Search Results for author: Jie Qiao

Found 16 papers, 5 papers with code

Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis

no code implementations25 Mar 2024 Jie Qiao, Yu Xiang, Zhengming Chen, Ruichu Cai, Zhifeng Hao

Fortunately, in this work, we found that the causal order from $X$ to its child $Y$ is identifiable if $X$ is a root vertex and has at least two directed paths to $Y$, or the ancestor of $X$ with the most directed path to $X$ has a directed path to $Y$ without passing $X$.

Causal Discovery Epidemiology

Learning by Doing: An Online Causal Reinforcement Learning Framework with Causal-Aware Policy

no code implementations7 Feb 2024 Ruichu Cai, Siyang Huang, Jie Qiao, Wei Chen, Yan Zeng, Keli Zhang, Fuchun Sun, Yang Yu, Zhifeng Hao

As a key component to intuitive cognition and reasoning solutions in human intelligence, causal knowledge provides great potential for reinforcement learning (RL) agents' interpretability towards decision-making by helping reduce the searching space.

Decision Making Reinforcement Learning (RL)

Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examples

no code implementations21 Dec 2023 Ruichu Cai, Yuxuan Zhu, Jie Qiao, Zefeng Liang, Furui Liu, Zhifeng Hao

By considering the underappreciated causal generating process, first, we pinpoint the source of the vulnerability of DNNs via the lens of causality, then give theoretical results to answer \emph{where to attack}.

counterfactual

Identification of Causal Structure in the Presence of Missing Data with Additive Noise Model

no code implementations19 Dec 2023 Jie Qiao, Zhengming Chen, Jianhua Yu, Ruichu Cai, Zhifeng Hao

With this observation, we aim to investigate the identification problem of learning causal structure from missing data under an additive noise model with different missingness mechanisms, where the `no self-masking missingness' assumption can be eliminated appropriately.

Causal Discovery

Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences

1 code implementation10 May 2023 Jie Qiao, Ruichu Cai, Siyu Wu, Yu Xiang, Keli Zhang, Zhifeng Hao

Learning causal structure among event types from discrete-time event sequences is a particularly important but challenging task.

On the Probability of Necessity and Sufficiency of Explaining Graph Neural Networks: A Lower Bound Optimization Approach

no code implementations14 Dec 2022 Ruichu Cai, Yuxuan Zhu, Xuexin Chen, Yuan Fang, Min Wu, Jie Qiao, Zhifeng Hao

To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose a framework of Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound.

counterfactual

REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

1 code implementation13 Jan 2022 Ruichu Cai, Fengzhu Wu, Zijian Li, Jie Qiao, Wei Chen, Yuexing Hao, Hao Gu

By explicitly Reconstructing Exposure STrategies (REST in short), we formalize the recommendation problem as the counterfactual reasoning and propose the debiased social recommendation method.

counterfactual Counterfactual Reasoning +1

On the Role of Entropy-based Loss for Learning Causal Structures with Continuous Optimization

1 code implementation5 Jun 2021 Weilin Chen, Jie Qiao, Ruichu Cai, Zhifeng Hao

Causal discovery from observational data is an important but challenging task in many scientific fields.

Causal Discovery

THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequences

no code implementations23 May 2021 Ruichu Cai, Siyu Wu, Jie Qiao, Zhifeng Hao, Keli Zhang, Xi Zhang

We further propose a causal structure learning method on THP in a likelihood framework.

Learning Disentangled Semantic Representation for Domain Adaptation

1 code implementation22 Dec 2020 Ruichu Cai, Zijian Li, Pengfei Wei, Jie Qiao, Kun Zhang, Zhifeng Hao

Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data.

Domain Adaptation

Disentanglement Challenge: From Regularization to Reconstruction

no code implementations30 Nov 2019 Jie Qiao, Zijian Li, Boyan Xu, Ruichu Cai, Kun Zhang

The challenge of learning disentangled representation has recently attracted much attention and boils down to a competition using a new real world disentanglement dataset (Gondal et al., 2019).

Disentanglement

Causal Discovery with Cascade Nonlinear Additive Noise Models

2 code implementations23 May 2019 Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao

In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect.

Causal Discovery

Causal Discovery from Discrete Data using Hidden Compact Representation

no code implementations NeurIPS 2018 Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang, Zhifeng Hao

In this paper we make an attempt to find a way to solve this problem by assuming a two-stage causal process: the first stage maps the cause to a hidden variable of a lower cardinality, and the second stage generates the effect from the hidden representation.

Causal Discovery

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