Search Results for author: Lin Liu

Found 107 papers, 26 papers with code

Wavelet-Based Dual-Branch Network for Image Demoiréing

no code implementations ECCV 2020 Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Aleš Leonardis, Wengang Zhou, Qi Tian

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.

Image Restoration Rain Removal

Mini Honor of Kings: A Lightweight Environment for Multi-Agent Reinforcement Learning

no code implementations6 Jun 2024 Lin Liu, Jian Zhao, Cheng Hu, Zhengtao Cao, Youpeng Zhao, Zhenbin Ye, Meng Meng, Wenjun Wang, Zhaofeng He, Houqiang Li, Xia Lin, Lanxiao Huang

To address these issues, we introduce the first publicly available map editor for the popular mobile game Honor of Kings and design a lightweight environment, Mini Honor of Kings (Mini HoK), for researchers to conduct experiments.

Multi-agent Reinforcement Learning

A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search

no code implementations24 May 2024 Huimu Wang, Mingming Li, Dadong Miao, Songlin Wang, Guoyu Tang, Lin Liu, Sulong Xu, Jinghe Hu

Subsequently, we derive a utility matrix based on the correlations, enabling the adaptive ranking of items in line with user preferences and establishing a balance between the aforementioned objectives.

Re-Ranking Variational Inference

Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model

no code implementations9 May 2024 Enqiang Xu, Yiming Qiu, Junyang Bai, Ping Zhang, Dadong Miao, Songlin Wang, Guoyu Tang, Lin Liu, Mingming Li

In large e-commerce platforms, search systems are typically composed of a series of modules, including recall, pre-ranking, and ranking phases.

Binary Classification Contrastive Learning

Multimodal Information Interaction for Medical Image Segmentation

1 code implementation25 Apr 2024 Xinxin Fan, Lin Liu, Haoran Zhang

To address this issue, we introduce an innovative Multimodal Information Cross Transformer (MicFormer), which employs a dual-stream architecture to simultaneously extract features from each modality.

Heart Segmentation Image Segmentation +3

Mixup Domain Adaptations for Dynamic Remaining Useful Life Predictions

1 code implementation7 Apr 2024 Muhammad Tanzil Furqon, Mahardhika Pratama, Lin Liu, Habibullah, Kutluyil Dogancay

MDAN encompasses a three-staged mechanism where the mix-up strategy is not only performed to regularize the source and target domains but also applied to establish an intermediate mix-up domain where the source and target domains are aligned.

Domain Adaptation Self-Supervised Learning

Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning

no code implementations17 Mar 2024 Yuan Zhou, Richang Hong, Yanrong Guo, Lin Liu, Shijie Hao, Hanwang Zhang

In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i. e., relation disentanglement, which means enhancing FSCIL via disentangling spurious relation between categories.

Disentanglement Few-Shot Class-Incremental Learning +2

RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space

no code implementations4 Mar 2024 Li Sun, Mengjie Li, Yong Yang, Xiao Li, Lin Liu, Pengfei Zhang, Haohua Du

Annotating anchor users is laborious and expensive, and thus it is impractical to work with quantities of anchor users.

Graph Attention Link Prediction

Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and Outlook

no code implementations12 Jan 2024 Ziying Song, Lin Liu, Feiyang Jia, Yadan Luo, Guoxin Zhang, Lei Yang, Li Wang, Caiyan Jia

In the realm of modern autonomous driving, the perception system is indispensable for accurately assessing the state of the surrounding environment, thereby enabling informed prediction and planning.

3D Object Detection Autonomous Driving +2

RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAM

1 code implementation8 Jan 2024 Ziying Song, Guoxing Zhang, Lin Liu, Lei Yang, Shaoqing Xu, Caiyan Jia, Feiyang Jia, Li Wang

To align SAM or SAM-AD with multi-modal methods, we then introduce AD-FPN for upsampling the image features extracted by SAM.

3D Object Detection Autonomous Driving +2

VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection

no code implementations5 Jan 2024 Ziying Song, Guoxin Zhang, Jun Xie, Lin Liu, Caiyan Jia, Shaoqing Xu, Zhepeng Wang

In particular, we propose a voxel-based image pipeline that involves projecting point clouds onto images to obtain both pixel- and patch-level features.

