Search Results for author: Kun Zhang

Found 115 papers, 29 papers with code

LTF: A Label Transformation Framework for Correcting Label Shift

no code implementations ICML 2020 Jiaxian Guo, Mingming Gong, Tongliang Liu, Kun Zhang, DaCheng Tao

Distribution shift is a major obstacle to the deployment of current deep learning models on real-world problems.

Label-Noise Robust Domain Adaptation

no code implementations ICML 2020 Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, DaCheng Tao

Domain adaptation aims to correct the classifiers when faced with distribution shift between source (training) and target (test) domains.

Denoising Domain Adaptation

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

1 code implementation14 Jan 2022 Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang

Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness.

Causal Discovery

Perlin Noise Improve Adversarial Robustness

no code implementations26 Dec 2021 Chengjun Tang, Kun Zhang, Chunfang Xing, Yong Ding, Zengmin Xu

Combined with the defensive idea of adversarial training, we use Perlin noise to train the neural network to obtain a model that can defend against procedural noise adversarial examples.

Adversarial Robustness

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

1 code implementation NeurIPS 2021 Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang

Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness.

Causal Discovery

Domain Adaptation with Invariant Representation Learning: What Transformations to Learn?

1 code implementation NeurIPS 2021 Petar Stojanov, Zijian Li, Mingming Gong, Ruichu Cai, Jaime Carbonell, Kun Zhang

We provide reasoning why when the supports of the source and target data from overlap, any map of $X$ that is fixed across domains may not be suitable for domain adaptation via invariant features.

Representation Learning Transfer Learning +1

Transferable Time-Series Forecasting under Causal Conditional Shift

no code implementations5 Nov 2021 Zijian Li, Ruichu Cai, Tom Z. J Fu, Kun Zhang

In order to address these challenges, we analyze variational conditional dependencies in time-series data and find that the causal structures are usually stable among domains, and further raise the causal conditional shift assumption.

Domain Adaptation Time Series +1

Nash equilibrium of multi-agent graphical game with a privacy information encrypted learning algorithm

no code implementations29 Oct 2021 Kun Zhang, Ji-Feng Zhang, Rong Su, Huaguang Zhang

With the secure hierarchical structure, the relationship between the secure consensus problem and global Nash equilibrium is discussed under potential packet loss attacks, and the necessary and sufficient condition for the existence of global Nash equilibrium is provided regarding the soft-constrained graphical game.

Quantization

Towards Federated Bayesian Network Structure Learning with Continuous Optimization

no code implementations18 Oct 2021 Ignavier Ng, Kun Zhang

Traditionally, Bayesian network structure learning is often carried out at a central site, in which all data is gathered.

Federated Learning

Action-Sufficient State Representation Learning for Control with Structural Constraints

no code implementations12 Oct 2021 Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang

Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.

Decision Making Representation Learning

Learning Temporally Causal Latent Processes from General Temporal Data

2 code implementations11 Oct 2021 Weiran Yao, Yuewen Sun, Alex Ho, Changyin Sun, Kun Zhang

In this work, we consider both a nonparametric, nonstationary setting and a parametric setting for the latent processes and propose two provable conditions under which temporally causal latent processes can be identified from their nonlinear mixtures.

Causal Discovery Representation Learning +1

Unaligned Image-to-Image Translation by Learning to Reweight

1 code implementation ICCV 2021 Shaoan Xie, Mingming Gong, Yanwu Xu, Kun Zhang

An essential yet restrictive assumption for unsupervised image translation is that the two domains are aligned, e. g., for the selfie2anime task, the anime (selfie) domain must contain only anime (selfie) face images that can be translated to some images in the other domain.

Translation Unsupervised Image-To-Image Translation

A Fast PC Algorithm with Reversed-order Pruning and A Parallelization Strategy

no code implementations10 Sep 2021 Kai Zhang, Chao Tian, Kun Zhang, Todd Johnson, Xiaoqian Jiang

The PC algorithm is the state-of-the-art algorithm for causal structure discovery on observational data.

