| Year | Publication (*Alphabetical authors) |
|---|---|
| 2024 | D. Rosati, J. Wehner, K. Williams, L. Bartoszcze, D. Atanasov, R. Gonzales, S. Majumdar, C. Maple, H. Sajjad, F. Rudzicz. Representation Noising: A Defence Mechanism Against Harmful Finetuning, NeurIPS 2024. |
| 2024 | M.A. Ayub, S. Majumdar. Embedding-based classifiers can detect prompt injection attacks, CAMLIS 2024. |
| 2024 | H. Raj, V. Gupta, D. Rosati, S. Majumdar. Improving Consistency in Large Language Models through Chain of Guidance, in submission. |
| 2024 | D. Rosati, G. Edkins, H. Raj, D. Atanasov, S. Majumdar, J. Rajendran, F. Rudzicz, H. Sajjad. Defending against Reverse Preference Attacks is Difficult, arXiv:2409.12914, in submission. |
| 2024 | L. Derczynski, E. Galinkin, J. Martin, S. Majumdar, N Inie. garak: A Framework for Security Probing Large Language Models, arXiv:2406.11036. |
| 2024 | S. Majumdar, T. Vogelslang. Towards Safe LLMs Integration. In Large Language Models in Cybersecurity, published by Springer, pp 243-247 (book chapter). |
| 2024 | S. Majumdar. Standards for LLM Security. In Large Language Models in Cybersecurity, published by Springer, pp 225-231 (book chapter). |
| 2023 | R.M. Rustamov, S. Majumdar. Intrinsic Sliced Wasserstein Distances for Comparing Collections of Probability Distributions on Manifolds and Graphs, ICML 2023. |
| 2023 | S. Majumdar, S. Basu, M. McGue, S. Chatterjee. Simultaneous Selection of Multiple Important Single Nucleotide Polymorphisms in Familial Genome Wide Association Studies Data, Scientific reports 13 (1), 8476. |
| 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. |
| 2023 | Y. Pruksachatkun, M. McAteer, S. Majumdar. Practicing Trustworthy Machine Learning, published by O'Reilly Media (book). |
| 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, 32, 1191–1214. |
| 2022 | C. Flynn, A. Guha, S. Majumdar, D. Srivastava, Z. Zhou. Towards Algorithmic Fairness in Space-Time: Filling in Black Holes, NeurIPS 2022 Workshop on Trustworthy and Socially Responsible Machine Learning. |
| 2022 | H. Raj, D. Rosati, S. Majumdar. Measuring Reliability of Large Language Models through Semantic Consistency, NeurIPS 2022 ML Safety Workshop (best paper award). |
| 2022 | G. Subramaniam, S. Majumdar. Network Security Modelling with Distributional Data, CAMLIS 2022. |
| 2022 | S. Majumdar, S. Chatterjee. Feature Selection using e-values, ICML 2022. |
| 2022 | S. Majumdar, S. Chatterjee. On weighted multivariate sign functions, Journal of Multivariate Analysis 191, 105013. |
| 2022 | S. Majumdar. Fairness, Explainability, Privacy, and Robustness for Trustworthy Algorithmic Decision Making, In Big Data Analytics in Chemoinformatics and Bioinformatics, published by Elsevier (book chapter). |
| 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. |
| 2021 | S. Majumdar, C. Flynn, R. Mitra. Detecting Bias in the Presence of Spatial Autocorrelation, NeurIPS 2021 AFCR Workshop. |
| 2021 | N. Derzsy*, S. Majumdar*, R. Malik*. An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research, CompleNet 2021. |
| 2021 | C. Last, P. Pramanik, N. Saini, A.S. Majety, D.-H. Kim, M.-G. Herranz, S. Majumdar. Towards An Open Global Air Quality Monitoring Platform to Assess Children's Exposure to Air Pollutants in the Light of COVID-19 Lockdowns, CHI 2021 Late Breaking Work. |
| 2020 | 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. |
| 2019 | S.C. Basak, S. Majumdar, A. Nandy, P. Roy, T. Dutta, M. Vracko, A.K. Bhattacharjee. Computer-Assisted and Data Driven Approaches for Surveillance, Drug Discovery, and Vaccine Design for the Zika Virus, Pharmaceuticals 12 (4), 157. |
| 2019 | S. Majumdar, S.C. Basak, C.N. Lungu, M.V. Diudea, G.D. Grunwald. Finding Needles in a Haystack: Determining Key Molecular Descriptors Associated with the Blood‐brain Barrier Entry of Chemical Compounds Using Machine Learning, Molecular Informatics 38 (8-9), 1800164. |
| 2019 | 11 authors. Confronting data sparsity to identify potential sources of Zika virus spillover infection among primates, Epidemics 27, 59-65. |
| 2019 | S. Majumdar. In Zika Virus Surveillance, Vaccinology, and Anti-Zika Drug Discovery: Computer-Assisted Strategies to Combat the Menace, published by Nova Science Publishers, pp 129-152 (book chapter). |
| 2018 | S. Majumdar, S.C. Basak, C.N. Lungu, M.V. Diudea, G.D. Grunwald. Mathematical structural descriptors and mutagenicity assessment: a study with congeneric and diverse datasets, SAR and QSAR in Environmental Research 29 (8), 579-590. |
| 2018 | 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 | S. Majumdar, S. Chatterjee. Non-convex penalized multitask regression using data depth-based penalties, Stat 7, e174. |
| 2016 | S. Majumdar, S.C. Basak. Exploring intrinsic dimensionality of chemical spaces for robust QSAR model development: A comparison of several statistical approaches, Current Computer-Aided Drug Design 12 (4), 294-301. |
| 2015 | S. Majumdar, L.R. Dietz, S. Chatterjee. Identifying Driving Factors Behind Indian Monsoon Precipitation using Model Selection based on Data Depth, Proceedings of the Fifth International Workshop on Climate Informatics: CI 2015. |
| 2015 | S.C. Basak, S. Majumdar. Prediction of Mutagenicity of Chemicals from Their Calculated Molecular Descriptors: A Case Study with Structurally Homogeneous versus Diverse Datasets, Current Computer-Aided Drug Design 11 (2), 117-123. |
| 2015 | 10 authors. Predictive Modeling for Public Health: Preventing Childhood Lead Poisoning, KDD 2015. |
| 2015 | U.K. Mukherjee, S. Majumdar, S. Chatterjee. Fast and robust supervised learning in high dimensions using the geometry of the data, ICDM 2015. |
| 2015 | S.C. Basak, S. Majumdar. Current landscape of hierarchical QSAR modeling and its applications: Some comments on the importance of mathematical descriptors as well as rigorous statistical methods of model building and validation, In Advances in mathematical chemistry and applications, published by Bentham Science Publishers, pp 251-281 (book chapter). |
| 2013 | S. Majumdar, S.C. Basak, G.D.Grunwald. Adapting Interrelated Two-Way Clustering Method for Quantitative Structure-Activity Relationship (QSAR) Modeling of Mutagenicity/Non-Mutagenicity of a Diverse Set of Chemicals, Current Computer-Aided Drug Design 9 (4), 463-471. |
© Subhabrata Majumdar 2024.