PAC-Bayesian Theory. - David McAllester; Takintayo Akinbiyi (Empirical Inference, 2013)

A Synopsis of Morphoid Type Theory. - David McAllester (MOL, 2015)

Exploring Generalization in Deep Learning. - Behnam Neyshabur; Srinadh Bhojanapalli; David McAllester; Nati Srebro (NIPS, 2017)

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. - Behnam Neyshabur; Srinadh Bhojanapalli; David McAllester; Nathan Srebro (CoRR, 2017)

Information Theoretic Co-Training. - David McAllester (CoRR, 2018)

On-The-Fly Information Retrieval Augmentation for Language Models. - Hai Wang; David McAllester (NUSE@ACL, 2020)

Formal Limitations on the Measurement of Mutual Information. - David McAllester; Karl Stratos (AISTATS, 2020)

MathZero, The Classification Problem, and Set-Theoretic Type Theory. - David McAllester (CoRR, 2020)

Information-Theoretic Segmentation by Inpainting Error Maximization. - Pedro Savarese; Sunnie S. Y. Kim; Michael Maire; Greg Shakhnarovich; David McAllester (CVPR, 2021)

Domain-Independent Dominance of Adaptive Methods. - Pedro Savarese; David McAllester; Sudarshan Babu; Michael Maire (CVPR, 2021)

Dependent Type Theory as Related to the Bourbaki Notions of Structure and Isomorphism. - David McAllester (CoRR, 2021)

On the Mathematics of Diffusion Models. - David McAllester (CoRR, 2023)

Context-Efficient Retrieval with Factual Decomposition. - Yanhong Li; David Yunis; David McAllester; Jiawei Zhou (NAACL (Short Papers), 2025)

PENCIL: Long Thoughts with Short Memory. - Chenxiao Yang; Nathan Srebro; David McAllester; Zhiyuan Li 005 (CoRR, 2025)