Search Results for author: Furui Liu

Found 31 papers, 9 papers with code

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples

no code implementations21 Dec 2023 Ruichu Cai, Yuxuan Zhu, Jie Qiao, Zefeng Liang, Furui Liu, Zhifeng Hao

By considering the underappreciated causal generating process, first, we pinpoint the source of the vulnerability of DNNs via the lens of causality, then give theoretical results to answer \emph{where to attack}.


Specify Robust Causal Representation from Mixed Observations

1 code implementation21 Oct 2023 Mengyue Yang, Xinyu Cai, Furui Liu, Weinan Zhang, Jun Wang

Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models.

CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Grading

no code implementations27 Sep 2023 Hao Wei, Peilun Shi, Juzheng Miao, Minqing Zhang, Guitao Bai, Jianing Qiu, Furui Liu, Wu Yuan

Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics.

Diabetic Retinopathy Grading Domain Generalization

Invariant Learning via Probability of Sufficient and Necessary Causes

1 code implementation NeurIPS 2023 Mengyue Yang, Zhen Fang, Yonggang Zhang, Yali Du, Furui Liu, Jean-Francois Ton, Jianhong Wang, Jun Wang

To capture the information of sufficient and necessary causes, we employ a classical concept, the probability of sufficiency and necessary causes (PNS), which indicates the probability of whether one is the necessary and sufficient cause.

Meta Adaptive Task Sampling for Few-Domain Generalization

no code implementations25 May 2023 Zheyan Shen, Han Yu, Peng Cui, Jiashuo Liu, Xingxuan Zhang, Linjun Zhou, Furui Liu

Moreover, we propose a Meta Adaptive Task Sampling (MATS) procedure to differentiate base tasks according to their semantic and domain-shift similarity to the novel task.

Domain Generalization

Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning

1 code implementation21 Mar 2023 Yang Yu, Danruo Deng, Furui Liu, Yueming Jin, Qi Dou, Guangyong Chen, Pheng-Ann Heng

Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers).

Outlier Detection

Traj-MAE: Masked Autoencoders for Trajectory Prediction

no code implementations ICCV 2023 Hao Chen, Jiaze Wang, Kun Shao, Furui Liu, Jianye Hao, Chenyong Guan, Guangyong Chen, Pheng-Ann Heng

Specifically, our Traj-MAE employs diverse masking strategies to pre-train the trajectory encoder and map encoder, allowing for the capture of social and temporal information among agents while leveraging the effect of environment from multiple granularities.

Autonomous Driving Trajectory Prediction

Uncertainty Estimation by Fisher Information-based Evidential Deep Learning

1 code implementation3 Mar 2023 Danruo Deng, Guangyong Chen, Yang Yu, Furui Liu, Pheng-Ann Heng

To address this problem, we propose a novel method, Fisher Information-based Evidential Deep Learning ($\mathcal{I}$-EDL).

Informativeness Representation Learning

RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction

1 code implementation CVPR 2023 Donghao Zhou, Chunbin Gu, Junde Xu, Furui Liu, Qiong Wang, Guangyong Chen, Pheng-Ann Heng

In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures.

ConfounderGAN: Protecting Image Data Privacy with Causal Confounder

no code implementations4 Dec 2022 Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu

The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet.

Generative Adversarial Network Image Classification

Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation

1 code implementation23 Aug 2022 Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu

The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each source with an independent treatment assignment mechanism).


S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?

no code implementations20 Jun 2022 Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu

To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations.

Multi-agent Reinforcement Learning Reinforcement Learning (RL) +2

Generalizable Information Theoretic Causal Representation

no code implementations17 Feb 2022 Mengyue Yang, Xinyu Cai, Furui Liu, Xu Chen, Zhitang Chen, Jianye Hao, Jun Wang

It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommender systems.

counterfactual Image Classification +2

Debiased Recommendation with User Feature Balancing

no code implementations16 Jan 2022 Mengyue Yang, Guohao Cai, Furui Liu, Zhenhua Dong, Xiuqiang He, Jianye Hao, Jun Wang, Xu Chen

To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.

Causal Inference Recommendation Systems

Informative Robust Causal Representation for Generalizable Deep Learning

no code implementations29 Sep 2021 Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jianye Hao, Jun Wang

In many real-world scenarios, such as image classification and recommender systems, it is evidence that representation learning can improve model's performance over multiple downstream tasks.

counterfactual Image Classification +2

Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms

no code implementations2 Jun 2021 Yunqi Wang, Furui Liu, Zhitang Chen, Qing Lian, Shoubo Hu, Jianye Hao, Yik-Chung Wu

Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains.

Domain Generalization

Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning

no code implementations1 Jun 2021 Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao

Specifically, Shapley Value and its desired properties are leveraged in deep MARL to credit any combinations of agents, which grants us the capability to estimate the individual credit for each agent.

counterfactual Multi-agent Reinforcement Learning +4

Learning to Select Cuts for Efficient Mixed-Integer Programming

no code implementations28 May 2021 Zeren Huang, Kerong Wang, Furui Liu, Hui-Ling Zhen, Weinan Zhang, Mingxuan Yuan, Jianye Hao, Yong Yu, Jun Wang

In the online A/B testing of the product planning problems with more than $10^7$ variables and constraints daily, Cut Ranking has achieved the average speedup ratio of 12. 42% over the production solver without any accuracy loss of solution.

Multiple Instance Learning

Causal World Models by Unsupervised Deconfounding of Physical Dynamics

no code implementations28 Dec 2020 Minne Li, Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jun Wang

The capability of imagining internally with a mental model of the world is vitally important for human cognition.


Weakly Supervised Disentangled Generative Causal Representation Learning

1 code implementation6 Oct 2020 Xinwei Shen, Furui Liu, Hanze Dong, Qing Lian, Zhitang Chen, Tong Zhang

This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information.


Decoder-free Robustness Disentanglement without (Additional) Supervision

no code implementations2 Jul 2020 Yifei Wang, Dan Peng, Furui Liu, Zhenguo Li, Zhitang Chen, Jiansheng Yang

Adversarial Training (AT) is proposed to alleviate the adversarial vulnerability of machine learning models by extracting only robust features from the input, which, however, inevitably leads to severe accuracy reduction as it discards the non-robust yet useful features.

BIG-bench Machine Learning Disentanglement

CausalVAE: Structured Causal Disentanglement in Variational Autoencoder

2 code implementations CVPR 2021 Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang

Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data.

counterfactual Disentanglement

DO-AutoEncoder: Learning and Intervening Bivariate Causal Mechanisms in Images

no code implementations25 Sep 2019 Tianshuo Cong, Dan Peng, Furui Liu, Zhitang Chen

Our experiments demonstrate our method is able to correctly identify the bivariate causal relationship between concepts in images and the representation learned enables a do-calculus manipulation to images, which generates artificial images that might possibly break the physical law depending on where we intervene the causal system.

Adversarial Attack Representation Learning

Causal Inference on Discrete Data via Estimating Distance Correlations

no code implementations21 Mar 2018 Furui Liu, Laiwan Chan

In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain.

Causal Inference

Confounder Detection in High Dimensional Linear Models using First Moments of Spectral Measures

no code implementations19 Mar 2018 Furui Liu, Laiwan Chan

Based on an assumption of rotation invariant generating process of the model, recent study shows that the spectral measure induced by the regression coefficient vector with respect to the covariance matrix of $X_n$ is close to a uniform measure in purely causal cases, but it differs from a uniform measure characteristically in the presence of a scalar confounder.


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