b'Lin Liu'

research

∙ 06/23/2022

Explanatory causal effects for model agnostic explanations

This paper studies the problem of estimating the contributions of featur...

Jiuyong Li, et al. ∙

research

∙ 06/04/2022

Discovering Ancestral Instrumental Variables for Causal Inference from Observational Data

Instrumental variable (IV) is a powerful approach to inferring the causa...

Debo Cheng, et al. ∙

research

∙ 05/25/2022

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

This paper reviews the challenge on constrained high dynamic range (HDR)...

Eduardo Pérez-Pellitero, et al. ∙

research

∙ 04/08/2022

Global Update Guided Federated Learning

Federated learning protects data privacy and security by exchanging mode...

Qilong Wu, et al. ∙

research

∙ 03/11/2022

TAPE: Task-Agnostic Prior Embedding for Image Restoration

Learning an generalized prior for natural image restoration is an import...

Lin Liu, et al. ∙

research

∙ 03/09/2022

Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It

Many fundamental problems affecting the care of critically ill patients ...

Harsh Parikh, et al. ∙

research

∙ 02/01/2022

Sequential Search with Off-Policy Reinforcement Learning

Recent years have seen a significant amount of interests in Sequential R...

Dadong Miao, et al. ∙

research

∙ 01/26/2022

A Cooperation-Aware Lane Change Method for Autonomous Vehicles

Lane change for autonomous vehicles (AVs) is an important but challengin...

Zihao Sheng, et al. ∙

research

∙ 01/11/2022

Ancestral instrument method for causal inference without a causal graph

Unobserved confounding is the main obstacle to causal effect estimation ...

Debo Cheng, et al. ∙

research

∙ 12/20/2021

Feature Selection for Efficient Local-to-Global Bayesian Network Structure Learning

Local-to-global learning approach plays an essential role in Bayesian ne...

Kui Yu, et al. ∙

research

∙ 12/17/2021

SiamTrans: Zero-Shot Multi-Frame Image Restoration with Pre-Trained Siamese Transformers

We propose a novel zero-shot multi-frame image restoration method for re...

Lin Liu, et al. ∙

research

∙ 05/30/2021

A splitting Hamiltonian Monte Carlo method for efficient sampling

We propose a splitting Hamiltonian Monte Carlo (SHMC) algorithm, which c...

Lei Li, et al. ∙

research

∙ 03/23/2021

Any Part of Bayesian Network Structure Learning

We study an interesting and challenging problem, learning any part of a ...

Zhaolong Ling, et al. ∙

research

∙ 03/11/2021

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

Causal Learner is a toolbox for learning causal structure and Markov bla...

Zhaolong Ling, et al. ∙

research

∙ 02/04/2021

A Learning-based Stochastic Driving Model for Autonomous Vehicle Testing

In the simulation-based testing and evaluation of autonomous vehicles (A...

Lin Liu, et al. ∙

research

∙ 12/06/2020

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...

Yin Teng, et al. ∙

research

∙ 11/16/2020

A Transfer Learning Based Active Learning Framework for Brain Tumor Classification

Brain tumor is one of the leading causes of cancer-related death globall...

Ruqian Hao, et al. ∙

research

∙ 11/13/2020

Dependency-based Anomaly Detection: Framework, Methods and Benchmark

Anomaly detection is an important research problem because anomalies oft...

Sha Lu, et al. ∙

research

∙ 11/12/2020

Learning causal representations for robust domain adaptation

Domain adaptation solves the learning problem in a target domain by leve...

Shuai Yang, et al. ∙

research

∙ 11/03/2020

Self-Adaptively Learning to Demoire from Focused and Defocused Image Pairs

Moire artifacts are common in digital photography, resulting from the in...

Lin Liu, et al. ∙

research

∙ 10/08/2020

Assessing the Fairness of Classifiers with Collider Bias

The increasing maturity of machine learning technologies and their appli...

Zhenlong Xu, et al. ∙

research

∙ 09/14/2020

Sufficient Dimension Reduction for Average Causal Effect Estimation

Having a large number of covariates can have a negative impact on the qu...

Debo Cheng, et al. ∙

research

∙ 08/07/2020

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...

Lin Liu, et al. ∙

research

∙ 07/24/2020

Adversarial Mixture Of Experts with Category Hierarchy Soft Constraint

Product search is the most common way for people to satisfy their shoppi...

Zhuojian Xiao, et al. ∙

research

∙ 07/20/2020

Local search for efficient causal effect estimation

Causal effect estimation from observational data is an important but cha...

