
Modelling EHR timeseries by restricting feature interaction
Time series data are prevalent in electronic health records, mostly in t...
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Domain Adaptation As a Problem of Inference on Graphical Models
This paper is concerned with datadriven unsupervised domain adaptation,...
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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...
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Transfer LearningBased Outdoor Position Recovery with Telco Data
Telecommunication (Telco) outdoor position recovery aims to localize out...
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Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach
Graph Convolutional Networks (GCNs) are stateoftheart graph based rep...
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Causal discovery in the presence of missing data
Missing data are ubiquitous in many domains such as healthcare. Dependin...
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Domain Generalization via Multidomain Discriminant Analysis
Domain generalization (DG) aims to incorporate knowledge from multiple s...
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GeometryConsistent Adversarial Networks for OneSided Unsupervised Domain Mapping
Unsupervised domain mapping aims at learning a function to translate dom...
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Causal Discovery with Cascade Nonlinear Additive Noise Models
Identification of causal direction between a causaleffect pair from obs...
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Twin Auxiliary Classifiers GAN
Conditional generative models enjoy remarkable progress over the past fe...
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Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation
Discovery of causal relations from observational data is essential for m...
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Learning Depth from Monocular Videos Using Synthetic Data: A TemporallyConsistent Domain Adaptation Approach
Majority of stateoftheart monocular depth estimation methods are supe...
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Causality Refined Diagnostic Prediction
Applying machine learning in the health care domain has shown promising ...
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Transfer Learning with Label Noise
Transfer learning aims to improve learning in the target domain with lim...
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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...
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Learning Causal Structures Using Regression Invariance
We study causal inference in a multienvironment setting, in which the f...
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A New Measure of Conditional Dependence
Measuring conditional dependencies among the variables of a network is o...
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Learning Vector Autoregressive Models with Latent Processes
We study the problem of learning the support of transition matrix betwee...
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Approximate Kernelbased Conditional Independence Tests for Fast NonParametric Causal Discovery
Constraintbased causal discovery (CCD) algorithms require fast and accu...
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Discovery and Visualization of Nonstationary Causal Models
It is commonplace to encounter nonstationary data, of which the underlyi...
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Distinguishing Cause from Effect Based on Exogeneity
Recent developments in structural equation modeling have produced severa...
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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...
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Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
A widely applied approach to causal inference from a nonexperimental ti...
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Bridging Information Criteria and Parameter Shrinkage for Model Selection
Model selection based on classical information criteria, such as BIC, is...
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Model Selection for Gaussian Mixture Models
This paper is concerned with an important issue in finite mixture modell...
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On Causal and Anticausal Learning
We consider the problem of function estimation in the case where an unde...
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On the Identifiability of the PostNonlinear Causal Model
By taking into account the nonlinear effect of the cause, the inner nois...
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Inferring deterministic causal relations
We consider two variables that are related to each other by an invertibl...
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Source Separation and HigherOrder Causal Analysis of MEG and EEG
Separation of the sources and analysis of their connectivity have been a...
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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...
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Kernelbased Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in ...
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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...
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Robust Learning via CauseEffect Models
We consider the problem of function estimation in the case where the dat...
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Scalable and accurate deep learning for electronic health records
Predictive modeling with electronic health record (EHR) data is anticipa...
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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...
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Causal Generative Domain Adaptation Networks
We propose a new generative model for domain adaptation, in which traini...
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UserSensitive Recommendation Ensemble with Clustered MultiTask Learning
This paper considers recommendation algorithm ensembles in a usersensit...
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Random Occlusionrecovery for Person Reidentification
As a basic task of multicamera surveillance system, person reidentific...
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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...
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On Learning Invariant Representation for Domain Adaptation
Due to the ability of deep neural nets to learn rich representations, re...
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Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from RestingState fMRI Data
Autism spectrum disorder (ASD) is one of the major developmental disorde...
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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...
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GenerativeDiscriminative Complementary Learning
Majority of stateoftheart deep learning methods for vision applicatio...
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Causal Discovery with General NonLinear Relationships Using NonLinear ICA
We consider the problem of inferring causal relationships between two or...
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Causal Discovery and Forecasting in Nonstationary Environments with StateSpace Models
In many scientific fields, such as economics and neuroscience, we are of...
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Identification of Effective Connectivity Subregions
Standard fMRI connectivity analyses depend on aggregating the time serie...
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Learning Linear NonGaussian Causal Models in the Presence of Latent Variables
We consider the problem of learning causal models from observational dat...
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LikelihoodFree Overcomplete ICA and Applications in Causal Discovery
Causal discovery witnessed significant progress over the past decades. I...
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Adversarial Orthogonal Regression: Two nonLinear Regressions for Causal Inference
We propose two nonlinear regression methods, named Adversarial Orthogona...
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DNANet: DeNormalized Attention Based MultiResolution Network for Human Pose Estimation
Recently, multiresolution networks (such as Hourglass, CPN, HRNet, etc....
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Kun Zhang
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Senior Research Scientist at Max Planck Institute for Intelligent Systems