3D Object Detection Feature Importance +2

Make-A-Character: High Quality Text-to-3D Character Generation within Minutes

no code implementations24 Dec 2023 Jianqiang Ren, Chao He, Lin Liu, Jiahao Chen, Yutong Wang, Yafei Song, Jianfang Li, Tangli Xue, Siqi Hu, Tao Chen, Kunkun Zheng, Jianjing Xiang, Liefeng Bo

There is a growing demand for customized and expressive 3D characters with the emergence of AI agents and Metaverse, but creating 3D characters using traditional computer graphics tools is a complex and time-consuming task.

3D Generation Text to 3D

Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders

no code implementations12 Dec 2023 Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Wentao Gao, Thuc Duy Le

Causal inference from longitudinal observational data is a challenging problem due to the difficulty in correctly identifying the time-dependent confounders, especially in the presence of latent time-dependent confounders.

Causal Inference

Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference

no code implementations8 Dec 2023 Debo Cheng, Yang Xie, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Yinghao Zhang, Zaiwen Feng

To address this problem with co-occurring M-bias and confounding bias, we propose a novel Disentangled Latent Representation learning framework for learning latent representations from proxy variables for unbiased Causal effect Estimation (DLRCE) from observational data.

Causal Inference Representation Learning

Three-Dimensional Medical Image Fusion with Deformable Cross-Attention

no code implementations10 Oct 2023 Lin Liu, Xinxin Fan, Chulong Zhang, Jingjing Dai, Yaoqin Xie, Xiaokun Liang

Furthermore, the prevailing methodologies are largely confined to fusing two-dimensional (2D) medical image slices, leading to a lack of contextual supervision in the fusion images and subsequently, a decreased information yield for physicians relative to three-dimensional (3D) images.


Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder

no code implementations3 Oct 2023 Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Kui Yu

In this paper, we relax some of the restrictions by proposing the concept of conditional front-door (CFD) adjustment and develop the theorem that guarantees the causal effect identifiability of CFD adjustment.

Causal Inference

Conditional Instrumental Variable Regression with Representation Learning for Causal Inference

no code implementations3 Oct 2023 Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le

To address these challenging and practical problems of the standard IV method (linearity assumption and the strict condition), in this paper, we use a conditional IV (CIV) to relax the unconfounded instrument condition of standard IV and propose a non-linear CIV regression with Confounding Balancing Representation Learning, CBRL. CIV, for jointly eliminating the confounding bias from unobserved confounders and balancing the observed confounders, without the linearity assumption.

Causal Inference regression +1

PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine

1 code implementation23 Aug 2023 Chenrui Zhang, Lin Liu, Jinpeng Wang, Chuyuan Wang, Xiao Sun, Hongyu Wang, Mingchen Cai

Moreover, to enhance stability of the prompt effect evaluation, we propose a novel prompt bagging method involving forward and backward thinking, which is superior to majority voting and is beneficial for both feedback and weight calculation in boosting.

Ensemble Learning Hallucination

Intestinal Microecology in Pediatric Surgery-Related Gastrointestinal Diseases Current Insights and Future Perspectives

no code implementations14 Aug 2023 Yingchao Li, Yuqing Wu, Suolin Li, Lin Liu, Xiaoyi Zhang, Jiaxun Lv, Qinqin Li

We review the relationship between pathogenesis, diagnosis and treatment of pediatric surgery-related gastrointestinal diseases and intestinal microecology, in order to provide new ideas and methods for clinical diagnosis, treatment and research.

SAR ATR under Limited Training Data Via MobileNetV3

1 code implementation27 Jun 2023 Chenwei Wang, Siyi Luo, Lin Liu, Yin Zhang, Jifang Pei, Yulin Huang, Jianyu Yang

In recent years, deep learning has been widely used to solve the bottleneck problem of synthetic aperture radar (SAR) automatic target recognition (ATR).