Generalized Minimum Error Entropy for Adaptive Filtering

no code implementations8 Sep 2021 Jiacheng He, Gang Wang, Bei Peng, Zhenyu Feng, Kun Zhang

In our study, a novel concept, called generalized error entropy, utilizing the generalized Gaussian density (GGD) function as the kernel function is proposed.

Instance-dependent Label-noise Learning under a Structural Causal Model

no code implementations NeurIPS 2021 Yu Yao, Tongliang Liu, Mingming Gong, Bo Han, Gang Niu, Kun Zhang

In particular, we show that properly modeling the instances will contribute to the identifiability of the label noise transition matrix and thus lead to a better classifier.

DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis

1 code implementation ICCV 2021 Shulan Ruan, Yong Zhang, Kun Zhang, Yanbo Fan, Fan Tang, Qi Liu, Enhong Chen

Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency.

Image Generation Sentence Embedding

Outdoor Position Recovery from HeterogeneousTelco Cellular Data

no code implementations24 Aug 2021 Yige Zhang, Weixiong Rao, Kun Zhang, Lei Chen

The HMM approaches typically assume stable mobility patterns of the underlying mobile devices.

Multi-Task Learning

LadRa-Net: Locally-Aware Dynamic Re-read Attention Net for Sentence Semantic Matching

no code implementations6 Aug 2021 Kun Zhang, Guangyi Lv, Le Wu, Enhong Chen, Qi Liu, Meng Wang

In order to overcome this problem and boost the performance of attention mechanism, we propose a novel dynamic re-read attention, which can pay close attention to one small region of sentences at each step and re-read the important parts for better sentence representations.

Language Modelling Natural Language Inference +1

Multi-Channel Auto-Encoders and a Novel Dataset for Learning Domain Invariant Representations of Histopathology Images

no code implementations15 Jul 2021 Andrew Moyes, Richard Gault, Kun Zhang, Ji Ming, Danny Crookes, Jing Wang

Experimental results show that the MCAE model produces feature representations that are less sensitive to inter-domain variations than the comparative StaNoSA method when tested on the novel synthetic data.

AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning

no code implementations6 Jul 2021 Biwei Huang, Fan Feng, Chaochao Lu, Sara Magliacane, Kun Zhang

We show that by explicitly leveraging this compact representation to encode changes, we can efficiently adapt the policy to the target domain, in which only a few samples are needed and further policy optimization is avoided.

Atari Games Transfer Reinforcement Learning

Region-Aware Network: Model Human's Top-Down Visual Perception Mechanism for Crowd Counting

no code implementations23 Jun 2021 Yuehai Chen, Jing Yang, Dong Zhang, Kun Zhang, Badong Chen, Shaoyi Du

More specifically, we scan the whole input images and its priority maps in the form of column vector to obtain a relevance matrix estimating their similarity.

Crowd Counting

AKG: Automatic Kernel Generation for Neural Processing Units using Polyhedral Transformations

1 code implementation Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation 2021 Jie Zhao, Bojie Li, Wang Nie, Zhen Geng, Renwei Zhang, Xiong Gao, Bin Cheng, Chen Wu, Yun Cheng, Zheng Li, Peng Di, Kun Zhang, Xuefeng Jin

Existing tensor compilers have proven their effectiveness in deploying deep neural networks on general-purpose hardware like CPU and GPU, but optimizing for neural processing units (NPUs) is still challenging due to the heterogeneous compute units and complicated memory hierarchy.

Code Generation

Graph Domain Adaptation: A Generative View

no code implementations14 Jun 2021 Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, Kun Zhang

Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules.

Graph Classification Graph Learning +1

Adversarial Robustness through the Lens of Causality

no code implementations11 Jun 2021 Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, Kun Zhang

The spurious correlation implies that the adversarial distribution is constructed via making the statistical conditional association between style information and labels drastically different from that in natural distribution.

Adversarial Attack Adversarial Robustness

DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching

no code implementations9 Jun 2021 Kun Zhang, Guangyi Lv, Meng Wang, Enhong Chen

Then, we develop a Dynamic Gaussian Attention (DGA) to dynamically capture the important parts and corresponding local contexts from a detailed perspective.

