Kun Zhang

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Max Planck Society ∙ Carnegie Mellon University ∙ USTC

Senior Research Scientist at Max Planck Institute for Intelligent Systems

How Do Fair Decisions Fare in Long-term Qualification?

Although many fairness criteria have been proposed for decision making, ...
10/21/2020 ∙ by Xueru Zhang, et al. ∙ 0 ∙ share

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Generalized Independent Noise Condition for Estimating Linear Non-Gaussian Latent Variable Graphs

Causal discovery aims to recover causal structures or models underlying ...
10/10/2020 ∙ by Feng Xie, et al. ∙ 4 ∙ share

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Attack on Multi-Node Attention for Object Detection

This paper focuses on high-transferable adversarial attacks on detection...
08/16/2020 ∙ by Sizhe Chen, et al. ∙ 74 ∙ share

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Adaptive Task Sampling for Meta-Learning

Meta-learning methods have been extensively studied and applied in compu...
07/17/2020 ∙ by Chenghao Liu, et al. ∙ 5 ∙ share

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

Learning graphical structure based on Directed Acyclic Graphs (DAGs) is ...
06/17/2020 ∙ by Ignavier Ng, et al. ∙ 0 ∙ share

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Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach

In many recommender systems, users and items are associated with attribu...
05/25/2020 ∙ by Le Wu, et al. ∙ 0 ∙ share

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A Causal View on Robustness of Neural Networks

We present a causal view on the robustness of neural networks against in...
05/03/2020 ∙ by Cheng Zhang, et al. ∙ 22 ∙ share

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Learning from Positive and Unlabeled Data by Identifying the Annotation Process

In binary classification, Learning from Positive and Unlabeled data (LeP...
03/02/2020 ∙ by Naji Shajarisales, et al. ∙ 20 ∙ share

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Domain Adaptation As a Problem of Inference on Graphical Models

This paper is concerned with data-driven unsupervised domain adaptation,...
02/09/2020 ∙ by Kun Zhang, et al. ∙ 20 ∙ share

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

Graph Convolutional Networks (GCNs) are state-of-the-art graph based rep...
01/28/2020 ∙ by Lei Chen, et al. ∙ 8 ∙ share

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

Graph Convolutional Networks (GCNs) have gained significant developments...
12/12/2019 ∙ by Menghan Wang, et al. ∙ 0 ∙ share

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Transfer Learning-Based Outdoor Position Recovery with Telco Data

Telecommunication (Telco) outdoor position recovery aims to localize out...
12/10/2019 ∙ by Yige Zhang, et al. ∙ 11 ∙ share

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Deep Physiological State Space Model for Clinical Forecasting

Clinical forecasting based on electronic medical records (EMR) can uncov...
12/04/2019 ∙ by Yuan Xue, et al. ∙ 0 ∙ share

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Disentanglement Challenge: From Regularization to Reconstruction

The challenge of learning disentangled representation has recently attra...
11/30/2019 ∙ by Jie Qiao, et al. ∙ 0 ∙ share

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Modelling EHR timeseries by restricting feature interaction

Time series data are prevalent in electronic health records, mostly in t...
11/14/2019 ∙ by Kun Zhang, et al. ∙ 21 ∙ share

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Characterizing Distribution Equivalence for Cyclic and Acyclic Directed Graphs

The main way for defining equivalence among acyclic directed graphs is b...
10/28/2019 ∙ by AmirEmad Ghassami, et al. ∙ 0 ∙ share

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DNANet: De-Normalized Attention Based Multi-Resolution Network for Human Pose Estimation

Recently, multi-resolution networks (such as Hourglass, CPN, HRNet, etc....
09/11/2019 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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

We propose two nonlinear regression methods, named Adversarial Orthogona...
09/10/2019 ∙ by M. Reza Heydari, et al. ∙ 0 ∙ share

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Likelihood-Free Overcomplete ICA and Applications in Causal Discovery

Causal discovery witnessed significant progress over the past decades. I...
09/04/2019 ∙ by Chenwei Ding, et al. ∙ 0 ∙ share

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

We consider the problem of learning causal models from observational dat...
08/11/2019 ∙ by Saber Salehkaleybar, et al. ∙ 0 ∙ share

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Identification of Effective Connectivity Subregions

Standard fMRI connectivity analyses depend on aggregating the time serie...
08/08/2019 ∙ by Ruben Sanchez-Romero, et al. ∙ 0 ∙ share

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Domain Generalization via Multidomain Discriminant Analysis

Domain generalization (DG) aims to incorporate knowledge from multiple s...
07/25/2019 ∙ by Shoubo Hu, et al. ∙ 5 ∙ share

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

Majority of state-of-the-art monocular depth estimation methods are supe...
07/16/2019 ∙ by Yipeng Mou, et al. ∙ 1 ∙ share

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Twin Auxiliary Classifiers GAN

