Rahul Yedida

Baldassarre, M. T., Ernst, N., Hermann, B., Menzies, T., & Yedida, R. (2023). (Re)Use of Research Results (Is Rampant). COMMUNICATIONS OF THE ACM, 66(2), 75–81. https://doi.org/10.1145/3554976

Yedida, R., Krishna, R., Kalia, A., Menzies, T., Xiao, J., & Vukovic, M. (2023). An expert system for redesigning software for cloud applications. EXPERT SYSTEMS WITH APPLICATIONS, 219. https://doi.org/10.1016/j.eswa.2023.119673

Yedida, R., Kang, H. J., Tu, H., Yang, X., Lo, D., & Menzies, T. (2023). How to Find Actionable Static Analysis Warnings: A Case Study With FindBugs. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(4), 2856–2872. https://doi.org/10.1109/TSE.2023.3234206

Yedida, R., Kang, H. J., Tu, H., Yang, X., Lo, D., & Menzies, T. (2022). How to Find Actionable Static Analysis Warnings: A Case Study with FindBugs. ArXiv. https://doi.org/10.48550/arXiv.2205.10504

Yedida, R., & Menzies, T. (2022). How to Improve Deep Learning for Software Analytics (a case study with code smell detection). 2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 156–166. https://doi.org/10.1145/3524842.3528458

Yedida, R., & Menzies, T. (2022). How to Improve Deep Learning for Software Analytics (a case study with code smell detection). ArXiv. https://doi.org/10.48550/arxiv.2202.01322

Yedida, R., Beasley, J.-M., Korn, D., Abrar, S. M., Melo-Filho, C. C., Muratov, E., … Tropsha, A. (2022). TEXT MINING OF THE PEOPLE'S PHARMACY RADIO SHOW TRANSCRIPTS CAN IDENTIFY NOVEL DRUG REPURPOSING HYPOTHESES. MedRxiv. https://doi.org/10.1101/2022.02.02.22270107

Yedida, R., Krishna, R., Kalia, A., Menzies, T., Xiao, J., & Vukovic, M. (2021). An Expert System for Redesigning Software for Cloud Applications. ArXiv. https://doi.org/10.48550/arXiv.2109.14569

Yedida, R., & Saha, S. (2021, July 21). Beginning with machine learning: a comprehensive primer. EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, Vol. 7. https://doi.org/10.1140/epjs/s11734-021-00209-7

Baldassarre, M. T., Ernst, N., Hermann, B., Menzies, T., & Yedida, R. (2021). Crowdsourcing the State of the Art(ifacts (ArXiv Preprint No. 2108.06821).

Baldassarre, M. T., Ernst, N., Hermann, B., Menzies, T., & Yedida, R. (2021). Crowdsourcing the state of the art(ifacts). ArXiv. https://doi.org/10.48550/arxiv.2108.06821

Yedida, R., & Menzies, T. (2021). Documenting Evidence of a Reuse of 'A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1595–1595. https://doi.org/10.1145/3468264.3477212

Yedida, R., & Menzies, T. (2021). Documenting Evidence of a Reuse of 'On the Number of Linear Regions of Deep Neural Networks'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1596–1596. https://doi.org/10.1145/3468264.3477213

Yang, X., Chen, J., Yedida, R., Yu, Z., & Menzies, T. (2021). Learning to recognize actionable static code warnings (is intrinsically easy). EMPIRICAL SOFTWARE ENGINEERING, 26(3). https://doi.org/10.1007/s10664-021-09948-6

Yedida, R., Krishna, R., Kalia, A., Menzies, T., Xiao, J., & Vukovic, M. (2021). Lessons learned from hyper-parameter tuning for microservice candidate identi cation. Proceedings of the thirty-sixth IEEE/ACM International Conference on Automated Software Engineering (ASE). Presented at the 36th IEEE/ACM International Conference on Automated Software Engineering, (Virtual).

Yedida, R., Krishna, R., Kalia, A., Menzies, T., Xiao, J., & Vukovic, M. (2021). Lessons learned from hyper-parameter tuning for microservice candidate identification. ArXiv. https://doi.org/10.48550/arxiv.2106.06652

Yedida, R., Krishna, R., Kalia, A., Menzies, T., Xiao, J., & Vukovic, M. (2021). Lessons learned from hyper-parameter tuning for microservice candidate identification. Proceedings - 2021 36th IEEE/ACM International Conference on Automated Software Engineering, ASE 2021, 1141–1145. https://doi.org/10.1109/ASE51524.2021.9678704