Language Modelling Representation Learning

Progressive Open-Domain Response Generation with Multiple Controllable Attributes

no code implementations7 Jun 2021 Haiqin Yang, Xiaoyuan Yao, Yiqun Duan, Jianping Shen, Jie Zhong, Kun Zhang

More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage.

Graph-based Exercise- and Knowledge-Aware Learning Network for Student Performance Prediction

no code implementations1 Jun 2021 Mengfan Liu, Pengyang Shao, Kun Zhang

Predicting student performance is a fundamental task in Intelligent Tutoring Systems (ITSs), by which we can learn about students' knowledge level and provide personalized teaching strategies for them.

Collaborative Filtering

Privileged Graph Distillation for Cold Start Recommendation

no code implementations31 May 2021 Shuai Wang, Kun Zhang, Le Wu, Haiping Ma, Richang Hong, Meng Wang

The teacher model is composed of a heterogeneous graph structure for warm users and items with privileged CF links.

Collaborative Filtering Recommendation Systems

Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation

1 code implementation16 May 2021 Lei Chen, Le Wu, Kun Zhang, Richang Hong, Meng Wang

Despite the performance gain of these implicit feedback based models, the recommendation results are still far from satisfactory due to the sparsity of the observed item set for each user.

Collaborative Filtering

A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation

1 code implementation27 Apr 2021 Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang

Influenced by the great success of deep learning in computer vision and language understanding, research in recommendation has shifted to inventing new recommender models based on neural networks.

Collaborative Filtering Sequential Recommendation

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders

no code implementations26 Mar 2021 Wei Chen, Kun Zhang, Ruichu Cai, Biwei Huang, Joseph Ramsey, Zhifeng Hao, Clark Glymour

The first step of our method uses the FCI procedure, which allows confounders and is able to produce asymptotically correct results.

Causal Discovery

Probing black hole microstructure with the kinetic turnover of phase transition

no code implementations18 Feb 2021 Ran Li, Kun Zhang, Jin Wang

By treating black hole as the macroscopic stable state on the free energy landscape, we propose that the stochastic dynamics of the black hole phase transition can be effectively described by the Langevin equation or equivalently by the Fokker-Planck equation in phase space.

General Relativity and Quantum Cosmology

Towards advancing the earthquake forecasting by machine learning of satellite data

no code implementations31 Jan 2021 Pan Xiong, Lei Tong, Kun Zhang, Xuhui Shen, Roberto Battiston, Dimitar Ouzounov, Roberto Iuppa, Danny Crookes, Cheng Long, Huiyu Zhou

Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range.

Field-free spin-orbit torque-induced switching of perpendicular magnetization in a ferrimagnetic layer with vertical composition gradient

no code implementations21 Jan 2021 Zhenyi Zheng, Yue Zhang, Victor Lopez-Dominguez, Luis Sánchez-Tejerina, Jiacheng Shi, Xueqiang Feng, Lei Chen, Zilu Wang, Zhizhong Zhang, Kun Zhang, Bin Hong, Yong Xu, Youguang Zhang, Mario Carpentieri, Albert Fert, Giovanni Finocchio, Weisheng Zhao, Pedram Khalili Amiri

Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications.

Mesoscale and Nanoscale Physics

Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction

no code implementations20 Jan 2021 Gang Qu, Li Xiao, Wenxing Hu, Kun Zhang, Vince D. Calhoun, Yu-Ping Wang

Methods: To take advantage of complementary information from multi-modal fMRI, we propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions.

Graph Convolutional Network Graph Embedding +1

Score-based Causal Discovery from Heterogeneous Data

no code implementations1 Jan 2021 Chenwei Ding, Biwei Huang, Mingming Gong, Kun Zhang, Tongliang Liu, DaCheng Tao

Most algorithms in causal discovery consider a single domain with a fixed distribution.

Causal Discovery

Minimal Geometry-Distortion Constraint for Unsupervised Image-to-Image Translation

no code implementations1 Jan 2021 Jiaxian Guo, Jiachen Li, Mingming Gong, Huan Fu, Kun Zhang, DaCheng Tao

Unsupervised image-to-image (I2I) translation, which aims to learn a domain mapping function without paired data, is very challenging because the function is highly under-constrained.