Conditional generative models enjoy remarkable progress over the past fe...
07/05/2019 ∙ by Mingming Gong, et al. ∙ 2 ∙ share

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Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation

Discovery of causal relations from observational data is essential for m...
06/04/2019 ∙ by Ruibo Tu, et al. ∙ 1 ∙ share

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

In many scientific fields, such as economics and neuroscience, we are of...
05/26/2019 ∙ by Biwei Huang, et al. ∙ 0 ∙ share

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Causal Discovery with Cascade Nonlinear Additive Noise Models

Identification of causal direction between a causal-effect pair from obs...
05/23/2019 ∙ by Ruichu Cai, et al. ∙ 2 ∙ share

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

We consider the problem of inferring causal relationships between two or...
04/19/2019 ∙ by Ricardo Pio Monti, et al. ∙ 0 ∙ share

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Generative-Discriminative Complementary Learning

Majority of state-of-the-art deep learning methods for vision applicatio...
04/02/2019 ∙ by Yanwu Xu, et al. ∙ 0 ∙ share

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Causal Discovery and Hidden Driving Force Estimation from Nonstationary/Heterogeneous Data

It is commonplace to encounter nonstationary or heterogeneous data. Such...
03/05/2019 ∙ by Biwei Huang, et al. ∙ 0 ∙ share

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On Learning Invariant Representation for Domain Adaptation

Due to the ability of deep neural nets to learn rich representations, re...
01/27/2019 ∙ by Han Zhao, et al. ∙ 0 ∙ share

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

Autism spectrum disorder (ASD) is one of the major developmental disorde...
01/27/2019 ∙ by Biwei Huang, et al. ∙ 0 ∙ share

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

Gliomas are the most common primary brain malignancies, with different d...
11/05/2018 ∙ by Spyridon Bakas, et al. ∙ 0 ∙ share

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Random Occlusion-recovery for Person Re-identification

As a basic task of multi-camera surveillance system, person re-identific...
09/26/2018 ∙ by Di Wu, et al. ∙ 0 ∙ share

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

Unsupervised domain mapping aims at learning a function to translate dom...
09/16/2018 ∙ by Huan Fu, et al. ∙ 2 ∙ share

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Causal discovery in the presence of missing data

Missing data are ubiquitous in many domains such as healthcare. Dependin...
07/11/2018 ∙ by Ruibo Tu, et al. ∙ 6 ∙ share

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User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning

This paper considers recommendation algorithm ensembles in a user-sensit...
04/28/2018 ∙ by Menghan Wang, et al. ∙ 0 ∙ share

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Causal Generative Domain Adaptation Networks

We propose a new generative model for domain adaptation, in which traini...
04/12/2018 ∙ by Mingming Gong, et al. ∙ 0 ∙ share

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Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipa...
01/24/2018 ∙ by Alvin Rajkomar, et al. ∙ 0 ∙ share

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

This paper is concerned with how to make efficient use of social informa...
11/30/2017 ∙ by Menghan Wang, et al. ∙ 0 ∙ share

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Causality Refined Diagnostic Prediction

Applying machine learning in the health care domain has shown promising ...
11/29/2017 ∙ by Marcus Klasson, et al. ∙ 0 ∙ share

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Transfer Learning with Label Noise

Transfer learning aims to improve learning in the target domain with lim...
07/31/2017 ∙ by Xiyu Yu, et al. ∙ 0 ∙ share

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Causal Discovery in the Presence of Measurement Error: Identifiability Conditions

Measurement error in the observed values of the variables can greatly ch...
06/10/2017 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Learning Causal Structures Using Regression Invariance

We study causal inference in a multi-environment setting, in which the f...
05/26/2017 ∙ by AmirEmad Ghassami, et al. ∙ 0 ∙ share

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A New Measure of Conditional Dependence

Measuring conditional dependencies among the variables of a network is o...
03/31/2017 ∙ by Jalal Etesami, et al. ∙ 0 ∙ share

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Learning Vector Autoregressive Models with Latent Processes

We study the problem of learning the support of transition matrix betwee...
02/27/2017 ∙ by Saber Salehkaleybar, et al. ∙ 0 ∙ share

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

Constraint-based causal discovery (CCD) algorithms require fast and accu...
02/13/2017 ∙ by Eric V. Strobl, et al. ∙ 0 ∙ share

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Learning Network of Multivariate Hawkes Processes: A Time Series Approach

Learning the influence structure of multiple time series data is of grea...
03/14/2016 ∙ by Jalal Etesami, et al. ∙ 0 ∙ share

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Discovery and Visualization of Nonstationary Causal Models

It is commonplace to encounter nonstationary data, of which the underlyi...
09/27/2015 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Distinguishing Cause from Effect Based on Exogeneity

Recent developments in structural equation modeling have produced severa...
04/22/2015 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Kun Zhang

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