Nikita Dhawan

14 papers A* 5Journal 8Unranked 1
YearRankTypeTitle / Venue / Authors
2026 J jnl
Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors.
Trans. Mach. Learn. Res.
Nikita Dhawan, Daniel Shen, Leonardo Cotta, Chris J. Maddison
2024 A* conf
End-To-End Causal Effect Estimation from Unstructured Natural Language Data.
NeurIPS
Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison
2024 J jnl
End-To-End Causal Effect Estimation from Unstructured Natural Language Data.
CoRR
Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul G. Krishnan, Chris J. Maddison
2024 J jnl
Leveraging Function Space Aggregation for Federated Learning at Scale.
Trans. Mach. Learn. Res.
Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
2023 A* conf
Efficient Parametric Approximations of Neural Network Function Space Distance.
ICML
Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Baker Grosse
2023 J jnl
Efficient Parametric Approximations of Neural Network Function Space Distance.
CoRR
Nikita Dhawan, Sicong Huang, Juhan Bae, Roger B. Grosse
2023 J jnl
Leveraging Function Space Aggregation for Federated Learning at Scale.
CoRR
Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
2022 A* conf
Dataset Inference for Self-Supervised Models.
NeurIPS
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
2022 J jnl
Dataset Inference for Self-Supervised Models.
CoRR
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem, Nikita Dhawan, Jonas Guan, Yannis Cattan, Franziska Boenisch, Nicolas Papernot
2022 A* conf
On the Difficulty of Defending Self-Supervised Learning against Model Extraction.
ICML
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
2022 J jnl
On the Difficulty of Defending Self-Supervised Learning against Model Extraction.
CoRR
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem, Jonas Guan, Nicolas Papernot
2021 A* conf
Adaptive Risk Minimization: Learning to Adapt to Domain Shift.
NeurIPS
Marvin Zhang, Henrik Marklund, Nikita Dhawan, Abhishek Gupta, Sergey Levine, Chelsea Finn
2020 — conf
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos.
Robotics: Science and Systems
Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine
2019 J jnl
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos.
CoRR
Laura Smith, Nikita Dhawan, Marvin Zhang, Pieter Abbeel, Sergey Levine