Dr Nitesh Kumar

Kumar, N., Chatterjee, U. and Schockaert, S. 2024. Ranking entities along conceptual space dimensions with LLMs: An analysis of fine-tuning strategies. Presented at: The 62nd Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand, 11-16 August 2024 Presented at Ku, L., Martins, A. and Srikumar, V. eds.Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics pp. 7974-7989.

Kumar, N., Kuzelka, O. and De Raedt, L. 2023. First-order context-specific likelihood weighting in hybrid probabilistic logic programs. Journal of Artificial Intelligence Research 77, pp. 683-735. (10.1613/jair.1.13657)
Kumar, N. and Schockaert, S. 2023. Solving hard analogy questions with relation embedding chains. Presented at: Conference on Empirical Methods in Natural Language Processing, EMNLP, Singapore, 6-10 December 2023 Presented at Bouamor, H., Pino, J. and Bali, K. eds.Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics pp. 6224–6236., (10.18653/v1/2023.emnlp-main.382)

Kumar, N., Kuželka, O. and De Raedt, L. 2022. Learning distributional programs for relational autocompletion. Theory and Practice of Logic Programming 22(1), pp. 81-114. (10.1017/S1471068421000144)

Kumar, N. and Kuželka, O. 2021. Context-specific likelihood weighting. Presented at: The 24th International Conference on Artificial Intelligence and Statistics, Virtual, 13 -15 April 2021Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, Vol. 130. Proceedings of Machine Learning Research (PMLR) pp. 2125-2133.

Zuidberg Dos Martires, P., Kumar, N., Persson, A., Loutfi, A. and De Raedt, L. 2020. Symbolic learning and reasoning with noisy data for probabilistic anchoring. Frontiers in Robotics and AI 7, article number: 100. (10.3389/frobt.2020.00100)

Kumar, N., Chatterjee, U. and Schockaert, S. 2024. Ranking entities along conceptual space dimensions with LLMs: An analysis of fine-tuning strategies. Presented at: The 62nd Annual Meeting of the Association for Computational Linguistics, Bangkok, Thailand, 11-16 August 2024 Presented at Ku, L., Martins, A. and Srikumar, V. eds.Findings of the Association for Computational Linguistics ACL 2024. Association for Computational Linguistics pp. 7974-7989.
Kumar, N. and Schockaert, S. 2023. Solving hard analogy questions with relation embedding chains. Presented at: Conference on Empirical Methods in Natural Language Processing, EMNLP, Singapore, 6-10 December 2023 Presented at Bouamor, H., Pino, J. and Bali, K. eds.Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics pp. 6224–6236., (10.18653/v1/2023.emnlp-main.382)
Kumar, N. and Kuželka, O. 2021. Context-specific likelihood weighting. Presented at: The 24th International Conference on Artificial Intelligence and Statistics, Virtual, 13 -15 April 2021Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, Vol. 130. Proceedings of Machine Learning Research (PMLR) pp. 2125-2133.

Kumar, N., Kuzelka, O. and De Raedt, L. 2023. First-order context-specific likelihood weighting in hybrid probabilistic logic programs. Journal of Artificial Intelligence Research 77, pp. 683-735. (10.1613/jair.1.13657)
Kumar, N., Kuželka, O. and De Raedt, L. 2022. Learning distributional programs for relational autocompletion. Theory and Practice of Logic Programming 22(1), pp. 81-114. (10.1017/S1471068421000144)
Zuidberg Dos Martires, P., Kumar, N., Persson, A., Loutfi, A. and De Raedt, L. 2020. Symbolic learning and reasoning with noisy data for probabilistic anchoring. Frontiers in Robotics and AI 7, article number: 100. (10.3389/frobt.2020.00100)

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