Translation Unsupervised Image-To-Image Translation

Attainability and Optimality: The Equalized-Odds Fairness Revisited

no code implementations1 Jan 2021 Zeyu Tang, Kun Zhang

In this paper, focusing on the Equalized Odds notion of fairness, we consider the attainability of this criterion, and furthermore, if attainable, the optimality of the prediction performance under various settings.

Fairness

Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior

no code implementations1 Jan 2021 Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven Hoi

Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.

Meta-Learning

Learning Disentangled Semantic Representation for Domain Adaptation

no code implementations22 Dec 2020 Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao

Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data.

Domain Adaptation

R$^2$-Net: Relation of Relation Learning Network for Sentence Semantic Matching

no code implementations16 Dec 2020 Kun Zhang, Le Wu, Guangyi Lv, Meng Wang, Enhong Chen, Shulan Ruan

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences.

Relation Classification

Learning Heatmap-Style Jigsaw Puzzles Provides Good Pretraining for 2D Human Pose Estimation

no code implementations13 Dec 2020 Kun Zhang, Rui Wu, Ping Yao, Kai Deng, Ding Li, Renbiao Liu, Chuanguang Yang, Ge Chen, Min Du, Tianyao Zheng

We note that 2D pose estimation task is highly dependent on the contextual relationship between image patches, thus we introduce a self-supervised method for pretraining 2D pose estimation networks.

Pose Estimation

How Do Fair Decisions Fare in Long-term Qualification?

1 code implementation NeurIPS 2020 Xueru Zhang, Ruibo Tu, Yang Liu, Mingyan Liu, Hedvig Kjellström, Kun Zhang, Cheng Zhang

Our results show that static fairness constraints can either promote equality or exacerbate disparity depending on the driving factor of qualification transitions and the effect of sensitive attributes on feature distributions.

Decision Making Fairness

Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs

no code implementations NeurIPS 2020 Feng Xie, Ruichu Cai, Biwei Huang, Clark Glymour, Zhifeng Hao, Kun Zhang

Despite its success in certain domains, most existing methods focus on causal relations between observed variables, while in many scenarios the observed ones may not be the underlying causal variables (e. g., image pixels), but are generated by latent causal variables or confounders that are causally related.

Causal Discovery

Relevance Attack on Detectors

1 code implementation16 Aug 2020 Sizhe Chen, Fan He, Xiaolin Huang, Kun Zhang

This paper focuses on high-transferable adversarial attacks on detectors, which are hard to attack in a black-box manner, because of their multiple-output characteristics and the diversity across architectures.

Autonomous Driving Instance Segmentation +2

Adaptive Task Sampling for Meta-Learning

no code implementations ECCV 2020 Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi

Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks.

General Classification Meta-Learning

On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

1 code implementation NeurIPS 2020 Ignavier Ng, AmirEmad Ghassami, Kun Zhang

Extensive experiments validate the effectiveness of our proposed method and show that the DAG-penalized likelihood objective is indeed favorable over the least squares one with the hard DAG constraint.

A Causal View on Robustness of Neural Networks

no code implementations3 May 2020 Cheng Zhang, Kun Zhang, Yingzhen Li

We present a causal view on the robustness of neural networks against input manipulations, which applies not only to traditional classification tasks but also to general measurement data.

Data Augmentation

Learning from Positive and Unlabeled Data by Identifying the Annotation Process

no code implementations2 Mar 2020 Naji Shajarisales, Peter Spirtes, Kun Zhang

Yet the annotation process is an important part of the data collection, and in many cases it naturally depends on certain features of the data (e. g., the intensity of an image and the size of the object to be detected in the image).

General Classification

Domain Adaptation as a Problem of Inference on Graphical Models

1 code implementation NeurIPS 2020 Kun Zhang, Mingming Gong, Petar Stojanov, Biwei Huang, Qingsong Liu, Clark Glymour

Such a graphical model distinguishes between constant and varied modules of the distribution and specifies the properties of the changes across domains, which serves as prior knowledge of the changing modules for the purpose of deriving the posterior of the target variable $Y$ in the target domain.

