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cs.LG2025
Leveraging a Simulator for Learning Causal Representations from Post-Treatment Covariates for CATE
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh +1
Treatment effect estimation involves assessing the impact of different treatments on individual outcomes. Current methods estimate Conditional Average Treatment Effect (CATE) using…
cs.LG2024
PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect
Lokesh Nagalapatti, Pranava Singhal, Avishek Ghosh +1
Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estima…
cs.LG2023
Greedy Shapley Client Selection for Communication-Efficient Federated Learning
Pranava Singhal, Shashi Raj Pandey, Petar Popovski
The standard client selection algorithms for Federated Learning (FL) are often unbiased and involve uniform random sampling of clients. This has been proven sub-optimal for fast co…