Hi! I'm Chhavi.
I'm a Machine Learning (ML) researcher, broadly interested in the foundations of Trustworthy ML and AI Security & Safety. Specifically, I aim to make ML systems accountable by
developing trustless verification systems using cryptographic tools such as Zero-Knowledge Proofs ,
studying auditing of closed models both theoretically and practically ,
exposing vulnerabilities and understanding behavior of existing Trustworthy ML tools (unlearning, attribution, xai) and
proposing evaluation frameworks and metrics.
Currently, I'm pursuing a PhD (final year) in the Computer Science & Engineering Department at UC San Diego, advised by Prof. Kamalika Chaudhuri. I'm also a co-founder of The Trustworthy ML Initiative.
I'm open to collaborations! Please shoot me an email if you have a cool idea and want to work together :)
Selected Papers
For an up-to-date list, check out my Google Scholar Profile. * := equal contribution
ExpProof: Operationalizing Explanations for Confidential Models with ZKPs. Chhavi Yadav*, Evan Monroe Laufer*, Dan Boneh, Kamalika Chaudhuri
Evaluating Deep Unlearning in Large Language Models. Ruihan Wu, Chhavi Yadav, Russ Salakhutdinov, Kamalika Chaudhuri (Code)
Influence-based Attributions can be Manipulated. Chhavi Yadav*, Ruihan Wu*, Kamalika Chaudhuri
RegML and ATTRIB workshops @Neurips 2024
FairProof : Confidential and Certifiable Fairness for Neural Networks. Chhavi Yadav, Amrita Roy Chowdhury, Dan Boneh, Kamalika Chaudhuri
ICML 2024 (Code)(Short Blog)
🏆 TensorOpera-FedML Best Paper Award @Privacy-ILR Workshop ICLR 2024
Keeping Up with the Language Models: Systematic Benchmark Extension for Bias Auditing. Ioana Baldini Soares, Chhavi Yadav, Payel Das, Kush Varshney
TrustNLP @NAACL 2023
XAudit : A Theoretical Look at Auditing with Explanations. Chhavi Yadav, Michal Moshkovitz, Kamalika Chaudhuri
TMLR 2024
TrustAI Workshop IJCAI 2024 Oral
Behavior of k-NN as an Instance-Based Explanation Method. Chhavi Yadav, Kamalika Chaudhuri
Advances in Interpretable Machine Learning and Artificial Intelligence @ ECML PKDD 2021 Oral
Cold Case : The Lost MNIST Digits. Chhavi Yadav, Leon Bottou
NeurIPS 2019 Spotlight Oral Presentation ~2.9%
On the design of CNNs for automatic detection of Alzheimer’s disease. Sheng Liu, Chhavi Yadav, Carlos Fernandez-Granda, Narges Razavian
Machine Learning for Health Workshop , NeurIPS 2019 🏆 Best Paper Honorable Mention
Latest News
Dec 2024 : I will be at NeurIPS, presenting my papers on, FairProof @RegML workshop & Influence-based Attributions @RegML, ATTRIB workshops 🎉
Nov 2024 : I will be attending the Rising Star in Data Science'24 workshop and will be presenting my work there.
Oct 2024 : I am giving a talk on FairProof at the Encore Institute!
Sep 2024 : I have been selected as a Rising Star in Data Science'24 🎉
Sep 2024 : Organizing a workshop on Interpretability at NeurIPS 2024! Please check out https://interpretable-ai-workshop.github.io/ for details! 🎉
Aug 2024 : I am serving on the Program Committee of SaTML'25.
Aug 2024 : I gave a talk on XAudit at IJCAI Trustworthy AI Workshop 2024.
July 2024 : I presented FairProof at ICML 2024.
June 2024 : Delighted to have received the "Contributions to Diversity" Award from the CSE Dept @UCSD 🎉
May 2024 : My work FairProof received a Best Paper Award at the Privacy Workshop @ICLR 2024 🎊