Writing
Dr Majumdar is a prolific writer, who has co-authored a book and published more than 40 academic papers.
In his first book Practicing Trustworthy Machine Learning, Dr Majumdar and his co-authors lay out a blueprint for building industry-grade trusted ML systems. With this book, engineers and data scientists will gain a much-needed foundation for releasing trustworthy ML applications into a noisy, messy, and often hostile world.
Practicing Trustworthy Machine Learning is available on O’Reilly Learning and through major booksellers.
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Below is a list of Dr Majumdar’s recent and influential publications. For a full list, see Google Scholar.
H. Raj, V. Gupta, D. Rosati, S. Majumdar. Semantic Consistency for Assuring Reliability of Large Language Models, arXiV preprint, 2023.
F.T. Brito, V.A.E. Farias, C. Flynn, J.C. Machado, S. Majumdar, D. Srivastava. Global and Local Differentially Private Release of Count-Weighted Graphs, SIGMOD 2023.
R. Rustamov, S. Majumdar. Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs, ICML 2023.
S. Majumdar, S. Chatterjee. Simultaneous Selection of Multiple Important Single Nucleotide Polymorphisms in Familial Genome Wide Association Studies Data, Scientific Reports, 2023.
V.A.E. Farias, F.T. Brito, C. Flynn, J.C. Machado, S. Majumdar, D. Srivastava. Local Dampening: Differential Privacy for Non-numeric Queries via Local Sensitivity, The VLDB Journal, 2023.
S. Majumdar, G. Subramaniam. Network Security Modelling with Distributional Data, CAMLIS 2022.
H. Raj, D. Rosati, S. Majumdar. Measuring Reliability of Large Language Models through Semantic Consistency, NeurIPS 2022 ML Safety workshop (Best Paper Award).
C. Flynn, A. Guha, S. Majumdar, D. Srivastava, Z. Zhou. Towards Algorithmic Fairness in Space-Time: Filling in Black Holes, NeurIPS 2022 TSRML workshop.
S. Majumdar, S. Chatterjee. Feature Selection using e-values, ICML 2022.
S. Majumdar, C. Flynn, R. Mitra. Detecting Bias in the Presence of Spatial Autocorrelation, NeurIPS 2021 AFCR workshop.
S. Majumdar, G. Michailidis. Joint Estimation and Inference for Data Integration Problems based on Multiple Multi-layered Gaussian Graphical Models, Journal of Machine Learning Research, 23(1), 1-53, 2022.
A. Ghosh, S. Majumdar. Ultrahigh-dimensional Robust and Efficient Sparse Regression using Non-Concave Penalized Density Power Divergence, IEEE Transactions on Information Theory, 66(12), 7812-7827, 2020.
11 authors. Confronting data sparsity to identify potential sources of Zika virus spillover infection among primates, Epidemics, 27, 59-65, 2019.
S. Majumdar, S.C. Basak. Beware of external validation!-A Comparative Study of Several Validation Techniques used in QSAR Modelling, Current computer-aided drug design 14(4), 284-291, 2018.
10 authors. Predictive Modeling for Public Health: Preventing Childhood Lead Poisoning, KDD 2015.