Yedida, R., Saha, S., & Prashanth, T. (2021). LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence. APPLIED INTELLIGENCE, 51(3), 1460–1478. https://doi.org/10.1007/s10489-020-01892-0

Yedida, R., Yang, X., & Menzies, T. (2021). Old but Gold: Reconsidering the value of feedforward learners for software analytics. ArXiv. https://doi.org/10.48550/arxiv.2101.06319

Yedida, R., & Menzies, T. (2021). On the Value of Oversampling for Deep Learning in Software Defect Prediction. IEEE Transactions on Software Engineering, 48(8), 1–1. https://doi.org/10.1109/TSE.2021.3079841

Agrawal, A., Yang, X., Agrawal, R., Yedida, R., Shen, X., & Menzies, T. (2021). Simpler Hyperparameter Optimization for Software Analytics: Why, How, When. IEEE Transactions on Software Engineering, 48(8), 1–1. https://doi.org/10.1109/TSE.2021.3073242

Yedida, R., Yang, X., & Menzies, T. (2021). When SIMPLE is better than complex: A case study on deep learning for predicting Bugzilla issue close time (ArXiv Preprint No. 2101.06319).

Saha, S., Nagaraj, N., Mathur, A., Yedida, R., & Sneha, H. R. (2020). [Review of Evolution of novel activation functions in neural network training for astronomy data: habitability classification of exoplanets]. EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS, 229(16), 2629–2738. https://doi.org/10.1140/epjst/e2020-000098-9

Yang, X., Chen, J., Yedida, R., Yu, Z., & Menzies, T. (2020). How to Recognize Actionable Static Code Warnings (Using Linear SVMs). ArXiv. https://doi.org/10.48550/arxiv.2006.00444

Yedida, R., & Menzies, T. (2020). On the value of oversampling for deep learning in software defect prediction. ArXiv. https://doi.org/10.48550/arxiv.2008.03835

Khaidem, L., Yedida, R., & Theophilus, A. J. (2020). Optimizing Inter-nationality of Journals: A Classical Gradient Approach Revisited via Swarm Intelligence. In Communications in Computer and Information Science: Vol. 1290. Modeling, Machine Learning and Astronomy (Vol. 1290, pp. 3–14). https://doi.org/10.1007/978-981-33-6463-9_1

Sridhar, S., Saha, S., Shaikh, A., Yedida, R., & Saha, S. (2020). Parsimonious Computing: A Minority Training Regime for Effective Prediction in Large Microarray Expression Data Sets. 2020 International Joint Conference on Neural Networks (IJCNN), 1–8. https://doi.org/10.1109/ijcnn48605.2020.9207083

Parsimonious computing: A minority training regime for effective prediction in large microarray expression data sets. (2020). ArXiv. https://doi.org/10.48550/arxiv.2005.08442

Yedida, R., Abrar, S. M., Melo-Filho, C., Muratov, E., Chirkova, R., & Tropsha, A. (2020). Text Mining to Identify and Extract Novel Disease Treatments From Unstructured Datasets (ArXiv Preprint No. 2011.07959).

Yedida, R., Abrar, S. M., Melo-Filho, C., Muratov, E., Chirkova, R., & Tropsha, A. (2020). Text mining to identify and extract novel disease treatments from unstructured datasets. ArXiv. https://doi.org/10.48550/arxiv.2011.07959

Saha, S., Nagaraj, N., Mathur, A., & Yedida, R. (2019). Evolution of novel activation functions in neural network training with applications to classification of exoplanets. ArXiv. https://doi.org/10.48550/arxiv.1906.01975

Yedida, R., & Saha, S. (2019). LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence. ArXiv. https://doi.org/10.48550/arxiv.1902.07399

Agrawal, A., Yang, X., Agrawal, R., Yedida, R., Shen, X., & Menzies, T. (2019). Simpler hyperparameter optimization for software analytics: Why, how, when? ArXiv. https://doi.org/10.48550/arxiv.1912.04061

Yedida, R. (2018). An Introduction to Data Analysis. Presented at the PES University, Bangalore, India.

Yedida, R., Reddy, R., Vahi, R., J., R., Abhilash, & Kulkarni, D. (2018). Employee attrition prediction. ArXiv. Retrieved from http://www.scopus.com/inward/record.url?eid=2-s2.0-85095619264&partnerID=MN8TOARS

Yedida, R. (2018). How to design a Flappy Bird game. Presented at the PES University, Bangalore, India.

Yedida, R. (2018). Machine Learning. Presented at the PES University, Bangalore, India.

Yedida, R. (2017). Complexity Classes and NP-Completeness. Presented at the PES University, Bangalore, India.