
Decoderfree Robustness Disentanglement without (Additional) Supervision
Adversarial Training (AT) is proposed to alleviate the adversarial vulne...
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Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several important advances in recent years, learning causal stru...
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Clustering Causal Additive Noise Models
Additive noise models are commonly used to infer the causal direction fo...
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CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Learning disentanglement aims at finding a low dimensional representatio...
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A Graph Autoencoder Approach to Causal Structure Learning
Causal structure learning has been a challenging task in the past decade...
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Masked GradientBased Causal Structure Learning
Learning causal graphical models based on directed acyclic graphs is an ...
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Causal Discovery by Kernel Intrinsic Invariance Measure
Reasoning based on causality, instead of association has been considered...
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Asymptotically Optimal One and TwoSample Testing with Kernels
We characterize the asymptotic performance of nonparametric one and two...
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Domain Generalization via Multidomain Discriminant Analysis
Domain generalization (DG) aims to incorporate knowledge from multiple s...
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Causal Discovery with Reinforcement Learning
Discovering causal structure among a set of variables is a fundamental p...
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Kernelbased MultiTask Contextual Bandits in Cellular Network Configuration
Cellular network configuration plays a critical role in network performa...
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Causal Inference and Mechanism Clustering of a Mixture of Additive Noise Models
The inference of the causal relationship between a pair of observed vari...
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A Kernel Embeddingbased Approach for Nonstationary Causal Model Inference
Although nonstationary data are more common in the real world, most exis...
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Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
Exogenous state variables and rewards can slow down reinforcement learni...
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Exponentially Consistent Kernel TwoSample Tests
Given two sets of independent samples from unknown distributions P and Q...
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Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit
We characterize the asymptotic performance of nonparametric goodness of ...
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Zhitang Chen
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