In machine learning, artificial intelligence and computational linguistics, full conference papers go through the same peer review as journal articles and are generally ranked above them, which is why they come first here.
Refereed Conference Articles
Zhang, L., Lieffers, J., & Pyarelal, A. (2025). Enhancing interpretability in deep reinforcement learning through semantic clustering. The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), 55908–55940. https://doi.org/10.52202/085713-1673
Pyarelal, A., Culnan, J. M., Qamar, A., Krishnaswamy, M., Wang, Y., Jeong, C., Chen, C., Miah, M. M. M., Hormozi, S., Tong, J., & Huang, R. (2025). MultiCAT: Multimodal communication annotations for teams. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Findings of the association for computational linguistics: NAACL 2025 (pp. 1077–1111). Association for Computational Linguistics. https://aclanthology.org/2025.findings-naacl.61/
Noriega-Atala, E., Vacareanu, R., Ashton, S. T., Pyarelal, A., Morrison, C. T., & Surdeanu, M. (2024). When and where did it happen? An encoder-decoder model to identify scenario context. In Y. Al-Onaizan, M. Bansal, & Y.-N. Chen (Eds.), Findings of the association for computational linguistics: EMNLP 2024 (pp. 3821–3829). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-emnlp.219
Soares, P., Pyarelal, A., Krishnaswamy, M., Butler, E., & Barnard, K. (2024). Probabilistic modeling of interpersonal coordination processes. Forty-First International Conference on Machine Learning (ICML 2024). https://openreview.net/forum?id=4zOZ0yKhm6
Pyarelal, A., Duong, E., Shibu, C. J., Soares, P., Boyd, S., Khosla, P., Pfeifer, V., Zhang, D., Andrews, E. S., Champlin, R., Raymond, V. P., Krishnaswamy, M., Morrison, C., Butler, E., & Barnard, K. (2023). The ToMCAT dataset. Thirty-Seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 40872–40923. https://doi.org/10.52202/075280-1781
Qamar, A., Pyarelal, A., & Huang, R. (2023). Who is speaking? Speaker-aware multiparty dialogue act classification. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the association for computational linguistics: EMNLP 2023 (pp. 10122–10135). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.678
Miah, M. M. M., Pyarelal, A., & Huang, R. (2023). Hierarchical fusion for online multimodal dialog act classification. In H. Bouamor, J. Pino, & K. Bali (Eds.), Findings of the association for computational linguistics: EMNLP 2023 (pp. 7532–7545). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.505
Alexeeva, M., Sharp, R., Valenzuela-Escárcega, M. A., Kadowaki, J., Pyarelal, A., & Morrison, C. (2020). MathAlign: Linking formula identifiers to their contextual natural language descriptions. Proceedings of the 12th Language Resources and Evaluation Conference, 2204–2212. https://www.aclweb.org/anthology/2020.lrec-1.269
Sharp, R., Pyarelal, A., Gyori, B., Alcock, K., Laparra, E., Valenzuela-Escárcega, M. A., Nagesh, A., Yadav, V., Bachman, J., Tang, Z., Lent, H., Luo, F., Paul, M., Bethard, S., Barnard, K., Morrison, C., & Surdeanu, M. (2019). Eidos, INDRA, & delphi: From free text to executable causal models. In W. Ammar, A. Louis, & N. Mostafazadeh (Eds.), Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics (demonstrations) (pp. 42–47). Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-4008
Refereed Journal Articles
Erikson, J. A., Alt, M., Pyarelal, A., & Kapa, L. (2024). Science vocabulary and science achievement in children with developmental language disorder and typical language development. Language, Speech, and Hearing Services in Schools. https://doi.org/10.1044/2024_LSHSS-24-00025
Pyarelal, A., & Su, S. (2020). Higgs assisted razor search for higgsinos at a 100 TeV \(pp\) collider. Science China Physics, Mechanics & Astronomy. https://doi.org/10.1007/s11433-019-1517-5
Kling, F., Li, H., Pyarelal, A., Song, H., & Su, S. (2019). Exotic higgs decays in type-II 2HDMs at the LHC and future 100 TeV hadron colliders. Journal of High Energy Physics, 2019(6), 31. https://doi.org/10.1007/JHEP06(2019)031
Kling, F., Pyarelal, A., & Su, S. (2015). Light Charged Higgs Bosons to AW/HW via Top Decay. Journal of High Energy Physics, 11, 051. https://doi.org/10.1007/JHEP11(2015)051
Refereed Workshop Articles
Liu, C., Noriega-Atala, E., Pyarelal, A., Morrison, C. T., & Cafarella, M. (2025). Variable extraction for model recovery in scientific literature. In P. Jansen, B. Dalvi Mishra, H. Trivedi, B. Prasad Majumder, T. Hope, T. Khot, D. Downey, & E. Horvitz (Eds.), Proceedings of the 1st workshop on AI and scientific discovery: Directions and opportunities (pp. 1–12). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.aisd-main.1
Zhang, L., Lieffers, J., Shivanna, P., & Pyarelal, A. (2024). Deep reinforcement learning with vector quantized encoding. Workshop on Interpretable Policies in Reinforcement Learning @RLC-2024. https://openreview.net/forum?id=OyHqrdWADY
Nitschke, R., Wang, Y., Chen, C., Pyarelal, A., & Sharp, R. (2022). Rule based event extraction for artificial social intelligence. Proceedings of the First Workshop on Pattern-Based Approaches to NLP in the Age of Deep Learning, 71–84. https://aclanthology.org/2022.pandl-1.9
Kling, F., Li, H., Li, S., Pyarelal, A., Song, H., Su, S., & Su, W. (2022, May). Exotic Higgs Decays in the Type-II 2HDMs at Current and Future pp Colliders. 2022 Snowmass Summer Study. https://arxiv.org/abs/2205.12198
Zhang, L., Lieffers, J., & Pyarelal, A. (2021, November). Using features at multiple temporal and spatial resolutions to predict human behavior in real time. AAAI Fall Symposium on Computational Theory of Mind for Human-Machine Teams. https://arxiv.org/abs/2211.06721
Soares, P., Pyarelal, A., & Barnard, K. (2021, November). Probabilistic modeling of human teams to infer false beliefs. AAAI Fall Symposium on Computational Theory of Mind for Human-Machine Teams. https://doi.org/10.48550/arXiv.2310.12929
Pyarelal, A., Banerjee, A., & Barnard, K. (2021, November). Modular procedural generation for voxel maps. AAAI Fall Symposium on Computational Theory of Mind for Human-Machine Teams. https://arxiv.org/abs/2104.08890
Pyarelal, A., Valenzuela-Escárcega, M. A., Sharp, R., Hein, P. D., Stephens, J., Bhandari, P., Lim, H., Debray, S., & Morrison, C. T. (2019, May). AutoMATES: Automated model assembly from text, equations, and software. Modeling the World’s Systems. https://arxiv.org/abs/2001.07295
Pyarelal, A., Sharp, R., Morrison, C., & Barnard, K. (2019, May). Interpreting causal expressions with gradable adjectives to assemble dynamics models. Modeling the World’s Systems.
Sharp, R., Pyarelal, A., Gyori, B., Alcock, K., Laparra, E., Valenzuela-Escárcega, M. A., Nagesh, A., Yadav, V., Bachman, J., Tang, Z., Lent, H., Luo, F., Paul, M., Bethard, S., Barnard, K., Morrison, C., & Surdeanu, M. (2019, May). Eidos, INDRA, & delphi: From free text to executable causal models. Modeling the World’s Systems.
Chapters in scholarly books and monographs
Zhang, L., Lieffers, J., & Pyarelal, A. (2022). Using features at multiple temporal and spatial resolutions to predict human behavior in real time. In N. Gurney & G. Sukthankar (Eds.), Computational theory of mind for human-machine teams (Vol. 13775, pp. 205–219). Springer, Cham. https://doi.org/10.1007/978-3-031-21671-8_13
Pyarelal, A., Banerjee, A., & Barnard, K. (2022). Modular procedural generation for voxel maps. In N. Gurney & G. Sukthankar (Eds.), Computational theory of mind for human-machine teams (Vol. 13775, pp. 85–101). Springer, Cham. https://doi.org/10.1007/978-3-031-21671-8_6
Talks
- Building machines that understand humans — Cognitive Science Colloquium, September 4, 2020. (Video, 619 MB.)