Search Results for author: Shengyu Zhu

Found 19 papers, 9 papers with code

RIS-Assisted Joint Uplink Communication and Imaging: Phase Optimization and Bayesian Echo Decoupling

no code implementations10 Jan 2023 Shengyu Zhu, Zehua Yu, Qinghua Guo, Jinshan Ding, Qiang Cheng, Tie Jun Cui

Achieving integrated sensing and communication (ISAC) via uplink transmission is challenging due to the unknown waveform and the coupling of communication and sensing echoes.

Reframed GES with a Neural Conditional Dependence Measure

1 code implementation17 Jun 2022 Xinwei Shen, Shengyu Zhu, Jiji Zhang, Shoubo Hu, Zhitang Chen

In this paper, we revisit the Greedy Equivalence Search (GES) algorithm, which is widely cited as a score-based algorithm for learning the MEC of the underlying causal structure.

Causal Discovery Causal Inference

Out-of-distribution Generalization with Causal Invariant Transformations

no code implementations CVPR 2022 Ruoyu Wang, Mingyang Yi, Zhitang Chen, Shengyu Zhu

In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature.

Out-of-Distribution Generalization

ZIN: When and How to Learn Invariance Without Environment Partition?

1 code implementation11 Mar 2022 Yong Lin, Shengyu Zhu, Lu Tan, Peng Cui

When data are divided into distinct environments according to the heterogeneity, recent invariant learning methods have proposed to learn robust and invariant models based on this environment partition.

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

1 code implementation23 Feb 2022 Yan Lyu, Sunhao Dai, Peng Wu, Quanyu Dai, yuhao deng, Wenjie Hu, Zhenhua Dong, Jun Xu, Shengyu Zhu, Xiao-Hua Zhou

To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios.

Causal Inference Descriptive +2

Universality of parametric Coupling Flows over parametric diffeomorphisms

no code implementations7 Feb 2022 Junlong Lyu, Zhitang Chen, Chang Feng, Wenjing Cun, Shengyu Zhu, Yanhui Geng, Zhijie Xu, Yongwei Chen

Invertible neural networks based on Coupling Flows CFlows) have various applications such as image synthesis and data compression.

Bayesian Optimization Data Compression +1

Improving OOD Generalization with Causal Invariant Transformations

no code implementations29 Sep 2021 Ruoyu Wang, Mingyang Yi, Shengyu Zhu, Zhitang Chen

In this work, we obviate these assumptions and tackle the OOD problem without explicitly recovering the causal feature.

Ordering-Based Causal Discovery with Reinforcement Learning

1 code implementation14 May 2021 Xiaoqiang Wang, Yali Du, Shengyu Zhu, Liangjun Ke, Zhitang Chen, Jianye Hao, Jun Wang

It is a long-standing question to discover causal relations among a set of variables in many empirical sciences.

Causal Discovery reinforcement-learning +2

A Local Method for Identifying Causal Relations under Markov Equivalence

no code implementations25 Feb 2021 Zhuangyan Fang, Yue Liu, Zhi Geng, Shengyu Zhu, Yangbo He

We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs).

On Low Rank Directed Acyclic Graphs and Causal Structure Learning

no code implementations10 Jun 2020 Zhuangyan Fang, Shengyu Zhu, Jiji Zhang, Yue Liu, Zhitang Chen, Yangbo He

Despite several advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.

A Graph Autoencoder Approach to Causal Structure Learning

3 code implementations18 Nov 2019 Ignavier Ng, Shengyu Zhu, Zhitang Chen, Zhuangyan Fang

Causal structure learning has been a challenging task in the past decades and several mainstream approaches such as constraint- and score-based methods have been studied with theoretical guarantees.

Causal Discovery by Kernel Intrinsic Invariance Measure

no code implementations2 Sep 2019 Zhitang Chen, Shengyu Zhu, Yue Liu, Tim Tse

We show our algorithm can be reduced to an eigen-decomposition task on a kernel matrix measuring intrinsic deviance/invariance.

Causal Discovery

Asymptotically Optimal One- and Two-Sample Testing with Kernels

no code implementations27 Aug 2019 Shengyu Zhu, Biao Chen, Zhitang Chen, Pengfei Yang

With Sanov's theorem, we derive a sufficient condition for one-sample tests to achieve the optimal error exponent in the universal setting, i. e., for any distribution defining the alternative hypothesis.

Change Detection Test +2

Causal Discovery with Reinforcement Learning

1 code implementation ICLR 2020 Shengyu Zhu, Ignavier Ng, Zhitang Chen

The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.

Causal Discovery Combinatorial Optimization +2

Exponentially Consistent Kernel Two-Sample Tests

no code implementations23 Feb 2018 Shengyu Zhu, Biao Chen, Zhitang Chen

Given two sets of independent samples from unknown distributions $P$ and $Q$, a two-sample test decides whether to reject the null hypothesis that $P=Q$.

Change Detection Test +1

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

no code implementations21 Feb 2018 Shengyu Zhu, Biao Chen, Pengfei Yang, Zhitang Chen

We show that two classes of Maximum Mean Discrepancy (MMD) based tests attain this optimality on $\mathbb R^d$, while the quadratic-time Kernel Stein Discrepancy (KSD) based tests achieve the maximum exponential decay rate under a relaxed level constraint.

Test Two-sample testing

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