Nitesh, Kumar, Ondřej Kuželka, and Luc De Raedt. "First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs." Journal of Artificial Intelligence Research (JAIR); (paper)(code)
Nitesh, Kumar, Ondřej Kuželka, and Luc De Raedt. "Learning Distributional Programs for Relational Autocompletion." Theory and Practice of Logic Programming (TPLP), Cambridge University Press; (paper)(code)
Pedro Zuidberg Dos Martires, Nitesh Kumar, Andreas Persson, Amy Loutfi, and Luc De Raedt. "Symbolic learning and reasoning with noisy data for probabilistic anchoring." Frontiers in Robotics and AI 7 (2020); (paper)
Korra Sathya Babu, Nithin Reddy, Nitesh Kumar, Mark Elliot, and Sanjay Kumar Jena. "Achieving k-anonymity Using Improved Greedy Heuristics for Very Large Relational Databases." Transactions on Data Privacy 6, no. 1 (2013): 1-17; (paper)
Nitesh Kumar, Usashi Chatterjee, and Steven Schockaert, "Ranking Entities along Conceptual Space Dimensions with LLMs: An Analysis of Fine-Tuning Strategies", The 62nd Annual Meeting of the Association for Computational Linguistics (ACL-findings), 2024; (paper) (code)
Nitesh Kumar, and Steven Schockaert. "Solving Hard Analogy Questions with Relation Embedding Chains" In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP-main); (paper) (code)
Nitesh Kumar, and Ondřej Kuželka. "Context-Specific Likelihood Weighting." In International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2021; (paper)(code)(short presentation)
Anil Kumar, Nitesh Kumar, Muzammil Hussain, Santanu Chaudhury, and Sumeet Agarwal. "Semantic clustering-based cross-domain recommendation." In 2014 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 137-141. IEEE, 2014; (paper)
Learning Distributional Programs for Relational Autocompletion. The 7th Workshop on Probabilistic Logic Programming; PLP 2020.
Hybrid Probabilistic Logic Programming: Inference and Learning. (link)