Search Results for author: Zhixuan Chu

Found 30 papers, 5 papers with code

Incorporating Casual Analysis into Diversified and Logical Response Generation

no code implementations COLING 2022 Jiayi Liu, Wei Wei, Zhixuan Chu, Xing Gao, Ji Zhang, Tan Yan, Yulin kang

Although the Conditional Variational Auto-Encoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.

Response Generation

Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions

no code implementations16 Feb 2024 Guangya Wan, Yuqi Wu, Mengxuan Hu, Zhixuan Chu, Sheng Li

Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields of study with significant implications for artificial intelligence.

Causal Discovery

GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention

1 code implementation11 Feb 2024 Fangyu Ding, Haiyang Wang, Zhixuan Chu, Tianming Li, Zhaoping Hu, Junchi Yan

Many recent endeavors of GIL focus on extracting the invariant subgraph from the input graph for prediction as a regularization strategy to improve the generalization performance of graph learning.

Graph Attention Graph Learning

Professional Agents -- Evolving Large Language Models into Autonomous Experts with Human-Level Competencies

no code implementations6 Feb 2024 Zhixuan Chu, Yan Wang, Feng Zhu, Lu Yu, Longfei Li, Jinjie Gu

The advent of large language models (LLMs) such as ChatGPT, PaLM, and GPT-4 has catalyzed remarkable advances in natural language processing, demonstrating human-like language fluency and reasoning capacities.

Position

LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation

no code implementations16 Jan 2024 Zhixuan Chu, Yan Wang, Qing Cui, Longfei Li, Wenqing Chen, Sheng Li, Zhan Qin, Kui Ren

As personalized recommendation systems become vital in the age of information overload, traditional methods relying solely on historical user interactions often fail to fully capture the multifaceted nature of human interests.

Explainable Recommendation Recommendation Systems

Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction

no code implementations20 Dec 2023 Zhixuan Chu, Mengxuan Hu, Qing Cui, Longfei Li, Sheng Li

To address this, we propose a Task-Driven Causal Feature Distillation model (TDCFD) to transform original feature values into causal feature attributions for the specific risk prediction task.

Intelligent Virtual Assistants with LLM-based Process Automation

no code implementations4 Dec 2023 Yanchu Guan, Dong Wang, Zhixuan Chu, Shiyu Wang, Feiyue Ni, Ruihua Song, Longfei Li, Jinjie Gu, Chenyi Zhuang

This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests.

Language Modelling Large Language Model

Data-Centric Financial Large Language Models

no code implementations7 Oct 2023 Zhixuan Chu, Huaiyu Guo, Xinyuan Zhou, Yijia Wang, Fei Yu, Hong Chen, Wanqing Xu, Xin Lu, Qing Cui, Longfei Li, Jun Zhou, Sheng Li

Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.

Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data

no code implementations23 Sep 2023 Zhichao Chen, Leilei Ding, Zhixuan Chu, Yucheng Qi, Jianmin Huang, Hao Wang

Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios.

Decision Making Time Series +1

Trustworthy Representation Learning Across Domains

no code implementations23 Aug 2023 Ronghang Zhu, Dongliang Guo, Daiqing Qi, Zhixuan Chu, Xiang Yu, Sheng Li

Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework which includes four concepts, i. e, robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction.

Fairness Representation Learning

Continual Learning in Predictive Autoscaling

no code implementations29 Jul 2023 Hongyan Hao, Zhixuan Chu, Shiyi Zhu, Gangwei Jiang, Yan Wang, Caigao Jiang, James Zhang, Wei Jiang, Siqiao Xue, Jun Zhou

In order to surmount this challenge and effectively integrate new sample distribution, we propose a density-based sample selection strategy that utilizes kernel density estimation to calculate sample density as a reference to compute sample weight, and employs weight sampling to construct a new memory set.

Continual Learning Density Estimation

EasyTPP: Towards Open Benchmarking Temporal Point Processes

1 code implementation16 Jul 2023 Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Hongyan Hao, Fan Zhou, Caigao Jiang, Chen Pan, James Y. Zhang, Qingsong Wen, Jun Zhou, Hongyuan Mei

In this paper, we present EasyTPP, the first central repository of research assets (e. g., data, models, evaluation programs, documentations) in the area of event sequence modeling.

