Artificial Intelligence

Allen Schmaltz and Danielle Rasooly. 2022. Introspection, Updatability, and Uncertainty Quantification with Transformers: Concrete Methods for AI Safety.
     December 2022, ML Safety Workshop, 36th Conference on Neural Information Processing Systems (NeurIPS 2022). Poster.

Allen Schmaltz and Danielle Rasooly. 2022. Approximate Conditional Coverage & Calibration via Neural Model Approximations. arXiv preprint arXiv:2205.14310.
     Spotlight talk, July 2022, Workshop on Distribution-Free Uncertainty Quantification at the Thirty-ninth International Conference on Machine Learning (ICML 2022), Baltimore, Maryland.

Allen Schmaltz. 2021. Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition. Computational Linguistics. https://doi.org/10.1162/coli_a_00416. Online Appendix. Code.

Natural Language Processing

Allen Schmaltz. 2019. Learning to Order & Learning to Correct. Harvard University, Ph.D. dissertation, Computer Science.

Allen Schmaltz, Yoon Kim, Alexander Rush, and Stuart Shieber. 2017. Adapting Sequence Models for Sentence Correction. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2807-2813, Copenhagen, Denmark, September. Association for Computational Linguistics. https://www.aclweb.org/anthology/D17-1298. (Appendix) (.bib)

Allen Schmaltz, Alexander M. Rush, and Stuart Shieber. 2016. Word Ordering Without Syntax. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2319-2324, Austin, TX, USA, November. Association for Computational Linguistics. https://aclweb.org/anthology/D16-1255. (.bib)

Allen Schmaltz, Yoon Kim, Alexander M. Rush, and Stuart Shieber. 2016. Sentence-Level Grammatical Error Identification as Sequence-to-Sequence Correction. In Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications, pages 242-251, San Diego, CA, USA, June. Association for Computational Linguistics. https://www.aclweb.org/anthology/W16-0528. (.bib)

Medicine and Public Health

Allen Schmaltz and Andrew L. Beam. 2020. Sharpening the Resolution on Data Matters: A Brief Roadmap for Understanding Deep Learning for Medical Data. The Spine Journal. https://doi.org/10.1016/j.spinee.2020.08.012.

Andrew L. Beam, Benjamin Kompa, Allen Schmaltz, Inbar Fried, Griffin Weber, Nathan P. Palmer, Xu Shi, Tianxi Cai, and Isaac S. Kohane. 2020. Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Data. In Proceedings of the Pacific Symposium on Biocomputing (PSB) 25, pages 295-306. arXiv:1804.01486.

Public Policy

Allen Schmaltz. 2018. On the Utility of Lay Summaries and AI Safety Disclosures: Toward Robust, Open Research Oversight. In Proceedings of the Second ACL Workshop on Ethics in Natural Language Processing, pages 1-6, New Orleans, LA, USA, June. Association for Computational Linguistics. https://aclweb.org/anthology/W18-0801. (.bib)

Quantitative Social Science

Wenxin Jiang, Gary King, Allen Schmaltz, and Martin A. Tanner. 2019. Ecological Regression with Partial Identification. Political Analysis. https://doi.org/10.1017/pan.2019.19.

Working Papers

Allen Schmaltz and Andrew Beam. 2020. Coarse-to-Fine Memory Matching for Joint Retrieval and Classification. arXiv preprint arXiv:2012.02287.

Allen Schmaltz and Andrew Beam. 2020. Exemplar Auditing for Multi-Label Biomedical Text Classification. arXiv preprint arXiv:2004.03093.