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Found 8 papers, 2 papers with code
Long-term Causal Effects Estimation via Latent Surrogates Representation Learning
Estimating long-term causal effects based on short-term surrogates is a significant but challenging problem in many real-world applications, e. g., marketing and medicine.
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation in Online Marketplace
We show the effectiveness of GCF by deriving the asymptotic property of the estimator and comparing it to popular uplift modeling methods on both synthetic and real-world datasets.
Relative velocity-based reward functions for crowd navigation of robots
In order to increase the appli-cable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc.
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods
Heterogeneous treatment effect (HTE) estimation with continuous treatment is essential in multiple disciplines, such as the online marketplace and pharmaceutical industry.
Meta Graph Attention on Heterogeneous Graph with Node-Edge Co-evolution
Graph neural networks have become an important tool for modeling structured data.
An Attention-based Graph Neural Network for Heterogeneous Structural Learning
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations.
Interpretable Text Classification Using CNN and Max-pooling
Deep neural networks have been widely used in text classification.
AHINE: Adaptive Heterogeneous Information Network Embedding
In this paper, we propose two novel algorithms, GHINE (General Heterogeneous Information Network Embedding) and AHINE (Adaptive Heterogeneous Information Network Embedding), to compute distributed representations for elements in heterogeneous networks.