Benchmarking Point Processes

pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting

no code implementations16 May 2023 Yunyi Zhou, Zhixuan Chu, Yijia Ruan, Ge Jin, Yuchen Huang, Sheng Li

However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on.

Probabilistic Time Series Forecasting Time Series

Continual Causal Inference with Incremental Observational Data

no code implementations3 Mar 2023 Zhixuan Chu, Ruopeng Li, Stephen Rathbun, Sheng Li

We propose a Continual Causal Effect Representation Learning method for estimating causal effects with observational data, which are incrementally available from non-stationary data distributions.

Causal Inference counterfactual +3

Fair Attribute Completion on Graph with Missing Attributes

1 code implementation25 Feb 2023 Dongliang Guo, Zhixuan Chu, Sheng Li

To our best knowledge, FairAC is the first method that jointly addresses the graph attribution completion and graph unfairness problems.

Attribute Fairness +1

Causal Effect Estimation: Recent Advances, Challenges, and Opportunities

no code implementations2 Feb 2023 Zhixuan Chu, Jianmin Huang, Ruopeng Li, Wei Chu, Sheng Li

Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising.

Causal Inference Marketing +1

Continual Causal Effect Estimation: Challenges and Opportunities

no code implementations3 Jan 2023 Zhixuan Chu, Sheng Li

A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns.

Causal Inference counterfactual +3

Incorporating Causal Analysis into Diversified and Logical Response Generation

no code implementations20 Sep 2022 Jiayi Liu, Wei Wei, Zhixuan Chu, Xing Gao, Ji Zhang, Tan Yan, Yulin kang

Although the Conditional Variational AutoEncoder (CVAE) model can generate more diversified responses than the traditional Seq2Seq model, the responses often have low relevance with the input words or are illogical with the question.

Response Generation

Hierarchical Capsule Prediction Network for Marketing Campaigns Effect

no code implementations22 Aug 2022 Zhixuan Chu, Hui Ding, Guang Zeng, Yuchen Huang, Tan Yan, Yulin kang, Sheng Li

In this paper, we provide an in-depth analysis of the underlying parse tree-like structure involved in the effect prediction task and we further establish a Hierarchical Capsule Prediction Network (HapNet) for predicting the effects of marketing campaigns.

Marketing

Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials

no code implementations10 Mar 2022 Zhixuan Chu, Stephen L. Rathbun, Sheng Li

In our paper, the basket trial is employed as an intuitive example to present this new causal inference setting.

Causal Inference counterfactual +3

Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data

no code implementations22 Feb 2022 Zhixuan Chu, Stephen Rathbun, Sheng Li

In this paper, we reveal the weaknesses of these strategies, i. e., they lead to the loss of predictive information when enforcing the domain invariance; and the treatment effect estimation performance is unstable, which heavily relies on the characteristics of the domain distributions and the choice of domain divergence metrics.

Causal Inference Representation Learning +1

Causal Triple Attention Time Series Forecasting

no code implementations29 Sep 2021 Zhixuan Chu, Tan Yan, Yue Wu, Yi Xu, Cheng Zhang, Yulin kang

Time series forecasting has historically been a key area of academic research and industrial applications.

Causal Inference Time Series +1

Continual Lifelong Causal Effect Inference with Real World Evidence

no code implementations1 Jan 2021 Zhixuan Chu, Stephen Rathbun, Sheng Li

We propose a Continual Causal Effect Representation Learning method for estimating causal effect with observational data, which are incrementally available from non-stationary data distributions.

counterfactual Representation Learning +1

Matching in Selective and Balanced Representation Space for Treatment Effects Estimation

no code implementations15 Sep 2020 Zhixuan Chu, Stephen L. Rathbun, Sheng Li

The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference.

Causal Inference counterfactual +3

A Survey on Causal Inference

1 code implementation5 Feb 2020 Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, Aidong Zhang

Embraced with the rapidly developed machine learning area, various causal effect estimation methods for observational data have sprung up.

BIG-bench Machine Learning Causal Inference

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