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Estimating Treatment Effects Under Heterogeneous Interference
Treatment effect estimation can assist in effective decision-making in e-commerce, medicine, and education. One popular application of this...
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Meta-learning for heterogeneous treatment effect estimation with closed-form solvers
This article proposes a meta-learning method for estimating the conditional average treatment effect (CATE) from a few observational data. The...
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Meta-learning for Estimating Multiple Treatment Effects with Imbalance
Ascertaining counterfactual questions, for instance, “Would individuals with diabetes have exhibited better if they had opted for a different...
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Bayesian tree-based heterogeneous mediation analysis with a time-to-event outcome
Mediation analysis aims at quantifying and explaining the underlying causal mechanism between an exposure and an outcome of interest. In the context...
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Combining the Clinical and Operational Perspectives in Heterogeneous Treatment Effect Inference in Healthcare Processes
Recent developments in causal machine learning open perspectives for new approaches that support decision-making in healthcare processes using causal...
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Towards Heterogeneous Federated Learning
Federated Learning (FL), a novel distributed machine learning framework, made it possible to model collaboratively without risking participants’...
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Predicting Individual Treatment Effects: Challenges and Opportunities for Machine Learning and Artificial Intelligence
Personalized medicine seeks to identify the right treatment for the right patient at the right time. Predicting the treatment effect for an...
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Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect
Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects. It is commonly used in industry and elsewhere for...
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Cardiac murmur grading and risk analysis of cardiac diseases based on adaptable heterogeneous-modality multi-task learning
Cardiovascular disease (CVDs) has become one of the leading causes of death, posing a significant threat to human life. The development of reliable...
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Safe Exploration in Dose Finding Clinical Trials with Heterogeneous Participants
In drug development, early phase dose-finding clinical trials are carried out to identify an optimal dose to administer to patients in larger...
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Five-dimensional evaluation system and perceptron intelligent computing performance measurement methods based on medical heterogeneous equipment health data
This article mainly focuses on the preprocessing method of medical heterogeneous equipment health data sources and the performance measurement of...
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Syndrome-Aware Herb Recommendation with Heterogeneous Graph Neural Network
The herb recommender system usually induces the implicit syndrome representations based on TCM prescriptions to generate related herbs as a treatment...
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CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation
New lesion segmentation is essential to estimate the disease progression and therapeutic effects during multiple sclerosis (MS) clinical treatments....
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Efficient Large Scale DLRM Implementation on Heterogeneous Memory Systems
We propose a new data structure called CachedEmbeddings for training large scale deep learning recommendation models (DLRM) efficiently on...
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Heterogeneous sets in dimensionality reduction and ensemble learning
We present a general framework for dealing with set heterogeneity in data and learning problems, which is able to exploit low complexity components....
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Identifying Drug - Disease Interactions Through Link Prediction in Heterogeneous Graphs
Unlike traditional development of new drugs that rely on labor- and time-intensive research and clinical trials, computational approaches, deep...
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Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information
Estimating the average treatment effect (ATE) from observational data is challenging due to selection bias. Existing works mainly tackle this...
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Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification
Real-world data of multi-class classification tasks often show complex data characteristics that lead to a reduced classification performance. Major...
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Agent-based crowd simulation: an in-depth survey of determining factors for heterogeneous behavior
In recent years, the field of crowd simulation has experienced significant advancements, attributed in part to the improvement of hardware...
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Collaborative Robot-Oriented Joint Real-Time Control Based on Heterogeneous Embedded Platform
A real-time joint controller oriented on collaborative robots (co-robots) using an embedded multi-core heterogeneous development board is proposed in...