An Advanced Private Social Activity Invitation Framework with Friendship Protection. - Weitian Tong; Lei Chen 029; Scott Buglass; Weinan Gao; Jeffrey Li (Wirel. Commun. Mob. Comput., 2017)

Differentially Private Meta-Learning. - Jeffrey Li; Mikhail Khodak; Sebastian Caldas; Ameet Talwalkar (ICLR, 2020)

A Learning Theoretic Perspective on Local Explainability. - Jeffrey Li; Vaishnavh Nagarajan; Gregory Plumb; Ameet Talwalkar (ICLR, 2021)

Towards Connecting Use Cases and Methods in Interpretable Machine Learning. - Valerie Chen; Jeffrey Li; Joon Sik Kim; Gregory Plumb; Ameet Talwalkar (CoRR, 2021)

Interpretable Machine Learning: Moving from mythos to diagnostics. - Valerie Chen; Jeffrey Li; Joon Sik Kim; Gregory Plumb; Ameet Talwalkar (ACM Queue, 2021)

Characterizing the Impacts of Semi-supervised Learning for Weak Supervision. - Jeffrey Li; Jieyu Zhang 001; Ludwig Schmidt; Alexander J. Ratner (NeurIPS, 2023)

Better Alignment with Instruction Back-and-Forth Translation. - Thao Nguyen; Jeffrey Li; Sewoong Oh; Ludwig Schmidt; Jason Weston; Luke Zettlemoyer; Xian Li 003 (EMNLP (Findings), 2024)

DataComp-LM: In search of the next generation of training sets for language models. - Jeffrey Li; Alex Fang; Georgios Smyrnis; Maor Ivgi; Matt Jordan; Samir Yitzhak Gadre; Hritik Bansal; Etash Kumar Guha; Sedrick Scott Keh; Kushal Arora; Saurabh Garg; Rui Xin; et al. (NeurIPS, 2024)

Stronger Than You Think: Benchmarking Weak Supervision on Realistic Tasks. - Tianyi Zhang 015; Linrong Cai; Jeffrey Li; Nicholas Roberts; Neel Guha; Frederic Sala (NeurIPS, 2024)

Language models scale reliably with over-training and on downstream tasks. - Samir Yitzhak Gadre; Georgios Smyrnis; Vaishaal Shankar; Suchin Gururangan; Mitchell Wortsman; Rulin Shao; Jean Mercat; Alex Fang; Jeffrey Li; Sedrick Keh; Rui Xin; Marianna Nezhurina; et al. (CoRR, 2024)

SDS - See it, Do it, Sorted: Quadruped Skill Synthesis from Single Video Demonstration. - Jeffrey Li; Maria Stamatopoulou; Dimitrios Kanoulas (CoRR, 2024)

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data. - Louis Mullie; Jonathan Afilalo; Patrick M. Archambault; Rima Bouchakri; Kip Brown; David L. Buckeridge; Yiorgos Alexandros Cavayas; Alexis F. Turgeon; Denis Martineau; François Lamontagne; Martine Lebrasseur; Renald Lemieux; et al. (J. Am. Medical Informatics Assoc., 2024)

TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining. - Jeffrey Li; Mohammadreza Armandpour; Iman Mirzadeh; Sachin Mehta; Vaishaal Shankar; Raviteja Vemulapalli; Samy Bengio; Oncel Tuzel; Mehrdad Farajtabar; Hadi Pouransari; Fartash Faghri (ACL (1), 2025)

Identifying Effective Praise in Tutoring: Large Language Models with Transparent Explanations. - Eason Chen; Jeffrey Li; Scarlett Huang; Xinyi Tang; Jionghao Lin; Paulo Carvalho 004; Kenneth R. Koedinger (AIED (6), 2025)

Language models scale reliably with over-training and on downstream tasks. - Samir Yitzhak Gadre; Georgios Smyrnis; Vaishaal Shankar; Suchin Gururangan; Mitchell Wortsman; Rulin Shao; Jean Mercat; Alex Fang; Jeffrey Li; Sedrick Keh; Rui Xin; Marianna Nezhurina; et al. (ICLR, 2025)

Stronger Than You Think: Benchmarking Weak Supervision on Realistic Tasks. - Tianyi Zhang 015; Linrong Cai; Jeffrey Li; Nicholas Roberts; Neel Guha; Jinoh Lee; Frederic Sala (CoRR, 2025)

OpenThoughts: Data Recipes for Reasoning Models. - Etash Kumar Guha; Ryan Marten; Sedrick Keh; Negin Raoof; Georgios Smyrnis; Hritik Bansal; Marianna Nezhurina; Jean Mercat; Trung Vu 002; Zayne Sprague; Ashima Suvarna; Benjamin Feuer; et al. (CoRR, 2025)

Language Models Improve When Pretraining Data Matches Target Tasks. - David Mizrahi; Anders Boesen Lindbo Larsen; Jesse Allardice; Suzie Petryk; Yuri Gorokhov; Jeffrey Li; Alex Fang; Josh Gardner 001; Tom Gunter; Afshin Dehghan (CoRR, 2025)