Few-Shot Continual Learning via Flat-to-Wide Approaches

1 code implementation26 Jun 2023 Muhammad Anwar Ma'sum, Mahardhika Pratama, Edwin Lughofer, Lin Liu, Habibullah, Ryszard Kowalczyk

This paper proposes a few-shot continual learning approach, termed FLat-tO-WidE AppRoach (FLOWER), where a flat-to-wide learning process finding the flat-wide minima is proposed to address the catastrophic forgetting problem.

Continual Learning Data Augmentation

Learning Conditional Instrumental Variable Representation for Causal Effect Estimation

1 code implementation21 Jun 2023 Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Thuc Duy Le, Jixue Liu

One of the fundamental challenges in causal inference is to estimate the causal effect of a treatment on its outcome of interest from observational data.

Causal Inference Representation Learning

Assumption-lean falsification tests of rate double-robustness of double-machine-learning estimators

no code implementations18 Jun 2023 Lin Liu, Rajarshi Mukherjee, James M. Robins

In many instances, an analyst justifies her claim by imposing complexity-reducing assumptions on $b$ and $p$ to ensure "rate double-robustness".


Exploring Effective Mask Sampling Modeling for Neural Image Compression

no code implementations9 Jun 2023 Lin Liu, Mingming Zhao, Shanxin Yuan, Wenlong Lyu, Wengang Zhou, Houqiang Li, Yanfeng Wang, Qi Tian

Specifically, Cube Mask Sampling Module (CMSM) is proposed to apply both spatial and channel mask sampling modeling to image compression in the pre-training stage.

Image Compression Self-Supervised Learning

Joint Channel Estimation and Feedback with Masked Token Transformers in Massive MIMO Systems

no code implementations8 Jun 2023 Mingming Zhao, Lin Liu, Lifu Liu, Mengke Li, Qi Tian

To achieve joint channel estimation and feedback, this paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix.

Decoder Denoising

SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model

2 code implementations NeurIPS 2023 Di Wang, Jing Zhang, Bo Du, Minqiang Xu, Lin Liu, DaCheng Tao, Liangpei Zhang

In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS.

Instance Segmentation Object +4

Linking a predictive model to causal effect estimation

no code implementations10 Apr 2023 Jiuyong Li, Lin Liu, Ziqi Xu, Ha Xuan Tran, Thuc Duy Le, Jixue Liu

This paper first tackles the challenge of estimating the causal effect of any feature (as the treatment) on the outcome w. r. t.

Decision Making Fairness

AST-SED: An Effective Sound Event Detection Method Based on Audio Spectrogram Transformer

no code implementations7 Mar 2023 Kang Li, Yan Song, Li-Rong Dai, Ian McLoughlin, Xin Fang, Lin Liu

In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED.

Audio Tagging Decoder +2

A Cooperation-Aware Lane Change Method for Automated Vehicles

no code implementations IEEE Transactions on Intelligent Transportation Systems 2023 Zihao Sheng, Lin Liu, Shibei Xue

Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV’s decision and surrounding vehicles’ interactive behaviors into constraints so as to avoid collisions during lane change.

Model Predictive Control Motion Planning +1

Disentangled Representation for Causal Mediation Analysis

1 code implementation19 Feb 2023 Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu, Lin Liu, Ke Wang

Causal mediation analysis is a method that is often used to reveal direct and indirect effects.

New $\sqrt{n}$-consistent, numerically stable higher-order influence function estimators

no code implementations16 Feb 2023 Lin Liu, Chang Li

Higher-Order Influence Functions (HOIFs) provide a unified theory for constructing rate-optimal estimators for a large class of low-dimensional (smooth) statistical functionals/parameters (and sometimes even infinite-dimensional functions) that arise in substantive fields including epidemiology, economics, and the social sciences.


Face Clustering via Graph Convolutional Networks with Confidence Edges

no code implementations ICCV 2023 Yang Wu, Zhiwei Ge, Yuhao Luo, Lin Liu, Sulong Xu

Experiments show that our method outperforms existing methods on the face and person datasets to achieve state-of-the-art.

