2 citations · 3 across the 2 of their papers we have counts for
7 papers · 1 filter
Individualised Treatment Effects Estimation with Composite Treatments and Composite Outcomes
Vinod Kumar Chauhan, Lei Clifton, Gaurav Nigam +1
Estimating individualised treatment effect (ITE) -- that is the causal effect of a set of variables (also called exposures, treatments, actions, policies, or interventions), referr…
Sample Selection Bias in Machine Learning for Healthcare
Vinod Kumar Chauhan, Lei Clifton, Achille Salaün +5
While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predi…
GTAGCN: Generalized Topology Adaptive Graph Convolutional Networks
Sukhdeep Singh, Anuj Sharma, Vinod Kumar Chauhan
Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data. The literature on GNN highlights the potential of this evolving…
A Brief Review of Hypernetworks in Deep Learning
Vinod Kumar Chauhan, Jiandong Zhou, Ping Lu +2
Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learn…
Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation
Vinod Kumar Chauhan, Jiandong Zhou, Ghadeer Ghosheh +2
Estimation of individualized treatment effects (ITE) from observational studies is a fundamental problem in causal inference and holds significant importance across domains, includ…
Stochastic Trust Region Inexact Newton Method for Large-scale Machine Learning
Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya
Nowadays stochastic approximation methods are one of the major research direction to deal with the large-scale machine learning problems. From stochastic first order methods, now t…