3 papers
cs.LG2025
Representation Learning Preserving Ignorability and Covariate Matching for Treatment Effects
Praharsh Nanavati, Ranjitha Prasad, Karthikeyan Shanmugam
Estimating treatment effects from observational data is challenging due to two main reasons: (a) hidden confounding, and (b) covariate mismatch (control and treatment groups not ha…
cs.LG2025
Noise Resilient Over-The-Air Federated Learning In Heterogeneous Wireless Networks
Zubair Shaban, Nazreen Shah, Ranjitha Prasad
In 6G wireless networks, Artificial Intelligence (AI)-driven applications demand the adoption of Federated Learning (FL) to enable efficient and privacy-preserving model training a…
cs.LG2024
On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients
Satish Kumar Keshri, Nazreen Shah, Ranjitha Prasad
The holy grail of machine learning is to enable Continual Federated Learning (CFL) to enhance the efficiency, privacy, and scalability of AI systems while learning from streaming d…