Sach Mukherjee

According to our database, Sach Mukherjee authored at least 37 papers between 2004 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments.

Luka Kovacevic, Izzy Newsham, Sach Mukherjee, John Whittaker

CoRR, 2024

Invariance-based Learning of Latent Dynamics.

Kai Lagemann, Christian Lagemann, Sach Mukherjee

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Improved baselines for causal structure learning on interventional data.

Robin Richter, Shankar Bhamidi, Sach Mukherjee

Stat. Comput., October, 2023

Deep learning of causal structures in high dimensions under data limitations.

Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee

Nat. Mac. Intell., October, 2023

Regularized Joint Mixture Models.

Konstantinos Perrakis, Thomas Lartigue, Frank Dondelinger, Sach Mukherjee

J. Mach. Learn. Res., 2023

Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers.

Kai Lagemann, Christian Lagemann, Sach Mukherjee

CoRR, 2023

2022

Deep Learning of Causal Structures in High Dimensions.

Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee

CoRR, 2022

High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data.

Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee

CoRR, 2022

Scalable Regularised Joint Mixture Models.

Thomas Lartigue, Sach Mukherjee

CoRR, 2022

On unsupervised projections and second order signals.

Thomas Lartigue, Sach Mukherjee

CoRR, 2022

2021

Deep recurrent optical flow learning for particle image velocimetry data.

Christian Lagemann, Kai Lagemann, Sach Mukherjee, Wolfgang Schröder

Nat. Mach. Intell., 2021

2020

High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking.

Fan Wang, Sach Mukherjee, Sylvia Richardson, Steven M. Hill

Stat. Comput., 2020

Evaluation of Causal Structure Learning Algorithms via Risk Estimation.

Marco Eigenmann, Sach Mukherjee, Marloes H. Maathuis

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019

Causal Learning via Manifold Regularization.

Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, Sach Mukherjee

J. Mach. Learn. Res., 2019

Ancestral causal learning in high dimensions with a human genome-wide application.

Umberto Noè, Bernd Taschler, Joachim Täger, Peter Heutink, Sach Mukherjee

CoRR, 2019

Model-based clustering in very high dimensions via adaptive projections.

Bernd Taschler, Frank Dondelinger, Sach Mukherjee

CoRR, 2019

2018

SCCA-Ref: Novel Sparse Canonical Correlation Analysis with Reference to Discover Independent Spatial Associations Between White Matter Hyperintensities and Atrophy.

Gerard Sanroma, Loes Rutten-Jacobs, Valerie Lohner, Johanna Kramme, Sach Mukherjee, Martin Reuter, Tony Stöcker, Monique M. B. Breteler

Proceedings of the Machine Learning in Medical Imaging - 9th International Workshop, 2018

2017

Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study.

Nicolas Städler, Frank Dondelinger, Steven M. Hill, Rehan Akbani, Yiling Lu, Gordon B. Mills, Sach Mukherjee

Bioinform., 2017

2016

Exact estimation of multiple directed acyclic graphs.

Chris J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens

Stat. Comput., 2016

Estimating Causal Structure Using Conditional DAG Models.

Chris J. Oates, Jim Q. Smith, Sach Mukherjee

J. Mach. Learn. Res., 2016

A Gibbs Sampler for Learning DAGs.

Robert J. B. Goudie, Sach Mukherjee

J. Mach. Learn. Res., 2016

2014

Causal network inference using biochemical kinetics.

Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, Sach Mukherjee

Bioinform., 2014

Joint Structure Learning of Multiple Non-Exchangeable Networks.

Chris J. Oates, Sach Mukherjee

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Network-based clustering with mixtures of L1-penalized Gaussian graphical models: an empirical investigation

Steven M. Hill, Sach Mukherjee

CoRR, 2013

Network inference using steady-state data and Goldbeter-Koshland kinetics.

Chris J. Oates, Bryan T. J. Hennessy, Yiling Lu, Gordon B. Mills, Sach Mukherjee

Bioinform., 2013

2012

Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology.

Steven M. Hill, Richard M. Neve, Nora Bayani, Wen-Lin Kuo, Safiyyah Ziyad, Paul T. Spellman, Joe W. Gray, Sach Mukherjee

BMC Bioinform., 2012

Bayesian Inference of Signaling Network Topology in a Cancer Cell Line.

Steven M. Hill, Yiling Lu, Jennifer Molina, Laura Heiser, Paul T. Spellman, Terence P. Speed, Joe W. Gray, Gordon B. Mills, Sach Mukherjee

Bioinform., 2012

2011

Network clustering: probing biological heterogeneity by sparse graphical models.

Sach Mukherjee, Steven M. Hill

Bioinform., 2011

2010

Temporal clustering by affinity propagation reveals transcriptional modules in <i>Arabidopsis thaliana</i>.

Steven J. Kiddle, Oliver P. F. Windram, Stuart McHattie, Andrew Meade, Jim Beynon, Vicky Buchanan-Wollaston, Katherine J. Denby, Sach Mukherjee

Bioinform., 2010

MC⁴: A Tempering Algorithm for Large-Sample Network Inference.

Daniel James Barker, Steven M. Hill, Sach Mukherjee

Proceedings of the Pattern Recognition in Bioinformatics, 2010

2009

Multiple Hypothesis Testing for Data Mining.

Sach Mukherjee

Proceedings of the Encyclopedia of Data Warehousing and Mining, Second Edition (4 Volumes), 2009

Sparse combinatorial inference with an application in cancer biology.

Sach Mukherjee, Steven Pelech, Richard M. Neve, Wen-Lin Kuo, Safiyyah Ziyad, Paul T. Spellman, Joe W. Gray, Terence P. Speed

Bioinform., 2009

2006

Next station in microarray data analysis: GEPAS.

David Montaner, Joaquín Tárraga, Jaime Huerta-Cepas, Jordi Burguet-Castell, Juan M. Vaquerizas, Lucía Conde, Pablo Minguez, Javier Vera, Sach Mukherjee, Joan Valls, Miguel A. G. Pujana, Eva Alloza, Javier Herrero, Fátima Al-Shahrour, Joaquín Dopazo

Nucleic Acids Res., 2006

2005

A Theoretical Analysis of the Selection of Differentially Expressed Genes.

Sach Mukherjee, Stephen J. Roberts

J. Bioinform. Comput. Biol., 2005

Data-adaptive test statistics for microarray data.

Sach Mukherjee, Stephen J. Roberts, Mark J. van der Laan

Proceedings of the ECCB/JBI'05 Proceedings, Fourth European Conference on Computational Biology/Sixth Meeting of the Spanish Bioinformatics Network (Jornadas de BioInformática), Palacio de Congresos, Madrid, Spain, September 28, 2005

2004

Probabilistic Consistency Analysis for Gene Selection.

Sach Mukherjee, Stephen J. Roberts

Proceedings of the 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference, 2004

A Theoretical Analysis of Gene Selection.

Sach Mukherjee, Stephen J. Roberts

Proceedings of the 3rd International IEEE Computer Society Computational Systems Bioinformatics Conference, 2004