Clustering Face Clustering

DG3D: Generating High Quality 3D Textured Shapes by Learning to Discriminate Multi-Modal Diffusion-Renderings

no code implementations ICCV 2023 Qi Zuo, Yafei Song, Jianfang Li, Lin Liu, Liefeng Bo

Many virtual reality applications require massive 3D content, which impels the need for low-cost and efficient modeling tools in terms of quality and quantity.

Causal Inference with Conditional Instruments using Deep Generative Models

no code implementations29 Nov 2022 Debo Cheng, Ziqi Xu, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le

The instrumental variable (IV) approach is a widely used way to estimate the causal effects of a treatment on an outcome of interest from observational data with latent confounders.

Causal Inference

DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning

1 code implementation10 Oct 2022 Siqi Xu, Lin Liu, Zhonghua Liu

Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness.

Causal Inference Fairness

Requirements Engineering for Machine Learning: A Review and Reflection

no code implementations3 Oct 2022 Zhongyi Pei, Lin Liu, Chen Wang, Jianmin Wang

Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes.

Decision Making

Low-Light Video Enhancement with Synthetic Event Guidance

no code implementations23 Aug 2022 Lin Liu, Junfeng An, Jianzhuang Liu, Shanxin Yuan, Xiangyu Chen, Wengang Zhou, Houqiang Li, Yanfeng Wang, Qi Tian

Low-light video enhancement (LLVE) is an important yet challenging task with many applications such as photographing and autonomous driving.

Autonomous Driving Image Enhancement +1

Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Survey

no code implementations20 Aug 2022 Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu, Thuc Duy Le

In recent years, research has emerged to use search strategies based on graphical causal modelling to discover useful knowledge from data for causal effect estimation, with some mild assumptions, and has shown promise in tackling the practical challenge.

Decision Making

Disentangled Representation with Causal Constraints for Counterfactual Fairness

no code implementations19 Aug 2022 Ziqi Xu, Jixue Liu, Debo Cheng, Jiuyong Li, Lin Liu, Ke Wang

Much research has been devoted to the problem of learning fair representations; however, they do not explicitly the relationship between latent representations.

counterfactual Fairness +1

Explanatory causal effects for model agnostic explanations

no code implementations23 Jun 2022 Jiuyong Li, Ha Xuan Tran, Thuc Duy Le, Lin Liu, Kui Yu, Jixue Liu

This paper studies the problem of estimating the contributions of features to the prediction of a specific instance by a machine learning model and the overall contribution of a feature to the model.

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

no code implementations25 May 2022 Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Richard Shaw, Aleš Leonardis, Radu Timofte, Zexin Zhang, Cen Liu, Yunbo Peng, Yue Lin, Gaocheng Yu, Jin Zhang, Zhe Ma, Hongbin Wang, Xiangyu Chen, Xintao Wang, Haiwei Wu, Lin Liu, Chao Dong, Jiantao Zhou, Qingsen Yan, Song Zhang, Weiye Chen, Yuhang Liu, Zhen Zhang, Yanning Zhang, Javen Qinfeng Shi, Dong Gong, Dan Zhu, Mengdi Sun, Guannan Chen, Yang Hu, Haowei Li, Baozhu Zou, Zhen Liu, Wenjie Lin, Ting Jiang, Chengzhi Jiang, Xinpeng Li, Mingyan Han, Haoqiang Fan, Jian Sun, Shuaicheng Liu, Juan Marín-Vega, Michael Sloth, Peter Schneider-Kamp, Richard Röttger, Chunyang Li, Long Bao, Gang He, Ziyao Xu, Li Xu, Gen Zhan, Ming Sun, Xing Wen, Junlin Li, Shuang Feng, Fei Lei, Rui Liu, Junxiang Ruan, Tianhong Dai, Wei Li, Zhan Lu, Hengyan Liu, Peian Huang, Guangyu Ren, Yonglin Luo, Chang Liu, Qiang Tu, Fangya Li, Ruipeng Gang, Chenghua Li, Jinjing Li, Sai Ma, Chenming Liu, Yizhen Cao, Steven Tel, Barthelemy Heyrman, Dominique Ginhac, Chul Lee, Gahyeon Kim, Seonghyun Park, An Gia Vien, Truong Thanh Nhat Mai, Howoon Yoon, Tu Vo, Alexander Holston, Sheir Zaheer, Chan Y. Park

The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i. e. solutions can not exceed a given number of operations).

