Rakshit Naidu

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 20

Top Topics

Differentially Private

Practical Guidelines

Top Venues

Preregister@NeurIPS

Publications

Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani
Personalized Differential Privacy for Ridge Regression. CoRR (2024)
Emily Black, Rakshit Naidu, Rayid Ghani, Kit T. Rodolfa, Daniel E. Ho, Hoda Heidari
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools. CoRR (2023)
Emily Black, Rakshit Naidu, Rayid Ghani, Kit T. Rodolfa, Daniel E. Ho, Hoda Heidari
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools. EAAMO (2023)
Aman Priyanshu, Supriti Vijay, Ayush Kumar, Rakshit Naidu, Fatemehsadat Mireshghallah
Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization. CoRR (2023)
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, Rakshit Naidu
Pruning has a disparate impact on model accuracy. NeurIPS (2022)
Saswat Das, Rakshit Naidu
Fair Context-Aware Privacy Threat Modelling. CoRR (2022)
Tiasa Singha Roy, Priyam Basu, Aman Priyanshu, Rakshit Naidu
Interpretability of Fine-grained Classification of Sadness and Depression. CoRR (2022)
Rakshit Naidu, Navid Kagalwalla
Can Causal (and Counterfactual) Reasoning improve Privacy Threat Modelling? CoRR (2022)
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, Rakshit Naidu
Pruning has a disparate impact on model accuracy. CoRR (2022)
Rakshit Naidu, Aman Priyanshu, Aadith Kumar, Sasikanth Kotti, Haofan Wang, Fatemehsadat Mireshghallah
When Differential Privacy Meets Interpretability: A Case Study. CoRR (2021)
Aman Priyanshu, Rakshit Naidu, Fatemehsadat Mireshghallah, Mohammad Malekzadeh
Efficient Hyperparameter Optimization for Differentially Private Deep Learning. CoRR (2021)
Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, Fatemehsadat Mireshghallah, Andrew Trask
DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy? CoRR (2021)
Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zümrüt Müftüoglu, Sahib Singh, Fatemehsadat Mireshghallah
Benchmarking Differential Privacy and Federated Learning for BERT Models. CoRR (2021)
Rakshit Naidu, Harshita Diddee, Ajinkya Mulay, Aleti Vardhan, Krithika Ramesh, Ahmed Zamzam
Towards Quantifying the Carbon Emissions of Differentially Private Machine Learning. CoRR (2021)
Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zümrüt Müftüoglu
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning. CoRR (2021)
Aman Priyanshu, Rakshit Naidu
FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic. CoRR (2021)
Ajinkya Mulay, Gaspard Baye, Rakshit Naidu, Santiago González-Toral, Vineeth S, Tushar Semwal, Ayush Manish Agrawal
FedPerf: A Practitioners' Guide to Performance of Federated Learning Algorithms. Preregister@NeurIPS (2020)
Rakshit Naidu, Ankita Ghosh, Yash Maurya, Shamanth R. Nayak K, Soumya Snigdha Kundu
IS-CAM: Integrated Score-CAM for axiomatic-based explanations. CoRR (2020)
Rakshit Naidu, Joy Michael
SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization. CoRR (2020)
Jithin Sunny, Joel Jogy, Rohan Rout, Rakshit Naidu
TeleVital: Enhancing the quality of contactless health assessment. CoRR (2020)