Rahul Yedida

According to our database, Rahul Yedida authored at least 24 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Strong convexity-guided hyper-parameter optimization for flatter losses.

Rahul Yedida, Snehanshu Saha

CoRR, 2024

SMOOTHIE: A Theory of Hyper-parameter Optimization for Software Analytics.

Rahul Yedida, Tim Menzies

CoRR, 2024

2023

An expert system for redesigning software for cloud applications.

Rahul Yedida, Rahul Krishna, Anup K. Kalia, Tim Menzies, Jin Xiao, Maja Vukovic

Expert Syst. Appl., June, 2023

How to Find Actionable Static Analysis Warnings: A Case Study With FindBugs.

Rahul Yedida, Hong Jin Kang, Huy Tu, Xueqi Yang, David Lo, Tim Menzies

IEEE Trans. Software Eng., April, 2023

(Re)Use of Research Results (Is Rampant).

Maria Teresa Baldassarre, Neil A. Ernst, Ben Hermann, Tim Menzies, Rahul Yedida

Commun. ACM, February, 2023

2022

On the Value of Oversampling for Deep Learning in Software Defect Prediction.

Rahul Yedida, Tim Menzies

IEEE Trans. Software Eng., 2022

Simpler Hyperparameter Optimization for Software Analytics: Why, How, When?

Amritanshu Agrawal, Xueqi Yang, Rishabh Agrawal, Rahul Yedida, Xipeng Shen, Tim Menzies

IEEE Trans. Software Eng., 2022

How to Find Actionable Static Analysis Warnings.

Rahul Yedida, Hong Jin Kang, Huy Tu, Xueqi Yang, David Lo, Tim Menzies

CoRR, 2022

How to Improve Deep Learning for Software Analytics (a case study with code smell detection).

Rahul Yedida, Tim Menzies

Proceedings of the 19th IEEE/ACM International Conference on Mining Software Repositories, 2022

2021

Learning to recognize actionable static code warnings (is intrinsically easy).

Xueqi Yang, Jianfeng Chen, Rahul Yedida, Zhe Yu, Tim Menzies

Empir. Softw. Eng., 2021

Partitioning Cloud-based Microservices (via Deep Learning).

Rahul Yedida, Rahul Krishna, Anup K. Kalia, Tim Menzies, Jin Xiao, Maja Vukovic

CoRR, 2021

Crowdsourcing the State of the Art(ifacts).

Maria Teresa Baldassarre, Neil A. Ernst, Ben Hermann, Tim Menzies, Rahul Yedida

CoRR, 2021

When SIMPLE is better than complex: A case study on deep learning for predicting Bugzilla issue close time.

Rahul Yedida, Xueqi Yang, Tim Menzies

CoRR, 2021

LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence.

Rahul Yedida, Snehanshu Saha, Tejas Prashanth

Appl. Intell., 2021

Documenting evidence of a reuse of 'on the number of linear regions of deep neural networks'.

Rahul Yedida, Tim Menzies

Proceedings of the ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021

Documenting evidence of a reuse of 'a systematic study of the class imbalance problem in convolutional neural networks'.

Rahul Yedida, Tim Menzies

Proceedings of the ESEC/FSE '21: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2021

Lessons learned from hyper-parameter tuning for microservice candidate identification.

Rahul Yedida, Rahul Krishna, Anup K. Kalia, Tim Menzies, Jin Xiao, Maja Vukovic

Proceedings of the 36th IEEE/ACM International Conference on Automated Software Engineering, 2021

2020

Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets.

Rahul Yedida, Saad Mohammad Abrar, Cleber C. Melo-Filho, Eugene N. Muratov, Rada Chirkova, Alexander Tropsha

CoRR, 2020

Improving Deep Learning for Defect Prediction (using the GHOST Hyperparameter Optimizer).

Rahul Yedida, Tim Menzies

CoRR, 2020

How to Recognize Actionable Static Code Warnings (Using Linear SVMs).

Xueqi Yang, Jianfeng Chen, Rahul Yedida, Zhe Yu, Tim Menzies

CoRR, 2020

Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets.

Shailesh Sridhar, Snehanshu Saha, Azhar Shaikh, Rahul Yedida, Sriparna Saha

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019

Evolution of Novel Activation Functions in Neural Network Training with Applications to Classification of Exoplanets.

Snehanshu Saha, Nithin Nagaraj, Archana Mathur, Rahul Yedida

CoRR, 2019

A novel adaptive learning rate scheduler for deep neural networks.

Rahul Yedida, Snehanshu Saha

CoRR, 2019

2018

Employee Attrition Prediction.

Rahul Yedida, Rahul Reddy, Rakshit Vahi, Rahul Jana, Abhilash G. V., Deepti Kulkarni

CoRR, 2018