PAC-Bayes Learning Bounds for Sample-Dependent Priors. - Pranjal Awasthi; Satyen Kale; Stefani Karp; Mehryar Mohri (NeurIPS, 2020)

Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels. - Stefani Karp; Ezra Winston; Yuanzhi Li; Aarti Singh (NeurIPS, 2021)

Agnostic Learnability of Halfspaces via Logistic Loss. - Ziwei Ji; Kwangjun Ahn; Pranjal Awasthi; Satyen Kale; Stefani Karp (ICML, 2022)

Efficient Training of Language Models using Few-Shot Learning. - Sashank J. Reddi; Sobhan Miryoosefi; Stefani Karp; Shankar Krishnan; Satyen Kale; Seungyeon Kim 001; Sanjiv Kumar (ICML, 2023)

Applying statistical learning theory to deep learning. - Cédric Gerbelot; Avetik Karagulyan; Stefani Karp; Kavya Ravichandran; Menachem Stern; Nathan Srebro (CoRR, 2023)

Role of Locality and Weight Sharing in Image-Based Tasks: A Sample Complexity Separation between CNNs, LCNs, and FCNs. - Aakash Lahoti; Stefani Karp; Ezra Winston; Aarti Singh; Yuanzhi Li (ICLR, 2024)

On the Inductive Bias of Stacking Towards Improving Reasoning. - Nikunj Saunshi; Stefani Karp; Shankar Krishnan; Sobhan Miryoosefi; Sashank Jakkam Reddi; Sanjiv Kumar (NeurIPS, 2024)

Landscape-Aware Growing: The Power of a Little LAG. - Stefani Karp; Nikunj Saunshi; Sobhan Miryoosefi; Sashank J. Reddi; Sanjiv Kumar (CoRR, 2024)

Gemini Robotics: Bringing AI into the Physical World. - Gemini Robotics Team; Saminda Abeyruwan; Joshua Ainslie; Jean-Baptiste Alayrac; Montserrat Gonzalez Arenas; Travis Armstrong; Ashwin Balakrishna; Robert Baruch; Maria Bauzá 001; Michiel Blokzijl; Steven Bohez; Konstantinos Bousmalis; et al. (CoRR, 2025)