Deep Domain Generalization via Conditional Invariant Adversarial Networks
- Ya Li, Xinmei Tian, D. Tao
- Computer ScienceEuropean Conference on Computer Vision
- 8 September 2018
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.
Kernel-based Conditional Independence Test and Application in Causal Discovery
- Kun Zhang, J. Peters, D. Janzing, B. Schölkopf
- Computer ScienceConference on Uncertainty in Artificial…
- 14 July 2011
A Kernel-based Conditional Independence test (KCI-test) is proposed, by constructing an appropriate test statistic and deriving its asymptotic distribution under the null hypothesis of conditional independence.
On Learning Invariant Representations for Domain Adaptation
- H. Zhao, Rémi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon
- Computer ScienceInternational Conference on Machine Learning
- 24 May 2019
This paper constructs a simple counterexample showing that, contrary to common belief, the above conditions are not sufficient to guarantee successful domain adaptation, and proposes a natural and interpretable generalization upper bound that explicitly takes into account the aforementioned shift.
Domain Adaptation under Target and Conditional Shift
- Kun Zhang, B. Schölkopf, Krikamol Muandet, Zhikun Wang
- Computer ScienceInternational Conference on Machine Learning
- 16 June 2013
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.
Information-geometric approach to inferring causal directions
- D. Janzing, J. Mooij, B. Schölkopf
- Computer ScienceArtificial Intelligence
- 1 May 2012
On causal and anticausal learning
- B. Schölkopf, D. Janzing, J. Peters, Eleni Sgouritsa, Kun Zhang, J. Mooij
- Computer ScienceInternational Conference on Machine Learning
- 26 June 2012
The problem of function estimation in the case where an underlying causal model can be inferred is considered, and a hypothesis for when semi-supervised learning can help is formulated, and corroborate it with empirical results.
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity
- Aapo Hyvärinen, Kun Zhang, Shohei Shimizu, P. Hoyer
- Computer Science, MathematicsJournal of machine learning research
- 1 March 2010
This work shows how to combine the non-Gaussian instantaneous model with autoregressive models, effectively what is called a structural vector autoregression (SVAR) model, and contributes to the long-standing problem of how to estimate SVAR's.
Review of Causal Discovery Methods Based on Graphical Models
- C. Glymour, Kun Zhang, P. Spirtes
- Computer ScienceFrontiers in Genetics
- 4 June 2019
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.
Multi-label learning by exploiting label dependency
- Min-Ling Zhang, Kun Zhang
- Computer ScienceKnowledge Discovery and Data Mining
- 25 July 2010
This paper proposes to use a Bayesian network structure to efficiently encode the conditional dependencies of the labels as well as the feature set, with the featureSet as the common parent of all labels.
Domain Adaptation with Conditional Transferable Components
- Mingming Gong, Kun Zhang, Tongliang Liu, D. Tao, C. Glymour, B. Schölkopf
- MathematicsInternational Conference on Machine Learning
- 1 June 2016
This paper aims to extract conditional transferable components whose conditional distribution is invariant after proper location-scale (LS) transformations, and identifies how P(Y) changes between domains simultaneously.
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