Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond. - Shuxiao Chen; Edgar Dobriban; Jane H. Lee (CoRR, 2019)

A Group-Theoretic Framework for Data Augmentation. - Shuxiao Chen; Edgar Dobriban; Jane H. Lee (NeurIPS, 2020)

Learning from Noisy Labels with No Change to the Training Process. - Mingyuan Zhang; Jane H. Lee; Shivani Agarwal 001 (ICML, 2021)

The rural-urban stress divide: Obtaining geographical insights through Twitter. - Kokil Jaidka; Sharath Chandra Guntuku; Jane H. Lee; Zhengyi Luo; Anneke Buffone; Lyle H. Ungar (Comput. Hum. Behav., 2021)

Exact Gradient Computation for Spiking Neural Networks Through Forward Propagation. - Jane H. Lee; Saeid Haghighatshoar; Amin Karbasi (CoRR, 2022)

Exact Gradient Computation for Spiking Neural Networks via Forward Propagation. - Jane H. Lee; Saeid Haghighatshoar; Amin Karbasi (AISTATS, 2023)

Learning Exponential Families from Truncated Samples. - Jane H. Lee; Andre Wibisono; Emmanouil Zampetakis (NeurIPS, 2023)

Efficient Statistics With Unknown Truncation, Polynomial Time Algorithms, Beyond Gaussians. - Jane H. Lee; Anay Mehrotra; Manolis Zampetakis (FOCS, 2024)

Learning with Positive and Imperfect Unlabeled Data. - Jane H. Lee; Anay Mehrotra; Manolis Zampetakis (CoRR, 2025)