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Kun Zhang

Researcher at Carnegie Mellon University

Publications -  244
Citations -  11098

Kun Zhang is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Causal model & Computer science. The author has an hindex of 38, co-authored 212 publications receiving 7472 citations. Previous affiliations of Kun Zhang include University of Southern California & Aalto University.

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

Spyridon Bakas, +438 more
- 05 Nov 2018 - 
TL;DR: This study assesses the state-of-the-art machine learning 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, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Proceedings Article

Domain Adaptation under Target and Conditional Shift

Kun Zhang, +3 more
TL;DR: This work considers domain adaptation under three possible scenarios, kernel embedding of conditional as well as marginal distributions, and proposes to estimate the weights or transformations by reweighting or transforming training data to reproduce the covariate distribution on the test domain.
Book ChapterDOI

Deep Domain Generalization via Conditional Invariant Adversarial Networks

Ya Li, +6 more
TL;DR: This work proposes an end-to-end conditional invariant deep domain generalization approach by leveraging deep neural networks for domain-invariant representation learning and proves the effectiveness of the proposed method.
Journal ArticleDOI

Review of Causal Discovery Methods Based on Graphical Models.

Clark Glymour, +2 more
- 04 Jun 2019 - 
TL;DR: This paper aims to give a introduction to and a brief review of the computational methods for causal discovery that were developed in the past three decades, including constraint-based and score-based methods and those based on functional causal models, supplemented by some illustrations and applications.
Journal ArticleDOI

Inferring causation from time series in Earth system sciences

Jakob Runge, +24 more
- 14 Jun 2019 - 
TL;DR: An overview of causal inference frameworks is given, promising applications and methodological challenges are identified, and a causality benchmark platform is initiated to close the gap between method users and developers.