Debo Cheng, et al. ∙

research

∙ 07/14/2020

Wavelet-Based Dual-Branch Network for Image Demoireing

When smartphone cameras are used to take photos of digital screens, usua...

Lin Liu, et al. ∙

research

∙ 07/14/2020

A unified survey on treatment effect heterogeneity modeling and uplift modeling

A central question in many fields of scientific research is to determine...

Weijia Zhang, et al. ∙

research

∙ 07/02/2020

Computational methods for cancer driver discovery: A survey

Motivation: Uncovering the genomic causes of cancer, known as cancer dri...

Vu Viet Hoang Pham, et al. ∙

research

∙ 06/21/2020

Clinically Relevant Mediation Analysis using Controlled Indirect Effect

Mediation analysis allows one to use observational data to estimate the ...

Haoqi Sun, et al. ∙

research

∙ 06/01/2020

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...

Ruqian Hao, et al. ∙

research

∙ 05/23/2020

Hierarchical Feature Embedding for Attribute Recognition

Attribute recognition is a crucial but challenging task due to viewpoint...

Jie Yang, et al. ∙

research

∙ 03/25/2020

A general framework for causal classification

In many applications, there is a need to predict the effect of an interv...

Jiuyong Li, et al. ∙

research

∙ 02/24/2020

Towards precise causal effect estimation from data with hidden variables

Causal effect estimation from observational data is a crucial but challe...

Debo Cheng, et al. ∙

research

∙ 01/29/2020

Treatment effect estimation with disentangled latent factors

A pressing concern faced by cancer patients is their prognosis under dif...

Weijia Zhang, et al. ∙

research

∙ 01/28/2020

Causal query in observational data with hidden variables

This paper discusses the problem of causal query in observational data w...

Debo Cheng, et al. ∙

research

∙ 01/23/2020

Receive Antenna Selection for Secure Pre-coding Aided Spatial Modulation

In this paper, we make an investigation of receive antenna selection (RA...

Lin Liu, et al. ∙

research

∙ 11/17/2019

Causality-based Feature Selection: Methods and Evaluations

Feature selection is a crucial preprocessing step in data analytics and ...

Kui Yu, et al. ∙

research

∙ 10/10/2019

Cooperative driving strategy based on naturalitic driving data and non-cooperative MPC

A cooperative driving strategy is proposed, in which the dynamic driving...

Rui Liu, et al. ∙

research

∙ 08/27/2019

Efficient estimation of optimal regimes under a no direct effect assumption

We derive new estimators of an optimal joint testing and treatment regim...

Lin Liu, et al. ∙

research

∙ 08/13/2019

Linking Graph Entities with Multiplicity and Provenance

Entity linking is a fundamental database problem with applicationsin dat...

Jixue Liu, et al. ∙

research

∙ 07/04/2019

A Deep-learning-based Joint Inference for Secure Spatial Modulation Receiver

As a green and secure wireless transmission way, secure spatial modulati...

Feng Shu, et al. ∙

research

∙ 06/14/2019

Identify treatment effect patterns for personalised decisions

In personalised decision making, evidence is required to determine suita...

Jiuyong Li, et al. ∙

research

∙ 06/09/2019

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

Network representation learning, as an approach to learn low dimensional...

Hao Peng, et al. ∙

research

∙ 04/08/2019

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...

Lin Liu, et al. ∙

research

∙ 02/13/2019

Distributionally Robust Multi-instance Learning with Stable Instances

Multi-instance learning (MIL) deals with tasks where data consist of set...

Weijia Zhang, et al. ∙

research

∙ 11/06/2018

An exploration of algorithmic discrimination in data and classification

Algorithmic discrimination is an important aspect when data is used for ...

Jixue Liu, et al. ∙

research

∙ 11/05/2018

FairMod - Making Predictive Models Discrimination Aware

Predictive models such as decision trees and neural networks may produce...

Jixue Liu, et al. ∙

research

∙ 08/20/2018

Discovering Context Specific Causal Relationships

With the increasing need of personalised decision making, such as person...

Saisai Ma, et al. ∙

research

∙ 02/16/2018

A Unified View of Causal and Non-causal Feature Selection

In this paper, we unify causal and non-causal feature feature selection ...

Kui Yu, et al. ∙

research

∙ 01/25/2018

Discovering Markov Blanket from Multiple interventional Datasets

In this paper, we study the problem of discovering the Markov blanket (M...

Kui Yu, et al. ∙

Lin Liu

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