135 citations · 164 across the 11 of their papers we have counts for
6 papers · 1 filter
SoK: Data Minimization in Machine Learning
Robin Staab, Nikola Jovanović, Kimberly Mai +4
Data minimization (DM) describes the principle of collecting only the data strictly necessary for a given task. It is a foundational principle across major data protection regulati…
The Data Minimization Principle in Machine Learning
Prakhar Ganesh, Cuong Tran, Reza Shokri +1
The principle of data minimization aims to reduce the amount of data collected, processed or retained to minimize the potential for misuse, unauthorized access, or data breaches. R…
An Empirical Investigation into Benchmarking Model Multiplicity for Trustworthy Machine Learning: A Case Study on Image Classification
Prakhar Ganesh
Deep learning models have proven to be highly successful. Yet, their over-parameterization gives rise to model multiplicity, a phenomenon in which multiple models achieve similar p…
On The Impact of Machine Learning Randomness on Group Fairness
Prakhar Ganesh, Hongyan Chang, Martin Strobel +1
Statistical measures for group fairness in machine learning reflect the gap in performance of algorithms across different groups. These measures, however, exhibit a high variance b…
Compressing Large-Scale Transformer-Based Models: A Case Study on BERT
Prakhar Ganesh, Yao Chen, Xin Lou +6
Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of pa…
VLSTM: Very Long Short-Term Memory Networks for High-Frequency Trading
Prakhar Ganesh, Puneet Rakheja
Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with it. The advent of electronic trading has allowed complex machine learning solutions t…