b'Lin Liu'

A splitting Hamiltonian Monte Carlo method for efficient sampling

We propose a splitting Hamiltonian Monte Carlo (SHMC) algorithm, which c...
05/30/2021 ∙ by Lei Li, et al. ∙ 0 ∙ share

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Any Part of Bayesian Network Structure Learning

We study an interesting and challenging problem, learning any part of a ...
03/23/2021 ∙ by Zhaolong Ling, et al. ∙ 14 ∙ share

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Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning

Causal Learner is a toolbox for learning causal structure and Markov bla...
03/11/2021 ∙ by Zhaolong Ling, et al. ∙ 0 ∙ share

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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...
02/04/2021 ∙ by Lin Liu, et al. ∙ 0 ∙ share

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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...
12/06/2020 ∙ by Yin Teng, et al. ∙ 0 ∙ share

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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...
11/16/2020 ∙ by Ruqian Hao, et al. ∙ 0 ∙ share

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Dependency-based Anomaly Detection: Framework, Methods and Benchmark

Anomaly detection is an important research problem because anomalies oft...
11/13/2020 ∙ by Sha Lu, et al. ∙ 0 ∙ share

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Learning causal representations for robust domain adaptation

Domain adaptation solves the learning problem in a target domain by leve...
11/12/2020 ∙ by Shuai Yang, et al. ∙ 5 ∙ share

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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...
11/03/2020 ∙ by Lin Liu, et al. ∙ 2 ∙ share

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Assessing the Fairness of Classifiers with Collider Bias

The increasing maturity of machine learning technologies and their appli...
10/08/2020 ∙ by Zhenlong Xu, et al. ∙ 3 ∙ share

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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...
09/14/2020 ∙ by Debo Cheng, et al. ∙ 9 ∙ share

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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...
08/07/2020 ∙ by Lin Liu, et al. ∙ 9 ∙ share

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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...
07/24/2020 ∙ by Zhuojian Xiao, et al. ∙ 6 ∙ share

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Local search for efficient causal effect estimation

Causal effect estimation from observational data is an important but cha...
07/20/2020 ∙ by Debo Cheng, et al. ∙ 0 ∙ share

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Wavelet-Based Dual-Branch Network for Image Demoireing

When smartphone cameras are used to take photos of digital screens, usua...
07/14/2020 ∙ by Lin Liu, et al. ∙ 6 ∙ share

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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...
07/14/2020 ∙ by Weijia Zhang, et al. ∙ 0 ∙ share

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Computational methods for cancer driver discovery: A survey

Motivation: Uncovering the genomic causes of cancer, known as cancer dri...
07/02/2020 ∙ by Vu Viet Hoang Pham, et al. ∙ 0 ∙ share

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Clinically Relevant Mediation Analysis using Controlled Indirect Effect

Mediation analysis allows one to use observational data to estimate the ...
06/21/2020 ∙ by Haoqi Sun, et al. ∙ 0 ∙ share

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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...
06/01/2020 ∙ by Ruqian Hao, et al. ∙ 2 ∙ share

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Hierarchical Feature Embedding for Attribute Recognition

Attribute recognition is a crucial but challenging task due to viewpoint...
05/23/2020 ∙ by Jie Yang, et al. ∙ 18 ∙ share

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A general framework for causal classification

In many applications, there is a need to predict the effect of an interv...
03/25/2020 ∙ by Jiuyong Li, et al. ∙ 15 ∙ share

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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...
02/24/2020 ∙ by Debo Cheng, et al. ∙ 18 ∙ share

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Treatment effect estimation with disentangled latent factors

A pressing concern faced by cancer patients is their prognosis under dif...
01/29/2020 ∙ by Weijia Zhang, et al. ∙ 16 ∙ share

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Causal query in observational data with hidden variables

This paper discusses the problem of causal query in observational data w...
01/28/2020 ∙ by Debo Cheng, et al. ∙ 7 ∙ share

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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...
01/23/2020 ∙ by Lin Liu, et al. ∙ 0 ∙ share

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Causality-based Feature Selection: Methods and Evaluations

Feature selection is a crucial preprocessing step in data analytics and ...
11/17/2019 ∙ by Kui Yu, et al. ∙ 39 ∙ share

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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...
10/10/2019 ∙ by Rui Liu, et al. ∙ 0 ∙ share

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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...
08/27/2019 ∙ by Lin Liu, et al. ∙ 0 ∙ share

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Linking Graph Entities with Multiplicity and Provenance

Entity linking is a fundamental database problem with applicationsin dat...
08/13/2019 ∙ by Jixue Liu, et al. ∙ 0 ∙ share

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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...
07/04/2019 ∙ by Feng Shu, et al. ∙ 0 ∙ share

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Identify treatment effect patterns for personalised decisions

In personalised decision making, evidence is required to determine suita...
06/14/2019 ∙ by Jiuyong Li, et al. ∙ 0 ∙ share

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Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

Network representation learning, as an approach to learn low dimensional...
06/09/2019 ∙ by Hao Peng, et al. ∙ 7 ∙ share

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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...
04/08/2019 ∙ by Lin Liu, et al. ∙ 0 ∙ share

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Distributionally Robust Multi-instance Learning with Stable Instances

Multi-instance learning (MIL) deals with tasks where data consist of set...
02/13/2019 ∙ by Weijia Zhang, et al. ∙ 0 ∙ share

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An exploration of algorithmic discrimination in data and classification

Algorithmic discrimination is an important aspect when data is used for ...
11/06/2018 ∙ by Jixue Liu, et al. ∙ 0 ∙ share

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FairMod - Making Predictive Models Discrimination Aware

Predictive models such as decision trees and neural networks may produce...
11/05/2018 ∙ by Jixue Liu, et al. ∙ 0 ∙ share

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Discovering Context Specific Causal Relationships

With the increasing need of personalised decision making, such as person...
08/20/2018 ∙ by Saisai Ma, et al. ∙ 0 ∙ share

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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 ...
02/16/2018 ∙ by Kui Yu, et al. ∙ 0 ∙ share

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Discovering Markov Blanket from Multiple interventional Datasets

In this paper, we study the problem of discovering the Markov blanket (M...
01/25/2018 ∙ by Kui Yu, et al. ∙ 0 ∙ share

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A Review on Algorithms for Constraint-based Causal Discovery

Causal discovery studies the problem of mining causal relationships betw...
11/12/2016 ∙ by Kui Yu, et al. ∙ 0 ∙ share

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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 ...
10/11/2015 ∙ by Thuc Duy Le, et al. ∙ 0 ∙ share

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Mining Combined Causes in Large Data Sets

In recent years, many methods have been developed for detecting causal r...
08/28/2015 ∙ by Saisai Ma, et al. ∙ 0 ∙ share

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From Observational Studies to Causal Rule Mining

Randomised controlled trials (RCTs) are the most effective approach to c...
08/16/2015 ∙ by Jiuyong Li, et al. ∙ 0 ∙ share

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Causal Decision Trees

Uncovering causal relationships in data is a major objective of data ana...
08/16/2015 ∙ by Jiuyong Li, et al. ∙ 0 ∙ share

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Lin Liu

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