J. van der Laan, Michael B. Eisen
Identification of regulatory elements using a feature selection method. Bioinform. 18 (9) (2002)