AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos. - Laura M. Smith; Nikita Dhawan; Marvin Zhang; Pieter Abbeel; Sergey Levine (Robotics: Science and Systems, 2020)

Adaptive Risk Minimization: Learning to Adapt to Domain Shift. - Marvin Zhang; Henrik Marklund; Nikita Dhawan; Abhishek Gupta 004; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

On the Difficulty of Defending Self-Supervised Learning against Model Extraction. - Adam Dziedzic; Nikita Dhawan; Muhammad Ahmad Kaleem; Jonas Guan; Nicolas Papernot (ICML, 2022)

Dataset Inference for Self-Supervised Models. - Adam Dziedzic; Haonan Duan 002; Muhammad Ahmad Kaleem; Nikita Dhawan; Jonas Guan; Yannis Cattan; Franziska Boenisch; Nicolas Papernot (NeurIPS, 2022)

Efficient Parametric Approximations of Neural Network Function Space Distance. - Nikita Dhawan; Sicong Huang 001; Juhan Bae; Roger Baker Grosse (ICML, 2023)

End-To-End Causal Effect Estimation from Unstructured Natural Language Data. - Nikita Dhawan; Leonardo Cotta; Karen Ullrich; Rahul G. Krishnan; Chris J. Maddison (NeurIPS, 2024)

Leveraging Function Space Aggregation for Federated Learning at Scale. - Nikita Dhawan; Nicole Mitchell; Zachary Charles; Zachary Garrett; Gintare Karolina Dziugaite (Trans. Mach. Learn. Res., 2024)