Search Results for author: Rakshit Naidu

Found 14 papers, 7 papers with code

Personalized Differential Privacy for Ridge Regression

1 code implementation30 Jan 2024 Krishna Acharya, Franziska Boenisch, Rakshit Naidu, Juba Ziani

DP requires to specify a uniform privacy level $\varepsilon$ that expresses the maximum privacy loss that each data point in the entire dataset is willing to tolerate.

regression

Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools

no code implementations29 Sep 2023 Emily Black, Rakshit Naidu, Rayid Ghani, Kit T. Rodolfa, Daniel E. Ho, Hoda Heidari

While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing fairness constraints during the optimization step, post-processing model outputs, or by manipulating the training data.

Fairness

Are Chatbots Ready for Privacy-Sensitive Applications? An Investigation into Input Regurgitation and Prompt-Induced Sanitization

no code implementations24 May 2023 Aman Priyanshu, Supriti Vijay, Ayush Kumar, Rakshit Naidu, FatemehSadat Mireshghallah

More specifically, we find that when ChatGPT is prompted to summarize cover letters of a 100 candidates, it would retain personally identifiable information (PII) verbatim in 57. 4% of cases, and we find this retention to be non-uniform between different subgroups of people, based on attributes such as gender identity.

In-Context Learning

Pruning has a disparate impact on model accuracy

no code implementations26 May 2022 Cuong Tran, Ferdinando Fioretto, Jung-eun Kim, Rakshit Naidu

Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy.

Network Pruning

Interpretability of Fine-grained Classification of Sadness and Depression

1 code implementation20 Mar 2022 Tiasa Singha Roy, Priyam Basu, Aman Priyanshu, Rakshit Naidu

While sadness is a human emotion that people experience at certain times throughout their lives, inflicting them with emotional disappointment and pain, depression is a longer term mental illness which impairs social, occupational, and other vital regions of functioning making it a much more serious issue and needs to be catered to at the earliest.

Classification Federated Learning

Efficient Hyperparameter Optimization for Differentially Private Deep Learning

1 code implementation9 Aug 2021 Aman Priyanshu, Rakshit Naidu, FatemehSadat Mireshghallah, Mohammad Malekzadeh

Tuning the hyperparameters in the differentially private stochastic gradient descent (DPSGD) is a fundamental challenge.

Hyperparameter Optimization

Benchmarking Differential Privacy and Federated Learning for BERT Models

1 code implementation26 Jun 2021 Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zumrut Muftuoglu, Sahib Singh, FatemehSadat Mireshghallah

Natural Language Processing (NLP) techniques can be applied to help with the diagnosis of medical conditions such as depression, using a collection of a person's utterances.

Benchmarking Federated Learning

When Differential Privacy Meets Interpretability: A Case Study

no code implementations24 Jun 2021 Rakshit Naidu, Aman Priyanshu, Aadith Kumar, Sasikanth Kotti, Haofan Wang, FatemehSadat Mireshghallah

Given the increase in the use of personal data for training Deep Neural Networks (DNNs) in tasks such as medical imaging and diagnosis, differentially private training of DNNs is surging in importance and there is a large body of work focusing on providing better privacy-utility trade-off.

DP-SGD vs PATE: Which Has Less Disparate Impact on Model Accuracy?

1 code implementation22 Jun 2021 Archit Uniyal, Rakshit Naidu, Sasikanth Kotti, Sahib Singh, Patrik Joslin Kenfack, FatemehSadat Mireshghallah, Andrew Trask

Recent advances in differentially private deep learning have demonstrated that application of differential privacy, specifically the DP-SGD algorithm, has a disparate impact on different sub-groups in the population, which leads to a significantly high drop-in model utility for sub-populations that are under-represented (minorities), compared to well-represented ones.

Fairness

FedPandemic: A Cross-Device Federated Learning Approach Towards Elementary Prognosis of Diseases During a Pandemic

no code implementations5 Apr 2021 Aman Priyanshu, Rakshit Naidu

The amount of data, manpower and capital required to understand, evaluate and agree on a group of symptoms for the elementary prognosis of pandemic diseases is enormous.

Federated Learning Retrieval

IS-CAM: Integrated Score-CAM for axiomatic-based explanations

1 code implementation6 Oct 2020 Rakshit Naidu, Ankita Ghosh, Yash Maurya, Shamanth R Nayak K, Soumya Snigdha Kundu

Convolutional Neural Networks have been known as black-box models as humans cannot interpret their inner functionalities.

SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization

2 code implementations25 Jun 2020 Haofan Wang, Rakshit Naidu, Joy Michael, Soumya Snigdha Kundu

Interpretation of the underlying mechanisms of Deep Convolutional Neural Networks has become an important aspect of research in the field of deep learning due to their applications in high-risk environments.

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