Dhawan, Nikita

9 publications

TMLR 2026 Bayesian Sensitivity of Causal Inference Estimators Under Evidence-Based Priors Nikita Dhawan, Daniel Shen, Leonardo Cotta, Chris J. Maddison

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

ICMLW 2024 End-to-End Causal Effect Estimation from Unstructured Natural Language Data Nikita Dhawan, Leonardo Cotta, Karen Ullrich, Rahul Krishnan, Chris J. Maddison

TMLR 2024 Leveraging Function Space Aggregation for Federated Learning at Scale Nikita Dhawan, Nicole Elyse Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite

ICMLW 2024 PAIR: Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations Haonan Duan, Marta Skreta, Leonardo Cotta, Ella Miray Rajaonson, Nikita Dhawan, Alan Aspuru-Guzik, Chris J. Maddison

ICML 2023 Efficient Parametric Approximations of Neural Network Function Space Distance Nikita Dhawan, Sicong Huang, Juhan Bae, Roger Baker Grosse

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

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

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