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ORCIDLokesh Nagalapatti
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2020 – today
- 2024
[c9]Lokesh Nagalapatti, Akshay Iyer, Abir De, Sunita Sarawagi:
Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing. AAAI 2024: 14397-14404- [c8]Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi:
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect. ICML 2024 - [c7]Durga Sivasubramanian, Lokesh Nagalapatti, Rishabh K. Iyer, Ganesh Ramakrishnan:
Gradient Coreset for Federated Learning. WACV 2024: 2636-2645 - [i6]Durga Sivasubramanian, Lokesh Nagalapatti, Rishabh K. Iyer, Ganesh Ramakrishnan:
Gradient Coreset for Federated Learning. CoRR abs/2401.06989 (2024) - [i5]Lokesh Nagalapatti, Akshay Iyer, Abir De, Sunita Sarawagi:
Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing. CoRR abs/2401.15447 (2024) - [i4]Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh, Sunita Sarawagi:
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect. CoRR abs/2406.03864 (2024) - 2023
- [j1]Hima Patel
, Shanmukha C. Guttula, Nitin Gupta, Sandeep Hans, Ruhi Sharma Mittal, Lokesh Nagalapatti:
A Data-centric AI Framework for Automating Exploratory Data Analysis and Data Quality Tasks. ACM J. Data Inf. Qual. 15(4): 44:1-44:26 (2023) - 2022
- [c6]Lokesh Nagalapatti, Ruhi Sharma Mittal, Ramasuri Narayanam:
Is Your Data Relevant?: Dynamic Selection of Relevant Data for Federated Learning. AAAI 2022: 7859-7867 - [c5]Lokesh Nagalapatti, Guntakanti Sai Koushik, Abir De, Sunita Sarawagi:
Learning Recourse on Instance Environment to Enhance Prediction Accuracy. NeurIPS 2022 - 2021
- [c4]Lokesh Nagalapatti, Ramasuri Narayanam:
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. AAAI 2021: 9046-9054 - [c3]Eitan Farchi, Ramasuri Narayanam, Lokesh Nagalapatti:
Ranking Data Slices for ML Model Validation: A Shapley Value Approach. ICDE 2021: 1937-1942 - [c2]Lingraj S. Vannur, Balaji Ganesan, Lokesh Nagalapatti, Hima Patel, M. N. Tippeswamy:
Data Augmentation for Fairness in Personal Knowledge Base Population. PAKDD (Workshops) 2021: 143-152 - [i3]Nitin Gupta, Hima Patel, Shazia Afzal, Naveen Panwar, Ruhi Sharma Mittal, Shanmukha C. Guttula, Abhinav Jain, Lokesh Nagalapatti, Sameep Mehta, Sandeep Hans, Pranay Lohia, Aniya Aggarwal, Diptikalyan Saha:
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets. CoRR abs/2108.05935 (2021) - [i2]Lokesh Nagalapatti, Ramasuri Narayanam:
Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. CoRR abs/2110.12257 (2021) - 2020
- [c1]Abhinav Jain, Hima Patel, Lokesh Nagalapatti, Nitin Gupta, Sameep Mehta, Shanmukha C. Guttula, Shashank Mujumdar, Shazia Afzal, Ruhi Sharma Mittal, Vitobha Munigala:
Overview and Importance of Data Quality for Machine Learning Tasks. KDD 2020: 3561-3562 - [i1]Lingraj S. Vannur, Lokesh Nagalapatti, Balaji Ganesan, Hima Patel:
Data Augmentation for Personal Knowledge Graph Population. CoRR abs/2002.10943 (2020)
Coauthor Index
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