Image Restoration Vocal Bursts Intensity Prediction

Secuer: ultrafast, scalable and accurate clustering of single-cell RNA-seq data

2 code implementations25 May 2022 Nana Wei, Yating Nie, Lin Liu, Xiaoqi Zheng, Hua-Jun Wu4

Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again greatly improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy.


Global Update Guided Federated Learning

no code implementations8 Apr 2022 Qilong Wu, Lin Liu, Shibei Xue

Furthermore, considering that the update direction of a global model is informative in the early stage of training, we propose adaptive loss weights based on the update distances of local models.

Federated Learning

Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients

no code implementations9 Mar 2022 Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover

Having a maximum EA burden greater than 75% when untreated had a 22% increased chance of a poor outcome (severe disability or death), and mild but long-lasting EA increased the risk of a poor outcome by 14%.

Causal Inference Decision Making

Sequential Search with Off-Policy Reinforcement Learning

no code implementations1 Feb 2022 Dadong Miao, Yanan Wang, Guoyu Tang, Lin Liu, Sulong Xu, Bo Long, Yun Xiao, Lingfei Wu, Yunjiang Jiang

Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.

reinforcement-learning Reinforcement Learning (RL) +1

A Cooperation-Aware Lane Change Method for Autonomous Vehicles

no code implementations26 Jan 2022 Zihao Sheng, Lin Liu, Shibei Xue, Dezong Zhao, Min Jiang, Dewei Li

Further, an evaluation is designed to make a decision on lane change, in which safety, efficiency and comfort are taken into consideration.

Autonomous Vehicles Model Predictive Control +2

Ancestral Instrument Method for Causal Inference without Complete Knowledge

no code implementations11 Jan 2022 Debo Cheng, Jiuyong Li, Lin Liu, Jiji Zhang, Thuc Duy Le, Jixue Liu

Based on the theory, we develop an algorithm for unbiased causal effect estimation with a given ancestral IV and observational data.

Causal Inference valid

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

no code implementations17 Dec 2021 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Xin Guo, Youliang Yan, Qi Tian

For zero-shot image restoration, we design a novel model, termed SiamTrans, which is constructed by Siamese transformers, encoders, and decoders.

Denoising Image Restoration +1

Dynamic Event-Triggered Consensus of Multi-agent Systems on Matrix-weighted Networks

no code implementations11 Jun 2021 Lulu Pan, Haibin Shao, Dewei Li, Lin Liu

This paper examines the event-triggered consensus of the multi-agent system on matrix-weighted networks, where the interdependencies among higher-dimensional states of neighboring agents are characterized by matrix-weighted edges in the network.

Any Part of Bayesian Network Structure Learning

no code implementations23 Mar 2021 Zhaolong Ling, Kui Yu, Hao Wang, Lin Liu, Jiuyong Li

We study an interesting and challenging problem, learning any part of a Bayesian network (BN) structure.

feature selection

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

1 code implementation11 Mar 2021 Zhaolong Ling, Kui Yu, Yiwen Zhang, Lin Liu, Jiuyong Li

Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data.

A Learning-based Stochastic Driving Model for Autonomous Vehicle Testing

no code implementations4 Feb 2021 Lin Liu, Shuo Feng, Yiheng Feng, Xichan Zhu, Henry X. Liu

However, pre-determined BV trajectories can not react to the AV's maneuvers, and deterministic models are different from real human drivers due to the lack of stochastic components and errors.

Autonomous Vehicles

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

no code implementations NeurIPS 2020 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian

In this paper, we propose a self-adaptive learning method for demoiréing a high-frequency image, with the help of an additional defocused moiré-free blur image.

