354 citations · 956 across the 24 of their papers we have counts for
28 papers
Convergence and clustering analysis for Mean Shift with radially symmetric, positive definite kernels
Susovan Pal
The mean shift (MS) is a non-parametric, density-based, iterative algorithm with prominent usage in clustering and image segmentation. A rigorous proof for the convergence of its m…
Predicting Survival of Hemodialysis Patients using Federated Learning
Abhiram Raju, Praneeth Vepakomma
Hemodialysis patients who are on donor lists for kidney transplant may get misidentified, delaying their wait time. Thus, predicting their survival time is crucial for optimizing w…
Parallel Quasi-concave set optimization: A new frontier that scales without needing submodularity
Praneeth Vepakomma, Yulia Kempner, Ramesh Raskar
Classes of set functions along with a choice of ground set are a bedrock to determine and develop corresponding variants of greedy algorithms to obtain efficient solutions for comb…
AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning
Yusuke Koda, Jihong Park, Mehdi Bennis +2
Wireless channels can be inherently privacy-preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing th…
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks
Abhishek Singh, Ayush Chopra, Vivek Sharma +4
Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assu…
NoPeek: Information leakage reduction to share activations in distributed deep learning
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta +1
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensit…