1 code implementation • 30 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.
no code implementations • 29 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.
no code implementations • 24 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.
no code implementations • 26 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.
1 code implementation • 20 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.
no code implementations • EMNLP (ECONLP) 2021 • Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zumrut Muftuoglu
Privacy is important considering the financial Domain as such data is highly confidential and sensitive.
1 code implementation • 9 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.
no code implementations • 14 Jul 2021 • Rakshit Naidu, Harshita Diddee, Ajinkya Mulay, Aleti Vardhan, Krithika Ramesh, Ahmed Zamzam
In recent years, machine learning techniques utilizing large-scale datasets have achieved remarkable performance.
1 code implementation • 26 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.
no code implementations • 24 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.
1 code implementation • 22 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.
no code implementations • 5 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.
1 code implementation • 6 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.
2 code implementations • 25 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.