Zhitang Chen | DeepAI

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∙ 01/30/2023

Reweighted Interacting Langevin Diffusions: an Accelerated Sampling Methodfor Optimization

We proposed a new technique to accelerate sampling methods for solving d...

0 Junlong Lyu, et al. ∙

∙ 12/29/2022

Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network

The mobile communication enabled by cellular networks is the one of the ...

0 Mehrtash Mehrabi, et al. ∙

∙ 06/17/2022

Reframed GES with a Neural Conditional Dependence Measure

In a nonparametric setting, the causal structure is often identifiable o...

0 Xinwei Shen, et al. ∙

∙ 03/22/2022

Out-of-distribution Generalization with Causal Invariant Transformations

In real-world applications, it is important and desirable to learn a mod...

0 Ruoyu Wang, et al. ∙

∙ 02/07/2022

Universality of parametric Coupling Flows over parametric diffeomorphisms

Invertible neural networks based on Coupling Flows CFlows) have various ...

0 Junlong Lyu, et al. ∙

∙ 12/23/2021

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

Machine learning is gaining growing momentum in various recent models fo...

0 Xiangle Cheng, et al. ∙

∙ 11/30/2021

gCastle: A Python Toolbox for Causal Discovery

is an end-to-end Python toolbox for causal structure learning. It provi...

0 Keli Zhang, et al. ∙

∙ 06/07/2021

High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

We introduce a method based on deep metric learning to perform Bayesian ...

37 Antoine Grosnit, et al. ∙

∙ 06/02/2021

Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms

Domain generalization aims to learn knowledge invariant across different...

0 Yunqi Wang, et al. ∙

∙ 05/14/2021

Ordering-Based Causal Discovery with Reinforcement Learning

It is a long-standing question to discover causal relations among a set ...

4 Xiaoqiang Wang, et al. ∙

∙ 12/28/2020

Causal World Models by Unsupervised Deconfounding of Physical Dynamics

The capability of imagining internally with a mental model of the world ...

9 Minne Li, et al. ∙

∙ 07/02/2020

Decoder-free Robustness Disentanglement without (Additional) Supervision

Adversarial Training (AT) is proposed to alleviate the adversarial vulne...

0 Yifei Wang, et al. ∙

∙ 06/10/2020

Low Rank Directed Acyclic Graphs and Causal Structure Learning

Despite several important advances in recent years, learning causal stru...

0 Zhuangyan Fang, et al. ∙

∙ 06/08/2020

Clustering Causal Additive Noise Models

Additive noise models are commonly used to infer the causal direction fo...

0 Vahid Partovi Nia, et al. ∙

∙ 04/18/2020

CausalVAE: Structured Causal Disentanglement in Variational Autoencoder

Learning disentanglement aims at finding a low dimensional representatio...

12 Mengyue Yang, et al. ∙

∙ 11/18/2019

A Graph Autoencoder Approach to Causal Structure Learning

Causal structure learning has been a challenging task in the past decade...

0 Ignavier Ng, et al. ∙

∙ 10/18/2019

Masked Gradient-Based Causal Structure Learning

Learning causal graphical models based on directed acyclic graphs is an ...

0 Ignavier Ng, et al. ∙

∙ 09/02/2019

Causal Discovery by Kernel Intrinsic Invariance Measure

Reasoning based on causality, instead of association has been considered...

0 Zhitang Chen, et al. ∙

∙ 08/27/2019

Asymptotically Optimal One- and Two-Sample Testing with Kernels

We characterize the asymptotic performance of nonparametric one- and two...

0 Shengyu Zhu, et al. ∙

∙ 07/25/2019

Domain Generalization via Multidomain Discriminant Analysis

Domain generalization (DG) aims to incorporate knowledge from multiple s...

5 Shoubo Hu, et al. ∙

∙ 06/11/2019

Causal Discovery with Reinforcement Learning

Discovering causal structure among a set of variables is a fundamental p...

0 Shengyu Zhu, et al. ∙

∙ 11/27/2018

Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration

Cellular network configuration plays a critical role in network performa...

0 Xiaoxiao Wang, et al. ∙

∙ 09/23/2018

Causal Inference and Mechanism Clustering of a Mixture of Additive Noise Models

The inference of the causal relationship between a pair of observed vari...

0 Shoubo Hu, et al. ∙

∙ 09/23/2018

A Kernel Embedding-based Approach for Nonstationary Causal Model Inference

Although nonstationary data are more common in the real world, most exis...

0 Shoubo Hu, et al. ∙

∙ 06/05/2018

Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning

Exogenous state variables and rewards can slow down reinforcement learni...

6 Thomas G. Dietterich, et al. ∙

∙ 02/23/2018

Exponentially Consistent Kernel Two-Sample Tests

Given two sets of independent samples from unknown distributions P and Q...

0 Shengyu Zhu, et al. ∙

∙ 02/21/2018

Universal Hypothesis Testing with Kernels: Asymptotically Optimal Tests for Goodness of Fit

We characterize the asymptotic performance of nonparametric goodness of ...

0 Shengyu Zhu, et al. ∙