Publications

  • Benavides-Prado, D., Erfani, S., Fournier-Viger, P., Boo, Y.L., Koh, Y.S. (2024). Data Science and Machine Learning: 21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11–13, 2023, Proceedings. Springer CCIS Volume 1943.

  • Hartill, T., Benavides-Prado, D., Witbrock, M., & Riddle, P. J. (2023). Answering unseen questions with smaller language models using rationale generation and dense retrieval. arXiv preprint arXiv:2308.04711.

  • Knowles, K., Bensemann, J., Benavides-Prado, D., Yogarajan, V., Witbrock, M., Dobbie, G., & Chen, Y. (2023). Neuromodulation Gated Transformer. arXiv preprint arXiv:2305.03232.

  • Benavides-Prado, D., & Riddle, P. (2022). A Theory for Knowledge Transfer in Continual Learning. Proceedings of the Conference on Lifelong Learning Agents (CoLLAs). Available at: arXiv preprint arXiv:2208.06931.

  • Bensemann, J., Peng, A., Benavides-Prado, D., Chen, Y., Tan, N., Corballis, P. M., … & Witbrock, M. J. (2022). Eye Gaze and Self-attention: How Humans and Transformers Attend Words in Sentences. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp. 75-87).

  • Elmes, K., Benavides-Prado, D., Tan, N. O., Nguyen, T. B., Sumpter, N., Leask, M., … & Gavryushkin, A. (2022). SNVformer: An Attention-based Deep Neural Network for GWAS Data. ICML Computational Biology Workshop. Available at: https://www.biorxiv.org/content/10.1101/2022.07.07.499217v2.abstract.

  • Sadiq, S., Aryani, A., Demartini, G., Hua, W., Indulska, M., Burton-Jones, A., Khosravi, H., Benavides-Prado, D., … & Zhou, X. (2022). Information Resilience: the nexus of responsible and agile approaches to information use. The VLDB Journal, 1-26.

  • Tan, N. Ö., Bensemann, J., Benavides-Prado, D., Chen, Y., Gahegan, M., Lee, L., … & Witbrock, M. (2021). An explainability analysis of a sentiment prediction task using a transformerbased attention filter. In Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems.

  • Benavides-Prado, D., Wanigasekara, C., & Swain, A. (2021). Generalised Controller Design Using Continual Learning. In International Conference on Artificial Neural Networks (pp. 397-408). Springer, Cham.

  • Cory, C., Benavides-Prado, D., & Koh, Y. S. (2021). Continual Correction of Errors Using Smart Memory Replay. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.

  • Vaithianathan, R., Benavides-Prado, D., Dalton, E., Chouldechova, A., & Putnam-Hornstein, E. (2021). Using a machine learning tool to support high-stakes decisions in child protection. AI Magazine, 42(1), 53-60.

  • Vaithianathan, R., Putnam-Hornstein, E., Chouldechova, A., Benavides-Prado, D., & Berger, R. (2020). Hospital injury encounters of children identified by a predictive risk model for screening child maltreatment referrals: evidence from the Allegheny Family Screening Tool. JAMA pediatrics, 174(11), e202770-e202770.

  • Vaithianathan, R., Benavides-Prado, D., & Putnam-Hornstein, E. (2020). Implementing the Hello Baby Prevention Program in Allegheny County.

  • Benavides Prado, D. (2020). Beyond Catastrophic Forgetting in Continual Learning: An Attempt with SVM. In ICML. The International Conference on Machine Learning (ICML).

  • Benavides-Prado, D., Koh, Y. S., & Riddle, P. (2020). Towards knowledgeable supervised lifelong learning systems. Journal of Artificial Intelligence Research, 68, 159-224.

  • Benavides-Prado, D. (2019). An SVM-based framework for long-term learning systems. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 9915-9916).

  • Vaithianathan, R., Kulick, E., Putnam-Hornstein, E., & Benavides-Prado, D. (2019). Allegheny family screening tool: Methodology, version 2. Center for Social Data Analytics, 1-22.

  • Vaithianathan, R., Dinh, H., Kalisher, A., Kithulgoda, C., Kulick, E., Mayur, M., … & Prado, D. B. (2019). Implementing a Child Welfare Decision Aide in Douglas County: Methodology Report. Centre for Social Data Analytics, Auckland.

  • Benavides-Prado, D., Koh, Y. S., & Riddle, P. (2019). Selective hypothesis transfer for lifelong learning. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-10). IEEE.

  • Benavides Prado, D. (2019). A framework for long-term learning systems (Doctoral dissertation, ResearchSpace@Auckland).

  • Chouldechova, A., Benavides-Prado, D., Fialko, O., & Vaithianathan, R. (2018). A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions. In Conference on Fairness, Accountability and Transparency (pp. 134-148). PMLR.

  • Benavides-Prado, D., Koh, Y. S., & Riddle, P. (2018). Measuring Cumulative Gain of Knowledgeable Lifelong Learners. In NeurIPS Continual Learning Workshop (pp. 1-8).

  • Benavides-Prado, D., Koh, Y. S., & Riddle, P. (2017). AccGenSVM: Selectively transferring from previous hypotheses. In Proc. Intern. Joint Conf. Artificial Intel (pp. 1440-1446).

  • Benavides-Prado, D. (2017). A framework for long-term learning systems. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (pp. 5167-5168).

  • Benavides-Prado, D. (2015). MOGACAR: A Method for Filtering Interesting Classification Association Rules. In International Workshop on Machine Learning and Data Mining in Pattern Recognition (pp. 172-183). Springer, Cham.

  • Benavides-Prado, D. K. (2012). KDBuss framework-knowledge discovery with association rules in the business context. Uniandes Masters Dissertation.