Bayesian Inference Unsupervised Domain Adaptation

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

2 code implementations28 Jan 2020 Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang

Second, we propose a residual network structure that is specifically designed for CF with user-item interaction modeling, which alleviates the over smoothing problem in graph convolution aggregation operation with sparse user-item interaction data.

Collaborative Filtering Graph Convolutional Network +2

Deep Technology Tracing for High-tech Companies

no code implementations2 Jan 2020 Han Wu, Kun Zhang, Guangyi Lv, Qi Liu, Runlong Yu, Weihao Zhao, Enhong Chen, Jianhui Ma

Technological change and innovation are vitally important, especially for high-tech companies.

Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs

no code implementations12 Dec 2019 Menghan Wang, Kun Zhang, Gulin Li, Keping Yang, Luo Si

We generalize the propagation strategies of current GCNs as a \emph{"Sink$\to$Source"} mode, which seems to be an underlying cause of the two challenges.

Representation Learning

Transfer Learning-Based Outdoor Position Recovery with Telco Data

no code implementations10 Dec 2019 Yige Zhang, Aaron Yi Ding, Jorg Ott, Mingxuan Yuan, Jia Zeng, Kun Zhang, Weixiong Rao

In this paper, by leveraging the recently developed transfer learning techniques, we design a novel Telco position recovery framework, called TLoc, to transfer good models in the carefully selected source domains (those fine-grained small subareas) to a target one which originally suffers from poor localization accuracy.

Transfer Learning

Deep Physiological State Space Model for Clinical Forecasting

no code implementations4 Dec 2019 Yuan Xue, Denny Zhou, Nan Du, Andrew Dai, Zhen Xu, Kun Zhang, Claire Cui

Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements.

Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering

1 code implementation NeurIPS 2019 Biwei Huang, Kun Zhang, Pengtao Xie, Mingming Gong, Eric P. Xing, Clark Glymour

The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population.

Causal Discovery

Triad Constraints for Learning Causal Structure of Latent Variables

no code implementations NeurIPS 2019 Ruichu Cai, Feng Xie, Clark Glymour, Zhifeng Hao, Kun Zhang

In this paper, by properly leveraging the non-Gaussianity of the data, we propose to estimate the structure over latent variables with the so-called Triad constraints: we design a form of "pseudo-residual" from three variables, and show that when causal relations are linear and noise terms are non-Gaussian, the causal direction between the latent variables for the three observed variables is identifiable by checking a certain kind of independence relationship.

Twin Auxilary Classifiers GAN

1 code implementation NeurIPS 2019 Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich

One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN) that generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier.

Conditional Image Generation

Disentanglement Challenge: From Regularization to Reconstruction

no code implementations30 Nov 2019 Jie Qiao, Zijian Li, Boyan Xu, Ruichu Cai, Kun Zhang

The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition using a new real world disentanglement dataset (Gondal et al., 2019).

Modelling EHR timeseries by restricting feature interaction

no code implementations14 Nov 2019 Kun Zhang, Yuan Xue, Gerardo Flores, Alvin Rajkomar, Claire Cui, Andrew M. Dai

Time series data are prevalent in electronic health records, mostly in the form of physiological parameters such as vital signs and lab tests.

Mortality Prediction Time Series

Characterizing Distribution Equivalence and Structure Learning for Cyclic and Acyclic Directed Graphs

1 code implementation ICML 2020 AmirEmad Ghassami, Alan Yang, Negar Kiyavash, Kun Zhang

The main approach to defining equivalence among acyclic directed causal graphical models is based on the conditional independence relationships in the distributions that the causal models can generate, in terms of the Markov equivalence.

Learning Enhanced Resolution-wise features for Human Pose Estimation

no code implementations11 Sep 2019 Kun Zhang, Peng He, Ping Yao, Ge Chen, Rui Wu, Min Du, Huimin Li, Li Fu, Tianyao Zheng

Specifically, RAM learns a group of weights to represent the different importance of feature maps across resolutions, and the GPR gradually merges every two feature maps from low to high resolutions to regress final human keypoint heatmaps.

