David McAllester

According to our database, David McAllester authored at least 15 papers between 2013 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

OKBench: Democratizing LLM Evaluation with Fully Automated, On-Demand, Open Knowledge Benchmarking.

Yanhong Li, Tianyang Xu, Kenan Tang, Karen Livescu, David McAllester, Jiawei Zhou

CoRR, November, 2025

Context-Efficient Retrieval with Factual Decomposition.

Yanhong Li, David Yunis, David McAllester, Jiawei Zhou

Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

PENCIL: Long Thoughts with Short Memory.

Chenxiao Yang, Nathan Srebro, David McAllester, Zhiyuan Li

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2023

On the Mathematics of Diffusion Models.

David McAllester

CoRR, 2023

2021

Dependent Type Theory as Related to the Bourbaki Notions of Structure and Isomorphism.

David McAllester

CoRR, 2021

Domain-Independent Dominance of Adaptive Methods.

Pedro Savarese, David McAllester, Sudarshan Babu, Michael Maire

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

Information-Theoretic Segmentation by Inpainting Error Maximization.

Pedro Savarese, Sunnie S. Y. Kim, Michael Maire, Greg Shakhnarovich, David McAllester

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020

MathZero, The Classification Problem, and Set-Theoretic Type Theory.

David McAllester

CoRR, 2020

Formal Limitations on the Measurement of Mutual Information.

David McAllester, Karl Stratos

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

On-The-Fly Information Retrieval Augmentation for Language Models.

Hai Wang, David McAllester

Proceedings of the First Joint Workshop on Narrative Understanding, Storylines, and Events, 2020

2018

Information Theoretic Co-Training.

David McAllester

CoRR, 2018

2017

A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks.

Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nathan Srebro

CoRR, 2017

Exploring Generalization in Deep Learning.

Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2015

A Synopsis of Morphoid Type Theory.

David McAllester

Proceedings of the 14th Meeting on the Mathematics of Language, 2015

2013

PAC-Bayesian Theory.

David McAllester, Takintayo Akinbiyi

Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013