On the design of convolutional neural networks for automatic detection of Alzheimer's disease. - Sheng Liu; Chhavi Yadav; Carlos Fernandez-Granda; Narges Razavian (ML4H@NeurIPS, 2019)

Cold Case: The Lost MNIST Digits. - Chhavi Yadav; Léon Bottou (NeurIPS, 2019)

Behavior of k-NN as an Instance-Based Explanation Method. - Chhavi Yadav; Kamalika Chaudhuri (PKDD/ECML Workshops (1), 2021)

A Learning-Theoretic Framework for Certified Auditing of Machine Learning Models. - Chhavi Yadav; Michal Moshkovitz; Kamalika Chaudhuri (CoRR, 2022)

Keeping Up with the Language Models: Robustness-Bias Interplay in NLI Data and Models. - Ioana Baldini; Chhavi Yadav; Payel Das; Kush R. Varshney (CoRR, 2023)

FairProof : Confidential and Certifiable Fairness for Neural Networks. - Chhavi Yadav; Amrita Roy Chowdhury 001; Dan Boneh; Kamalika Chaudhuri (ICML, 2024)

Influence-based Attributions can be Manipulated. - Chhavi Yadav; Ruihan Wu; Kamalika Chaudhuri (CoRR, 2024)

Evaluating Deep Unlearning in Large Language Models. - Ruihan Wu; Chhavi Yadav; Russ Salakhutdinov; Kamalika Chaudhuri (CoRR, 2024)

XAudit : A Learning-Theoretic Look at Auditing with Explanations. - Chhavi Yadav; Michal Moshkovitz; Kamalika Chaudhuri (Trans. Mach. Learn. Res., 2024)

The Mainstays of Trustworthy Machine Learning. - Chhavi Yadav (AAAI, 2025)

ExpProof : Operationalizing Explanations for Confidential Models with ZKPs. - Chhavi Yadav; Evan Laufer; Dan Boneh; Kamalika Chaudhuri (CoRR, 2025)

Can We Infer Confidential Properties of Training Data from LLMs? - Pengrun Huang; Chhavi Yadav; Ruihan Wu; Kamalika Chaudhuri (CoRR, 2025)