Search Results for author: Yujia Zheng

Found 19 papers, 6 papers with code

Local Causal Discovery with Linear non-Gaussian Cyclic Models

1 code implementation21 Mar 2024 Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang

Local causal discovery is of great practical significance, as there are often situations where the discovery of the global causal structure is unnecessary, and the interest lies solely on a single target variable.

Causal Discovery

Causal Representation Learning from Multiple Distributions: A General Setting

no code implementations7 Feb 2024 Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng

We show that under the sparsity constraint on the recovered graph over the latent variables and suitable sufficient change conditions on the causal influences, interestingly, one can recover the moralized graph of the underlying directed acyclic graph, and the recovered latent variables and their relations are related to the underlying causal model in a specific, nontrivial way.

Representation Learning

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables

no code implementations18 Dec 2023 Xinshuai Dong, Biwei Huang, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang

Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems.

Causal Discovery

A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education

no code implementations5 Dec 2023 Jacob Doughty, Zipiao Wan, Anishka Bompelli, Jubahed Qayum, Taozhi Wang, Juran Zhang, Yujia Zheng, Aidan Doyle, Pragnya Sridhar, Arav Agarwal, Christopher Bogart, Eric Keylor, Can Kultur, Jaromir Savelka, Majd Sakr

While there is a growing body of research in computing education on utilizing large language models (LLMs) in generation and engagement with coding exercises, the use of LLMs for generating programming MCQs has not been extensively explored.


Causal-learn: Causal Discovery in Python

1 code implementation31 Jul 2023 Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang

Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.

Causal Discovery

Partial Identifiability for Domain Adaptation

no code implementations10 Jun 2023 Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang

In general, without further assumptions, the joint distribution of the features and the label is not identifiable in the target domain.

Unsupervised Domain Adaptation

Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data

no code implementations28 May 2023 Mugariya Farooq, Shahad Hardan, Aigerim Zhumbhayeva, Yujia Zheng, Preslav Nakov, Kun Zhang

The need for more usable and explainable machine learning models in healthcare increases the importance of developing and utilizing causal discovery algorithms, which aim to discover causal relations by analyzing observational data.

Causal Discovery

Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks

no code implementations19 May 2023 Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang

A Markov network characterizes the conditional independence structure, or Markov property, among a set of random variables.

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

1 code implementation1 Nov 2022 Yue Yu, Xuan Kan, Hejie Cui, ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang

To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.

Time Series Time Series Analysis

Whole Page Unbiased Learning to Rank

no code implementations19 Oct 2022 Haitao Mao, Lixin Zou, Yujia Zheng, Jiliang Tang, Xiaokai Chu, Jiashu Zhao, Qian Wang, Dawei Yin

To address the above challenges, we propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, named BAL, to automatically find the user behavior model with causal discovery and mitigate the biases induced by multiple SERP features with no specific design.

Causal Discovery Information Retrieval +2

On the Identifiability of Nonlinear ICA: Sparsity and Beyond

no code implementations15 Jun 2022 Yujia Zheng, Ignavier Ng, Kun Zhang

We show that under specific instantiations of such constraints, the independent latent sources can be identified from their nonlinear mixtures up to a permutation and a component-wise transformation, thus achieving nontrivial identifiability of nonlinear ICA without auxiliary variables.

Inductive Bias

Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions

1 code implementation NeurIPS 2021 Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang

Many of the causal discovery methods rely on the faithfulness assumption to guarantee asymptotic correctness.

Causal Discovery

Source Free Unsupervised Graph Domain Adaptation

1 code implementation2 Dec 2021 Haitao Mao, Lun Du, Yujia Zheng, Qiang Fu, Zelin Li, Xu Chen, Shi Han, Dongmei Zhang

To address the non-trivial adaptation challenges in this practical scenario, we propose a model-agnostic algorithm called SOGA for domain adaptation to fully exploit the discriminative ability of the source model while preserving the consistency of structural proximity on the target graph.

Domain Adaptation Node Classification

Learning Elastic Embeddings for Customizing On-Device Recommenders

no code implementations4 Jun 2021 Tong Chen, Hongzhi Yin, Yujia Zheng, Zi Huang, Yang Wang, Meng Wang

The core idea is to compose elastic embeddings for each item, where an elastic embedding is the concatenation of a set of embedding blocks that are carefully chosen by an automated search function.

Recommendation Systems

Cold-start Sequential Recommendation via Meta Learner

no code implementations10 Dec 2020 Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu

As there is generally no side information in the setting of sequential recommendation task, previous cold-start methods could not be applied when only user-item interactions are available.

Meta-Learning Sequential Recommendation

Heterogeneous Graph Collaborative Filtering

no code implementations13 Nov 2020 Zekun Li, Yujia Zheng, Shu Wu, XiaoYu Zhang, Liang Wang

In this work, we propose to model user-item interactions as a heterogeneous graph which consists of not only user-item edges indicating their interaction but also user-user edges indicating their similarity.

Collaborative Filtering

DGTN: Dual-channel Graph Transition Network for Session-based Recommendation

1 code implementation21 Sep 2020 Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu

These item transitions include potential collaborative information and reflect similar behavior patterns, which we assume may help with the recommendation for the target session.

Session-Based Recommendations

Long-tail Session-based Recommendation

no code implementations24 Jul 2020 Siyi Liu, Yujia Zheng

Session-based recommendation focuses on the prediction of user actions based on anonymous sessions and is a necessary method in the lack of user historical data.

Session-Based Recommendations

Balancing Multi-level Interactions for Session-based Recommendation

no code implementations29 Oct 2019 Yujia Zheng, Siyi Liu, Zailei Zhou

For mining the new data without breaking the equilibrium of the model between different interactions, we construct an intra-session graph and an inter-session graph for the current session.

Session-Based Recommendations

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