GPR Keypoint Detection

Adversarial Orthogonal Regression: Two non-Linear Regressions for Causal Inference

no code implementations10 Sep 2019 M. Reza Heydari, Saber Salehkaleybar, Kun Zhang

We propose two nonlinear regression methods, named Adversarial Orthogonal Regression (AdOR) for additive noise models and Adversarial Orthogonal Structural Equation Model (AdOSE) for the general case of structural equation models.

Causal Inference

Gated Convolutional Networks with Hybrid Connectivity for Image Classification

1 code implementation26 Aug 2019 Chuanguang Yang, Zhulin An, Hui Zhu, Xiaolong Hu, Kun Zhang, Kaiqiang Xu, Chao Li, Yongjun Xu

We propose a simple yet effective method to reduce the redundancy of DenseNet by substantially decreasing the number of stacked modules by replacing the original bottleneck by our SMG module, which is augmented by local residual.

Adversarial Defense General Classification +1

Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variables

no code implementations11 Aug 2019 Saber Salehkaleybar, AmirEmad Ghassami, Negar Kiyavash, Kun Zhang

It can be shown that causal effects among observed variables cannot be identified uniquely even under the assumptions of faithfulness and non-Gaussianity of exogenous noises.

Identification of Effective Connectivity Subregions

no code implementations8 Aug 2019 Ruben Sanchez-Romero, Joseph D. Ramsey, Kun Zhang, Clark Glymour

These algorithms allow for identification of subregions of voxels driving the connectivity between regions of interest, recovering valuable anatomical and functional information that is lost when ROIs are aggregated.

Hippocampus Time Series

Domain Generalization via Multidomain Discriminant Analysis

no code implementations25 Jul 2019 Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan

Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains.

Domain Generalization Learning Theory

Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach

no code implementations16 Jul 2019 Yipeng Mou, Mingming Gong, Huan Fu, Kayhan Batmanghelich, Kun Zhang, DaCheng Tao

Due to the stylish difference between synthetic and real images, we propose a temporally-consistent domain adaptation (TCDA) approach that simultaneously explores labels in the synthetic domain and temporal constraints in the videos to improve style transfer and depth prediction.

Domain Adaptation Monocular Depth Estimation +3

Twin Auxiliary Classifiers GAN

4 code implementations5 Jul 2019 Mingming Gong, Yanwu Xu, Chunyuan Li, Kun Zhang, Kayhan Batmanghelich

One of the popular conditional models is Auxiliary Classifier GAN (AC-GAN), which generates highly discriminative images by extending the loss function of GAN with an auxiliary classifier.

Conditional Image Generation

Neural News Recommendation with Long- and Short-term User Representations

1 code implementation ACL 2019 Mingxiao An, Fangzhao Wu, Chuhan Wu, Kun Zhang, Zheng Liu, Xing Xie

In this paper, we propose a neural news recommendation approach which can learn both long- and short-term user representations.

News Recommendation

Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models

no code implementations26 May 2019 Biwei Huang, Kun Zhang, Mingming Gong, Clark Glymour

In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments.

Bayesian Inference Causal Discovery +1

Causal Discovery with Cascade Nonlinear Additive Noise Models

2 code implementations23 May 2019 Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao

In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect.

Causal Discovery

Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA

no code implementations19 Apr 2019 Ricardo Pio Monti, Kun Zhang, Aapo Hyvarinen

We consider the problem of inferring causal relationships between two or more passively observed variables.

Causal Discovery

Generative-Discriminative Complementary Learning

no code implementations2 Apr 2019 Yanwu Xu, Mingming Gong, Junxiang Chen, Tongliang Liu, Kun Zhang, Kayhan Batmanghelich

The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases.

Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changes

no code implementations5 Mar 2019 Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, Bernhard Schölkopf

In this paper, we develop a framework for causal discovery from such data, called Constraint-based causal Discovery from heterogeneous/NOnstationary Data (CD-NOD), to find causal skeleton and directions and estimate the properties of mechanism changes.

Causal Discovery

On Learning Invariant Representation for Domain Adaptation

2 code implementations27 Jan 2019 Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon

Our result characterizes a fundamental tradeoff between learning invariant representations and achieving small joint error on both domains when the marginal label distributions differ from source to target.

