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A Learning-based Stochastic Driving Model for Autonomous Vehicle Testing
In the simulation-based testing and evaluation of autonomous vehicles (A...
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Low-complexity and High-performance Receive Beamforming for Secure Directional Modulation Networks against an Eavesdropping-enabled Full-duplex Attacker
In this paper, we present a novel scenario for directional modulation (D...
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A Transfer Learning Based Active Learning Framework for Brain Tumor Classification
Brain tumor is one of the leading causes of cancer-related death globall...
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Dependency-based Anomaly Detection: Framework, Methods and Benchmark
Anomaly detection is an important research problem because anomalies oft...
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Learning causal representations for robust domain adaptation
Domain adaptation solves the learning problem in a target domain by leve...
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Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs
Moire artifacts are common in digital photography, resulting from the in...
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Assessing the Fairness of Classifiers with Collider Bias
The increasing maturity of machine learning technologies and their appli...
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Sufficient Dimension Reduction for Average Causal Effect Estimation
Having a large number of covariates can have a negative impact on the qu...
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Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning
This is the rejoinder to the discussion by Kennedy, Balakrishnan and Was...
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Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint
Product search is the most common way for people to satisfy their shoppi...
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Local search for efficient causal effect estimation
Causal effect estimation from observational data is an important but cha...
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Wavelet-Based Dual-Branch Network for Image Demoireing
When smartphone cameras are used to take photos of digital screens, usua...
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A unified survey on treatment effect heterogeneity modeling and uplift modeling
A central question in many fields of scientific research is to determine...
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Computational methods for cancer driver discovery: A survey
Motivation: Uncovering the genomic causes of cancer, known as cancer dri...
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Clinically Relevant Mediation Analysis using Controlled Indirect Effect
Mediation analysis allows one to use observational data to estimate the ...
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A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks
Data augmentation refers to a group of techniques whose goal is to battl...
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Hierarchical Feature Embedding for Attribute Recognition
Attribute recognition is a crucial but challenging task due to viewpoint...
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A general framework for causal classification
In many applications, there is a need to predict the effect of an interv...
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Towards precise causal effect estimation from data with hidden variables
Causal effect estimation from observational data is a crucial but challe...
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Treatment effect estimation with disentangled latent factors
A pressing concern faced by cancer patients is their prognosis under dif...
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Causal query in observational data with hidden variables
This paper discusses the problem of causal query in observational data w...
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Receive Antenna Selection for Secure Pre-coding Aided Spatial Modulation
In this paper, we make an investigation of receive antenna selection (RA...
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Causality-based Feature Selection: Methods and Evaluations
Feature selection is a crucial preprocessing step in data analytics and ...
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Cooperative driving strategy based on naturalitic driving data and non-cooperative MPC
A cooperative driving strategy is proposed, in which the dynamic driving...
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Efficient estimation of optimal regimes under a no direct effect assumption
We derive new estimators of an optimal joint testing and treatment regim...
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Linking Graph Entities with Multiplicity and Provenance
Entity linking is a fundamental database problem with applicationsin dat...
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A Deep-learning-based Joint Inference for Secure Spatial Modulation Receiver
As a green and secure wireless transmission way, secure spatial modulati...
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Identify treatment effect patterns for personalised decisions
In personalised decision making, evidence is required to determine suita...
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Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
Network representation learning, as an approach to learn low dimensional...
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On assumption-free tests and confidence intervals for causal effects estimated by machine learning
For many causal effect parameters ψ of interest doubly robust machine le...
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Distributionally Robust Multi-instance Learning with Stable Instances
Multi-instance learning (MIL) deals with tasks where data consist of set...
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An exploration of algorithmic discrimination in data and classification
Algorithmic discrimination is an important aspect when data is used for ...
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FairMod - Making Predictive Models Discrimination Aware
Predictive models such as decision trees and neural networks may produce...
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Discovering Context Specific Causal Relationships
With the increasing need of personalised decision making, such as person...
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A Unified View of Causal and Non-causal Feature Selection
In this paper, we unify causal and non-causal feature feature selection ...
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Discovering Markov Blanket from Multiple interventional Datasets
In this paper, we study the problem of discovering the Markov blanket (M...
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A Review on Algorithms for Constraint-based Causal Discovery
Causal discovery studies the problem of mining causal relationships betw...
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ParallelPC: an R package for efficient constraint based causal exploration
Discovering causal relationships from data is the ultimate goal of many ...
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Mining Combined Causes in Large Data Sets
In recent years, many methods have been developed for detecting causal r...
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From Observational Studies to Causal Rule Mining
Randomised controlled trials (RCTs) are the most effective approach to c...
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Causal Decision Trees
Uncovering causal relationships in data is a major objective of data ana...
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