"Less is More" in Bayesian Word Segmentation: When cognitively plausible learners outperform the ideal. - Lawrence Phillips; Lisa Pearl (CogSci, 2012)

Bayesian inference as a viable cross-linguistic word segmentation strategy: It's all about what's useful. - Lawrence Phillips; Lisa Pearl (CogSci, 2014)

Utility-based evaluation metrics for models of language acquisition: A look at speech segmentation. - Lawrence Phillips; Lisa Pearl (CMCL@NAACL-HLT, 2015)

The Utility of Cognitive Plausibility in Language Acquisition Modeling: Evidence From Word Segmentation. - Lawrence Phillips; Lisa Pearl (Cogn. Sci., 2015)

The Role of Empirical Evidence in Modeling Speech Segmentation. - Lawrence Phillips (2015)

Assessing the Linguistic Productivity of Unsupervised Deep Neural Networks. - Lawrence Phillips; Nathan Oken Hodas (CogSci, 2017)

Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams. - Lawrence Phillips; Kyle Shaffer; Dustin Arendt; Nathan Oken Hodas; Svitlana Volkova (Rep4NLP@ACL, 2017)

Using Social Media to Predict the Future: A Systematic Literature Review. - Lawrence Phillips; Chase Dowling; Kyle Shaffer; Nathan Oken Hodas; Svitlana Volkova (CoRR, 2017)

Predicting Foreign Language Usage from English-Only Social Media Posts. - Svitlana Volkova; Stephen Ranshous; Lawrence Phillips (NAACL-HLT (2), 2018)

Few-Shot Learning with Metric-Agnostic Conditional Embeddings. - Nathan Hilliard; Lawrence Phillips; Scott Howland; Artëm Yankov; Courtney D. Corley; Nathan O. Hodas (CoRR, 2018)

Sparse hierarchical representation learning on molecular graphs. - Matthias Bal; Hagen Triendl; Mariana Assmann; Michael Craig; Lawrence Phillips; Jarvist Moore Frost; Usman Bashir; Noor Shaker; Vid Stojevic (CoRR, 2019)

Metric-Based Few-Shot Learning for Video Action Recognition. - Chris Careaga; Brian Hutchinson; Nathan Oken Hodas; Lawrence Phillips (CoRR, 2019)

Explanatory Masks for Neural Network Interpretability. - Lawrence Phillips; Garrett B. Goh; Nathan O. Hodas (CoRR, 2019)

Intrinsic uncertainties and where to find them. - Francesco Farina; Lawrence Phillips; Nicola J. Richmond (CoRR, 2021)

Towards a Realistic Long-Term Benchmark for Open-Web Research Agents. - Peter Mühlbacher; Nikos I. Bosse; Lawrence Phillips (CoRR, 2024)

CellRep: A Multichannel Image Representation Learning Model. - Lawrence Phillips; Rory M. Donovan-Maiye (CVPR Workshops, 2025)

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries. - Josefa Lia Stoisser; Marc Boubnovski Martell; Kaspar Märtens; Lawrence Phillips; Stephen Michael Town; Rory M. Donovan-Maiye; Julien Fauqueur (CoRR, 2025)

Deep Research Bench: Evaluating AI Web Research Agents. - Nikos I. Bosse; Jon Evans; Robert G. Gambee; Daniel Hnyk; Peter Mühlbacher; Lawrence Phillips; Dan Schwarz; Jack Wildman (CoRR, 2025)

Bench to the Future: A Pastcasting Benchmark for Forecasting Agents. - Jack Wildman; Nikos I. Bosse; Daniel Hnyk; Peter Mühlbacher; Finn Hambly; Jon Evans; Dan Schwarz; Lawrence Phillips (CoRR, 2025)

STRuCT-LLM: Unifying Tabular and Graph Reasoning with Reinforcement Learning for Semantic Parsing. - Josefa Lia Stoisser; Marc Boubnovski Martell; Lawrence Phillips; Casper Hansen; Julien Fauqueur (CoRR, 2025)