
How Do Fair Decisions Fare in Longterm Qualification?
Although many fairness criteria have been proposed for decision making, ...
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Generalized Independent Noise Condition for Estimating Linear NonGaussian Latent Variable Graphs
Causal discovery aims to recover causal structures or models underlying ...
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Attack on MultiNode Attention for Object Detection
This paper focuses on hightransferable adversarial attacks on detection...
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Adaptive Task Sampling for MetaLearning
Metalearning methods have been extensively studied and applied in compu...
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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 ...
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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...
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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...
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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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Domain Adaptation As a Problem of Inference on Graphical Models
This paper is concerned with datadriven unsupervised domain adaptation,...
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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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Tracing the Propagation Path: A Flow Perspective of Representation Learning on Graphs
Graph Convolutional Networks (GCNs) have gained significant developments...
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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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Deep Physiological State Space Model for Clinical Forecasting
Clinical forecasting based on electronic medical records (EMR) can uncov...
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Disentanglement Challenge: From Regularization to Reconstruction
The challenge of learning disentangled representation has recently attra...
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Modelling EHR timeseries by restricting feature interaction
Time series data are prevalent in electronic health records, mostly in t...
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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...
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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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Adversarial Orthogonal Regression: Two nonLinear Regressions for Causal Inference
We propose two nonlinear regression methods, named Adversarial Orthogona...
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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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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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Identification of Effective Connectivity Subregions
Standard fMRI connectivity analyses depend on aggregating the time serie...
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Domain Generalization via Multidomain Discriminant Analysis
Domain generalization (DG) aims to incorporate knowledge from multiple s...
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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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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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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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Causal Discovery with Cascade Nonlinear Additive Noise Models
Identification of causal direction between a causaleffect pair from obs...
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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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GenerativeDiscriminative Complementary Learning
Majority of stateoftheart deep learning methods for vision applicatio...
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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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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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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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Random Occlusionrecovery for Person Reidentification
As a basic task of multicamera surveillance system, person reidentific...
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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 in the presence of missing data
Missing data are ubiquitous in many domains such as healthcare. Dependin...
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UserSensitive Recommendation Ensemble with Clustered MultiTask Learning
This paper considers recommendation algorithm ensembles in a usersensit...
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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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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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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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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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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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Kun Zhang
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Senior Research Scientist at Max Planck Institute for Intelligent Systems