Test-time Adaptation

A Transfer Learning Based Active Learning Framework for Brain Tumor Classification

no code implementations16 Nov 2020 Ruqian Hao, Khashayar Namdar, Lin Liu, Farzad Khalvati

The model achieved AUC of 82% compared with AUC of 78. 48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.

Active Learning General Classification +1

Learning causal representations for robust domain adaptation

no code implementations12 Nov 2020 Shuai Yang, Kui Yu, Fuyuan Cao, Lin Liu, Hao Wang, Jiuyong Li

In this paper, we study the cases where at the training phase the target domain data is unavailable and only well-labeled source domain data is available, called robust domain adaptation.

Domain Adaptation

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

1 code implementation3 Nov 2020 Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian

In this paper, we propose a self-adaptive learning method for demoireing a high-frequency image, with the help of an additional defocused moire-free blur image.

Demoire Test-time Adaptation

Assessing Classifier Fairness with Collider Bias

no code implementations8 Oct 2020 Zhenlong Xu, Jixue Liu, Debo Cheng, Jiuyong Li, Lin Liu, Ke Wang, Ziqi Xu, Zhenlong Xu contributed equally to this paper

The increasing application of machine learning techniques in everyday decision-making processes has brought concerns about the fairness of algorithmic decision-making.

Decision Making Fairness

Sufficient Dimension Reduction for Average Causal Effect Estimation

no code implementations14 Sep 2020 Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu

Having a large number of covariates can have a negative impact on the quality of causal effect estimation since confounding adjustment becomes unreliable when the number of covariates is large relative to the samples available.

counterfactual Dimensionality Reduction

Rejoinder: On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

no code implementations7 Aug 2020 Lin Liu, Rajarshi Mukherjee, James M. Robins

This is the rejoinder to the discussion by Kennedy, Balakrishnan and Wasserman on the paper "On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning" published in Statistical Science.

BIG-bench Machine Learning

A unified survey of treatment effect heterogeneity modeling and uplift modeling

no code implementations14 Jul 2020 Weijia Zhang, Jiuyong Li, Lin Liu

A central question in many fields of scientific research is to determine how an outcome would be affected by an action, or to measure the effect of an action (a. k. a treatment effect).


Wavelet-Based Dual-Branch Network for Image Demoireing

1 code implementation14 Jul 2020 Lin Liu, Jianzhuang Liu, Shanxin Yuan, Gregory Slabaugh, Ales Leonardis, Wengang Zhou, Qi Tian

When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality.

Demoire Image Restoration +1

Computational methods for cancer driver discovery: A survey

no code implementations2 Jul 2020 Vu Viet Hoang Pham, Lin Liu, Cameron Bracken, Gregory Goodall, Jiuyong Li, Thuc Duy Le

Due to the complexity of the mechanistic insight of cancer genes in driving cancer and the fast development of the field, it is necessary to have a comprehensive review about the current computational methods for discovering different types of cancer drivers.

Driver Identification

A Comprehensive Study of Data Augmentation Strategies for Prostate Cancer Detection in Diffusion-weighted MRI using Convolutional Neural Networks

no code implementations1 Jun 2020 Ruqian Hao, Khashayar Namdar, Lin Liu, Masoom A. Haider, Farzad Khalvati

Data augmentation refers to a group of techniques whose goal is to battle limited amount of available data to improve model generalization and push sample distribution toward the true distribution.

Data Augmentation

Joint Demosaicing and Denoising With Self Guidance

1 code implementation CVPR 2020 Lin Liu, Xu Jia, Jianzhuang Liu, Qi Tian

In this paper, we propose a self-guidance network (SGNet), where the green channels are initially estimated and then works as a guidance to recover all missing values in the input image.

Demosaicking Denoising +2

Hierarchical Feature Embedding for Attribute Recognition

no code implementations CVPR 2020 Jie Yang, Jiarou Fan, Yiru Wang, Yige Wang, Weihao Gan, Lin Liu, Wei Wu

Attribute recognition is a crucial but challenging task due to viewpoint changes, illumination variations and appearance diversities, etc.


