26 citations · 55 across the 9 of their papers we have counts for
11 papers
A Generalisation Signal Need Not Be a Model-Selection Signal
Aditya Nagarsekar, M P Ashish Bhat, Aadi Nesarkar +5
Model selection in computational biology often relies on validation data drawn from the training regime, even when deployment lies outside it. When validation no longer preserves w…
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications
Shlok Mehendale, Aditya Challa, Rahul Yedida +3
Which principle underpins the design of an effective anomaly detection loss function? The answer lies in the concept of Radon-Nikodým theorem, a fundamental concept in measure theo…
Strong convexity-guided hyper-parameter optimization for flatter losses
Rahul Yedida, Snehanshu Saha
We propose a novel white-box approach to hyper-parameter optimization. Motivated by recent work establishing a relationship between flat minima and generalization, we first establi…
Lessons learned from hyper-parameter tuning for microservice candidate identification
Rahul Yedida, Rahul Krishna, Anup Kalia +3
When optimizing software for the cloud, monolithic applications need to be partitioned into many smaller *microservices*. While many tools have been proposed for this task, we warn…
Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets
Rahul Yedida, Saad Mohammad Abrar, Cleber Melo-Filho +3
Objective: We aim to learn potential novel cures for diseases from unstructured text sources. More specifically, we seek to extract drug-disease pairs of potential cures to disease…
On the Value of Oversampling for Deep Learning in Software Defect Prediction
Rahul Yedida, Tim Menzies
One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature en…