112 citations · 211 across the 15 of their papers we have counts for
15 papers
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…
Network science approach for identifying disruptive elements of an airline
Vinod Kumar Chauhan, Anna Ledwoch, Alexandra Brintrup +4
Currently, flight delays are common and they propagate from an originating flight to connecting flights, leading to large disruptions in the overall schedule. These disruptions cau…
Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing
Vinod Kumar Chauhan, Stephen Mak, Ajith Kumar Parlikad +3
Supplier selection and order allocation (SSOA) are key strategic decisions in supply chain management which greatly impact the performance of the supply chain. Although, the SSOA p…
Exploitation of material consolidation trade-offs in multi-tier complex supply networks
Vinod Kumar Chauhan, Muhannad Alomari, James Arney +2
While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an unde…
Adversarial De-confounding in Individualised Treatment Effects Estimation
Vinod Kumar Chauhan, Soheila Molaei, Marzia Hoque Tania +3
Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the…
Trolley Optimisation for Loading Printed Circuit Board Components
Vinod Kumar Chauhan, Mark Bass, Ajith Kumar Parlikad +1
A trolley is a container for loading printed circuit board (PCB) components, and a trolley optimisation problem (TOP) is an assignment of PCB components to trolleys for use in the…
HCR-Net: A deep learning based script independent handwritten character recognition network
Vinod Kumar Chauhan, Sukhdeep Singh, Anuj Sharma
Handwritten character recognition (HCR) remains a challenging pattern recognition problem despite decades of research, and lacks research on script independent recognition techniqu…
LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms
Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya
LIBS2ML is a library based on scalable second order learning algorithms for solving large-scale problems, i.e., big data problems in machine learning. LIBS2ML has been developed us…
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…
SAAGs: Biased Stochastic Variance Reduction Methods for Large-scale Learning
Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya
Stochastic approximation is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, cause…
Faster Learning by Reduction of Data Access Time
Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya
Nowadays, the major challenge in machine learning is the Big Data challenge. The big data problems due to large number of data points or large number of features in each data point…