A general framework for causal classification

no code implementations25 Mar 2020 Jiuyong Li, Weijia Zhang, Lin Liu, Kui Yu, Thuc Duy Le, Jixue Liu

We also propose a general framework for causal classification, by using off-the-shelf supervised methods for flexible implementations.

Classification Decision Making +2

Towards unique and unbiased causal effect estimation from data with hidden variables

no code implementations24 Feb 2020 Debo Cheng, Jiuyong Li, Lin Liu, Kui Yu, Thuc Duy Lee, Jixue Liu

Based on the theorems, two algorithms are proposed for finding the proper adjustment sets from data with hidden variables to obtain unbiased and unique causal effect estimation.

Treatment effect estimation with disentangled latent factors

2 code implementations29 Jan 2020 Weijia Zhang, Lin Liu, Jiuyong Li

Much research has been devoted to the problem of estimating treatment effects from observational data; however, most methods assume that the observed variables only contain confounders, i. e., variables that affect both the treatment and the outcome.

Variational Inference

Causal query in observational data with hidden variables

no code implementations28 Jan 2020 Debo Cheng, Jiuyong Li, Lin Liu, Jixue Liu, Kui Yu, Thuc Duy Le

In this paper, we develop a theorem for using local search to find a superset of the adjustment (or confounding) variables for causal effect estimation from observational data under a realistic pretreatment assumption.

Causality-based Feature Selection: Methods and Evaluations

1 code implementation17 Nov 2019 Kui Yu, Xianjie Guo, Lin Liu, Jiuyong Li, Hao Wang, Zhaolong Ling, Xindong Wu

It has been shown that the knowledge about the causal relationships between features and the class variable has potential benefits for building interpretable and robust prediction models, since causal relationships imply the underlying mechanism of a system.

feature selection

Linking Graph Entities with Multiplicity and Provenance

no code implementations13 Aug 2019 Jixue Liu, Selasi Kwashie, Jiuyong Li, Lin Liu, Michael Bewong

The graph model is versatile, thus, it is capable of handling multiple values for an attribute or a relationship, as well as the provenance descriptions of the values.

Attribute Data Integration +5

Identify treatment effect patterns for personalised decisions

no code implementations14 Jun 2019 Jiuyong Li, Lin Liu, Shisheng Zhang, Saisai Ma, Thuc Duy Le, Jixue Liu

The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual.

Decision Making

Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling

1 code implementation9 Jun 2019 Hao Peng, Jian-Xin Li, Hao Yan, Qiran Gong, Senzhang Wang, Lin Liu, Lihong Wang, Xiang Ren

Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.

Link Prediction Multi-Label Classification +1

On nearly assumption-free tests of nominal confidence interval coverage for causal parameters estimated by machine learning

no code implementations8 Apr 2019 Lin Liu, Rajarshi Mukherjee, James M. Robins

In this paper, we introduce essentially assumption-free tests that (i) can falsify the null hypothesis that the bias of $\hat{\psi}_{1}$ is of smaller order than its standard error, (ii) can provide an upper confidence bound on the true coverage of the Wald interval, and (iii) are valid under the null under no smoothness/sparsity assumptions on the nuisance parameters.

BIG-bench Machine Learning valid

Robust Multi-instance Learning with Stable Instances

no code implementations13 Feb 2019 Weijia Zhang, Jiuyong Li, Lin Liu

Multi-instance learning (MIL) deals with tasks where data is represented by a set of bags and each bag is described by a set of instances.

Causal Inference Image Classification

An exploration of algorithmic discrimination in data and classification

no code implementations6 Nov 2018 Jixue Liu, Jiuyong Li, Feiyue Ye, Lin Liu, Thuc Duy Le, Ping Xiong

The paper uses real world data sets to demonstrate the existence of discrimination and the independence between the discrimination of data sets and the discrimination of classification models.

Classification General Classification

FairMod - Making Predictive Models Discrimination Aware

no code implementations5 Nov 2018 Jixue Liu, Jiuyong Li, Lin Liu, Thuc Duy Le, Feiyue Ye, Gefei Li

It models the post-processing of predictions problem as a nonlinear optimization problem to find best adjustments to the predictions so that the discrimination constraints of all protected variables are all met at the same time.

