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.
Order on
Below is a list of Dr Majumdar’s recent and influential publications. For a full list, see Google Scholar.
Rosati et al. Representation noising effectively prevents harmful fine-tuning on LLMs, NeurIPS 2024.
M.A. Ayub, S. Majumdar. Embedding-based classifiers can detect prompt injection attacks, CAMLIS 2024.
Rosati et al. Defending against Reverse Preference Attacks is Difficult, arXiV 2024.
L. Derczynski, E. Galinkin, J. Martin, S. Majumdar, N. Inie. garak: A Framework for Security Probing Large Language Models, arXiV 2024.
S. Majumdar. Standards for LLM Security, in: Large Language Models in Cybersecurity: Threats, Exposure and Mitigation, Springer, 2024.
S. Majumdar, T. Vogelslang. Towards Safe LLMs Integration, in: Large Language Models in Cybersecurity: Threats, Exposure and Mitigation, Springer, 2024.
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.
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.
H. Raj, D. Rosati, S. Majumdar. Measuring Reliability of Large Language Models through Semantic Consistency, NeurIPS 2022 ML Safety workshop (Best Paper Award).
S. Majumdar, S. Chatterjee. Feature Selection using e-values, ICML 2022.
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.
Han et al. 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.
Potash et al. Predictive Modeling for Public Health: Preventing Childhood Lead Poisoning, KDD 2015.