Representation Learning Unsupervised Domain Adaptation

Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data

no code implementations27 Jan 2019 Biwei Huang, Kun Zhang, Ruben Sanchez-Romero, Joseph Ramsey, Madelyn Glymour, Clark Glymour

A substantial body of researches use Pearson's correlation coefficients, mutual information, or partial correlation to investigate the differences in brain connectivities between ASD and typical controls from functional Magnetic Resonance Imaging (fMRI).

Causal Discovery Feature Selection

Causal Discovery from Discrete Data using Hidden Compact Representation

no code implementations NeurIPS 2018 Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao

In this paper we make an attempt to find a way to solve this problem by assuming a two-stage causal process: the first stage maps the cause to a hidden variable of a lower cardinality, and the second stage generates the effect from the hidden representation.

Causal Discovery

Modeling Dynamic Missingness of Implicit Feedback for Recommendation

no code implementations NeurIPS 2018 Menghan Wang, Mingming Gong, Xiaolin Zheng, Kun Zhang

Recent studies modeled \emph{exposure}, a latent missingness variable which indicates whether an item is missing to a user, to give each missing entry a confidence of being negative feedback.

Collaborative Filtering Recommendation Systems

Multi-domain Causal Structure Learning in Linear Systems

no code implementations NeurIPS 2018 Amiremad Ghassami, Negar Kiyavash, Biwei Huang, Kun Zhang

We study the problem of causal structure learning in linear systems from observational data given in multiple domains, across which the causal coefficients and/or the distribution of the exogenous noises may vary.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

Random Occlusion-recovery for Person Re-identification

no code implementations26 Sep 2018 Di Wu, Kun Zhang, Fei Cheng, Yang Zhao, Qi Liu, Chang-An Yuan, De-Shuang Huang

As a basic task of multi-camera surveillance system, person re-identification aims to re-identify a query pedestrian observed from non-overlapping multiple cameras or across different time with a single camera.

Person Re-Identification

Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping

no code implementations CVPR 2019 Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, Kun Zhang, DaCheng Tao

Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples.

Deep Domain Generalization via Conditional Invariant Adversarial Networks

no code implementations ECCV 2018 Ya Li, Xinmei Tian, Mingming Gong, Yajing Liu, Tongliang Liu, Kun Zhang, DaCheng Tao

Under the assumption that the conditional distribution $P(Y|X)$ remains unchanged across domains, earlier approaches to domain generalization learned the invariant representation $T(X)$ by minimizing the discrepancy of the marginal distribution $P(T(X))$.

Domain Generalization Representation Learning

Causal Discovery in the Presence of Missing Data

1 code implementation11 Jul 2018 Ruibo Tu, Kun Zhang, Paul Ackermann, Bo Christer Bertilson, Clark Glymour, Hedvig Kjellström, Cheng Zhang

When these data entries are not missing completely at random, the (conditional) independence relations in the observed data may be different from those in the complete data generated by the underlying causal process.

Causal Discovery

Causal Generative Domain Adaptation Networks

no code implementations12 Apr 2018 Mingming Gong, Kun Zhang, Biwei Huang, Clark Glymour, DaCheng Tao, Kayhan Batmanghelich

For this purpose, we first propose a flexible Generative Domain Adaptation Network (G-DAN) with specific latent variables to capture changes in the generating process of features across domains.

Domain Adaptation

Counting and Sampling from Markov Equivalent DAGs Using Clique Trees

no code implementations5 Feb 2018 AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang

In this paper, we propose a new technique for counting the number of DAGs in a Markov equivalence class.

Causal Inference

Collaborative Filtering with Social Exposure: A Modular Approach to Social Recommendation

no code implementations30 Nov 2017 Menghan Wang, Xiaolin Zheng, Yang Yang, Kun Zhang

We assume that people get information of products from their online friends and they do not have to share similar preferences, which is less restrictive and seems closer to reality.

Collaborative Filtering Recommendation Systems

Causality Refined Diagnostic Prediction

no code implementations29 Nov 2017 Marcus Klasson, Kun Zhang, Bo C. Bertilson, Cheng Zhang, Hedvig Kjellström

In this work, we explore the possibility of utilizing causal relationships to refine diagnostic prediction.

