Andrew McCallum

Distinguished Professor@University of Massachusetts Amherst·美国·OpenReview

研究方向

deep learning · natural language processing · knowledge bases · question answering · information integration · entity resolution · conditional random fields · machine learning · topic models · social network analysis · computational social science

5.0

Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions

ICLR 2026Rejected

通讯

4.0

A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings

ICML 2025Poster

三作

4.3

A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings

合作者 (20)

Andrew McCallum

Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions

MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time

CosyCPT: Coreness-Aware Synthetic Continued Pretraining

AutoDiscovery: Open-ended Scientific Discovery via Bayesian Surprise

A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings

A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings