Kun Zhang

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Max Planck Society ∙ Carnegie Mellon University ∙ USTC

Senior Research Scientist at Max Planck Institute for Intelligent Systems

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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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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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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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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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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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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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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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 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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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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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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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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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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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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Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components

A widely applied approach to causal inference from a non-experimental ti...
11/14/2014 ∙ by Philipp Geiger, et al. ∙ 0 ∙ share

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Bridging Information Criteria and Parameter Shrinkage for Model Selection

Model selection based on classical information criteria, such as BIC, is...
07/08/2013 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Model Selection for Gaussian Mixture Models

This paper is concerned with an important issue in finite mixture modell...
01/16/2013 ∙ by Tao Huang, et al. ∙ 0 ∙ share

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On Causal and Anticausal Learning

We consider the problem of function estimation in the case where an unde...
06/27/2012 ∙ by Bernhard Schoelkopf, et al. ∙ 0 ∙ share

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On the Identifiability of the Post-Nonlinear Causal Model

By taking into account the nonlinear effect of the cause, the inner nois...
05/09/2012 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Inferring deterministic causal relations

We consider two variables that are related to each other by an invertibl...
03/15/2012 ∙ by Povilas Daniusis, et al. ∙ 0 ∙ share

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Source Separation and Higher-Order Causal Analysis of MEG and EEG

Separation of the sources and analysis of their connectivity have been a...
03/15/2012 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Invariant Gaussian Process Latent Variable Models and Application in Causal Discovery

In nonlinear latent variable models or dynamic models, if we consider th...
03/15/2012 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Kernel-based Conditional Independence Test and Application in Causal Discovery

Conditional independence testing is an important problem, especially in ...
02/14/2012 ∙ by Kun Zhang, et al. ∙ 0 ∙ share

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Testing whether linear equations are causal: A free probability theory approach

We propose a method that infers whether linear relations between two hig...
02/14/2012 ∙ by Jakob Zscheischler, et al. ∙ 0 ∙ share

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Robust Learning via Cause-Effect Models

We consider the problem of function estimation in the case where the dat...
12/12/2011 ∙ by Bernhard Schölkopf, 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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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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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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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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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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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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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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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 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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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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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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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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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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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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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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Kun Zhang

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