Causal Identification Decision Making

Transfer Learning with Label Noise

no code implementations31 Jul 2017 Xiyu Yu, Tongliang Liu, Mingming Gong, Kun Zhang, Kayhan Batmanghelich, DaCheng Tao

However, when learning this invariant knowledge, existing methods assume that the labels in source domain are uncontaminated, while in reality, we often have access to source data with noisy labels.

Denoising Transfer Learning

Causal Discovery in the Presence of Measurement Error: Identifiability Conditions

no code implementations10 Jun 2017 Kun Zhang, Mingming Gong, Joseph Ramsey, Kayhan Batmanghelich, Peter Spirtes, Clark Glymour

This problem has received much attention in multiple fields, but it is not clear to what extent the causal model for the measurement-error-free variables can be identified in the presence of measurement error with unknown variance.

Causal Discovery

Learning Causal Structures Using Regression Invariance

no code implementations NeurIPS 2017 AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, Kun Zhang

We study causal inference in a multi-environment setting, in which the functional relations for producing the variables from their direct causes remain the same across environments, while the distribution of exogenous noises may vary.

Causal Inference

A New Measure of Conditional Dependence

no code implementations31 Mar 2017 Jalal Etesami, Kun Zhang, Negar Kiyavash

Measuring conditional dependencies among the variables of a network is of great interest to many disciplines.

Learning Vector Autoregressive Models with Latent Processes

no code implementations27 Feb 2017 Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang

We show that the support of transition matrix among the observed processes and lengths of all latent paths between any two observed processes can be identified successfully under some conditions on the VAR model.

Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery

no code implementations13 Feb 2017 Eric V. Strobl, Kun Zhang, Shyam Visweswaran

Constraint-based causal discovery (CCD) algorithms require fast and accurate conditional independence (CI) testing.

Causal Discovery

Learning Network of Multivariate Hawkes Processes: A Time Series Approach

no code implementations14 Mar 2016 Jalal Etesami, Negar Kiyavash, Kun Zhang, Kushagra Singhal

This paper studies the problem of recovering the causal structure in network of multivariate linear Hawkes processes.

Time Series

Distinguishing Cause from Effect Based on Exogeneity

no code implementations22 Apr 2015 Kun Zhang, Jiji Zhang, Bernhard Schölkopf

Recent developments in structural equation modeling have produced several methods that can usually distinguish cause from effect in the two-variable case.

Causal Inference

Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

no code implementations14 Nov 2014 Philipp Geiger, Kun Zhang, Mingming Gong, Dominik Janzing, Bernhard Schölkopf

A widely applied approach to causal inference from a non-experimental time series $X$, often referred to as "(linear) Granger causal analysis", is to regress present on past and interpret the regression matrix $\hat{B}$ causally.

Causal Inference Time Series

Bridging Information Criteria and Parameter Shrinkage for Model Selection

no code implementations8 Jul 2013 Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvarinen

Model selection based on classical information criteria, such as BIC, is generally computationally demanding, but its properties are well studied.

Model Selection

Causal discovery with scale-mixture model for spatiotemporal variance dependencies

no code implementations NeurIPS 2012 Zhitang Chen, Kun Zhang, Laiwan Chan

In conventional causal discovery, structural equation models (SEM) are directly applied to the observed variables, meaning that the causal effect can be represented as a function of the direct causes themselves.

Causal Discovery

On Causal and Anticausal Learning

1 code implementation27 Jun 2012 Bernhard Schoelkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, Joris Mooij

We consider the problem of function estimation in the case where an underlying causal model can be inferred.

Transfer Learning

Kernel-based Conditional Independence Test and Application in Causal Discovery

1 code implementation14 Feb 2012 Kun Zhang, Jonas Peters, Dominik Janzing, Bernhard Schoelkopf

Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.

Causal Discovery

Probabilistic latent variable models for distinguishing between cause and effect

no code implementations NeurIPS 2010 Oliver Stegle, Dominik Janzing, Kun Zhang, Joris M. Mooij, Bernhard Schölkopf

To this end, we consider the hypothetical effect variable to be a function of the hypothetical cause variable and an independent noise term (not necessarily additive).

Model Selection

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