General Classification

Discovering Context Specific Causal Relationships

no code implementations20 Aug 2018 Saisai Ma, Jiuyong Li, Lin Liu, Thuc Duy Le

With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships.

Causal Inference Decision Making +1

A Unified View of Causal and Non-causal Feature Selection

no code implementations16 Feb 2018 Kui Yu, Lin Liu, Jiuyong Li

The unified view will fill in the gap in the research of the relation between the two types of methods.

Attribute feature selection

Discovering Markov Blanket from Multiple interventional Datasets

no code implementations25 Jan 2018 Kui Yu, Lin Liu, Jiuyong Li

In this paper, we study the problem of discovering the Markov blanket (MB) of a target variable from multiple interventional datasets.

Building Diversified Multiple Trees for Classification in High Dimensional Noisy Biomedical Data

no code implementations18 Dec 2016 Jiuyong Li, Lin Liu, Jixue Liu, Ryan Green

It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise.

General Classification

A Review on Algorithms for Constraint-based Causal Discovery

no code implementations12 Nov 2016 Kui Yu, Jiuyong Li, Lin Liu

Recent years, as the availability of abundant large-sized and complex observational data, the constrain-based approaches have gradually attracted a lot of interest and have been widely applied to many diverse real-world problems due to the fast running speed and easy generalizing to the problem of causal insufficiency.

Causal Discovery

The ELRA License Wizard

no code implementations LREC 2016 Val{\'e}rie Mapelli, Vladimir Popescu, Lin Liu, Meritxell Fern{\'a}ndez Barrera, Khalid Choukri

To allow an easy understanding of the various licenses that exist for the use of Language Resources (ELRA{'}s, META-SHARE{'}s, Creative Commons{'}, etc.

feature selection

New Developments in the LRE Map

no code implementations LREC 2016 Vladimir Popescu, Lin Liu, Riccardo Del Gratta, Khalid Choukri, Nicoletta Calzolari

In this paper we describe the new developments brought to LRE Map, especially in terms of the user interface of the Web application, of the searching of the information therein, and of the data model updates.

Language Resource Citation: the ISLRN Dissemination and Further Developments

no code implementations LREC 2016 Val{\'e}rie Mapelli, Vladimir Popescu, Lin Liu, Khalid Choukri

This article presents the latest dissemination activities and technical developments that were carried out for the International Standard Language Resource Number (ISLRN) service.


ParallelPC: an R package for efficient constraint based causal exploration

no code implementations11 Oct 2015 Thuc Duy Le, Tao Hoang, Jiuyong Li, Lin Liu, Shu Hu

Discovering causal relationships from data is the ultimate goal of many research areas.

Mining Combined Causes in Large Data Sets

no code implementations28 Aug 2015 Saisai Ma, Jiuyong Li, Lin Liu, Thuc Duy Le

A straightforward approach to uncovering a combined cause is to include both individual and combined variables in the causal discovery using existing methods, but this scheme is computationally infeasible due to the huge number of combined variables.

Causal Discovery Computational Efficiency

Causal Decision Trees

no code implementations16 Aug 2015 Jiuyong Li, Saisai Ma, Thuc Duy Le, Lin Liu, Jixue Liu

Classification methods are fast and they could be practical substitutes for finding causal signals in data.

Causal Discovery Causal Inference +2

From Observational Studies to Causal Rule Mining

no code implementations16 Aug 2015 Jiuyong Li, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, Bingyu Sun, Saisai Ma

Specifically, association rule mining can be used to deal with the high-dimensionality problem while observational studies can be utilised to eliminate non-causal associations.

Causal Discovery

A fast PC algorithm for high dimensional causal discovery with multi-core PCs

no code implementations9 Feb 2015 Thuc Duy Le, Tao Hoang, Jiuyong Li, Lin Liu, Huawen Liu

However, runtime of the PC algorithm, in the worst-case, is exponential to the number of nodes (variables), and thus it is inefficient when being applied to high dimensional data, e. g. gene expression datasets.

Causal Discovery Causal Inference

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