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Sach Mukherjee
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2020 – today
- 2024
- [c8]Kai Lagemann, Christian Lagemann, Sach Mukherjee:
Invariance-based Learning of Latent Dynamics. ICLR 2024 - [i9]Luka Kovacevic, Izzy Newsham, Sach Mukherjee, John Whittaker:
Simulation-based Benchmarking for Causal Structure Learning in Gene Perturbation Experiments. CoRR abs/2407.06015 (2024) - 2023
- [j20]Konstantinos Perrakis, Thomas Lartigue
, Frank Dondelinger, Sach Mukherjee:
Regularized Joint Mixture Models. J. Mach. Learn. Res. 24: 19:1-19:47 (2023) - [j19]Kai Lagemann
Deep learning of causal structures in high dimensions under data limitations. Nat. Mac. Intell. 5(11): 1306-1316 (2023) - [j18]Robin Richter, Shankar Bhamidi
Improved baselines for causal structure learning on interventional data. Stat. Comput. 33(5): 93 (2023) - [i8]Kai Lagemann, Christian Lagemann, Sach Mukherjee:
Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers. CoRR abs/2306.12077 (2023) - 2022
- [i7]Thomas Lartigue, Sach Mukherjee:
On unsupervised projections and second order signals. CoRR abs/2204.05139 (2022) - [i6]Thomas Lartigue, Sach Mukherjee:
Scalable Regularised Joint Mixture Models. CoRR abs/2205.01486 (2022) - [i5]Konstantin Göbler, Anne Miloschewski, Mathias Drton, Sach Mukherjee:
High-Dimensional Undirected Graphical Models for Arbitrary Mixed Data. CoRR abs/2211.11700 (2022) - [i4]Kai Lagemann, Christian Lagemann, Bernd Taschler, Sach Mukherjee:
Deep Learning of Causal Structures in High Dimensions. CoRR abs/2212.04866 (2022) - 2021
- [j17]Christian Lagemann
Deep recurrent optical flow learning for particle image velocimetry data. Nat. Mach. Intell. 3(7): 641-651 (2021) - 2020
- [j16]Fan Wang, Sach Mukherjee, Sylvia Richardson, Steven M. Hill
High-dimensional regression in practice: an empirical study of finite-sample prediction, variable selection and ranking. Stat. Comput. 30(3): 697-719 (2020) - [c7]Marco Eigenmann, Sach Mukherjee, Marloes H. Maathuis:
Evaluation of Causal Structure Learning Algorithms via Risk Estimation. UAI 2020: 151-160
2010 – 2019
- 2019
- [j15]Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, Sach Mukherjee:
Causal Learning via Manifold Regularization. J. Mach. Learn. Res. 20: 127:1-127:32 (2019) - [i3]Bernd Taschler, Frank Dondelinger, Sach Mukherjee:
Model-based clustering in very high dimensions via adaptive projections. CoRR abs/1902.08472 (2019) - [i2]Umberto Noè, Bernd Taschler, Joachim Täger, Peter Heutink, Sach Mukherjee:
Ancestral causal learning in high dimensions with a human genome-wide application. CoRR abs/1905.11506 (2019) - 2018
- [c6]Gerard Sanroma, Loes Rutten-Jacobs, Valerie Lohner
SCCA-Ref: Novel Sparse Canonical Correlation Analysis with Reference to Discover Independent Spatial Associations Between White Matter Hyperintensities and Atrophy. MLMI@MICCAI 2018: 81-88 - 2017
- [j14]Nicolas Städler, Frank Dondelinger
Molecular heterogeneity at the network level: high-dimensional testing, clustering and a TCGA case study. Bioinform. 33(18): 2890-2896 (2017) - 2016
- [j13]Robert J. B. Goudie, Sach Mukherjee:
A Gibbs Sampler for Learning DAGs. J. Mach. Learn. Res. 17: 30:1-30:39 (2016) - [j12]Chris J. Oates, Jim Q. Smith, Sach Mukherjee:
Estimating Causal Structure Using Conditional DAG Models. J. Mach. Learn. Res. 17: 54:1-54:23 (2016) - [j11]Chris J. Oates, Jim Q. Smith, Sach Mukherjee, James Cussens
Exact estimation of multiple directed acyclic graphs. Stat. Comput. 26(4): 797-811 (2016) - 2014
- [j10]Chris J. Oates, Frank Dondelinger, Nora Bayani, James Korkola, Joe W. Gray, Sach Mukherjee:
Causal network inference using biochemical kinetics. Bioinform. 30(17): 468-474 (2014) - [c5]Chris J. Oates, Sach Mukherjee:
Joint Structure Learning of Multiple Non-Exchangeable Networks. AISTATS 2014: 687-695 - 2013
- [j9]Chris J. Oates, Bryan T. J. Hennessy
Network inference using steady-state data and Goldbeter-Koshland kinetics. Bioinform. 29(6): 819 (2013) - [i1]Steven M. Hill, Sach Mukherjee:
Network-based clustering with mixtures of L1-penalized Gaussian graphical models: an empirical investigation. CoRR abs/1301.2194 (2013) - 2012
- [j8]Chris J. Oates, Bryan T. J. Hennessy
Network inference using steady-state data and Goldbeter-koshland kinetics. Bioinform. 28(18): 2342-2348 (2012) - [j7]Steven M. Hill
Bayesian Inference of Signaling Network Topology in a Cancer Cell Line. Bioinform. 28(21): 2804-2810 (2012) - [j6]Steven M. Hill
Integrating biological knowledge into variable selection: an empirical Bayes approach with an application in cancer biology. BMC Bioinform. 13: 94 (2012) - 2011
- [j5]Sach Mukherjee, Steven M. Hill
Network clustering: probing biological heterogeneity by sparse graphical models. Bioinform. 27(7): 994-1000 (2011) - 2010
- [j4]Steven J. Kiddle
Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana. Bioinform. 26(3): 355-362 (2010) - [c4]Daniel James Barker, Steven M. Hill
MC4: A Tempering Algorithm for Large-Sample Network Inference. PRIB 2010: 431-442
2000 – 2009
- 2009
- [j3]Sach Mukherjee, Steven Pelech, Richard M. Neve, Wen-Lin Kuo, Safiyyah Ziyad, Paul T. Spellman, Joe W. Gray, Terence P. Speed:
Sparse combinatorial inference with an application in cancer biology. Bioinform. 25(2): 265-271 (2009) - [r1]Sach Mukherjee:
Multiple Hypothesis Testing for Data Mining. Encyclopedia of Data Warehousing and Mining 2009: 1390-1395 - 2006
- [j2]David Montaner
Next station in microarray data analysis: GEPAS. Nucleic Acids Res. 34(Web-Server-Issue): 486-491 (2006) - 2005
- [j1]Sach Mukherjee, Stephen J. Roberts:
A Theoretical Analysis of the Selection of Differentially Expressed Genes. J. Bioinform. Comput. Biol. 3(3): 627-644 (2005) - [c3]Sach Mukherjee, Stephen J. Roberts, Mark J. van der Laan:
Data-adaptive test statistics for microarray data. ECCB/JBI 2005: 114 - 2004
- [c2]Sach Mukherjee, Stephen J. Roberts:
A Theoretical Analysis of Gene Selection. CSB 2004: 131-141 - [c1]Sach Mukherjee, Stephen J. Roberts:
Probabilistic Consistency Analysis for Gene Selection. CSB 2004: 487-488
Coauthor Index
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