Search Results for author: Longfei Li

Found 30 papers, 2 papers with code

A Causal Explainable Guardrails for Large Language Models

no code implementations7 May 2024 Zhixuan Chu, Yan Wang, Longfei Li, Zhibo Wang, Zhan Qin, Kui Ren

Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases.

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.


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, 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.

Financial Analysis

Learning from Negative User Feedback and Measuring Responsiveness for Sequential Recommenders

no code implementations23 Aug 2023 Yueqi Wang, Yoni Halpern, Shuo Chang, Jingchen Feng, Elaine Ya Le, Longfei Li, Xujian Liang, Min-Cheng Huang, Shane Li, Alex Beutel, Yaping Zhang, Shuchao Bi

In this work, we incorporate explicit and implicit negative user feedback into the training objective of sequential recommenders in the retrieval stage using a "not-to-recommend" loss function that optimizes for the log-likelihood of not recommending items with negative feedback.

counterfactual Recommendation Systems +1

ALT: An Automatic System for Long Tail Scenario Modeling

no code implementations19 May 2023 Ya-Lin Zhang, Jun Zhou, Yankun Ren, Yue Zhang, Xinxing Yang, Meng Li, Qitao Shi, Longfei Li

In this paper, we consider the problem of long tail scenario modeling with budget limitation, i. e., insufficient human resources for model training stage and limited time and computing resources for model inference stage.

Meta-Learning Neural Architecture Search +1

DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

no code implementations13 Feb 2023 Feng Zhu, Mingjie Zhong, Xinxing Yang, Longfei Li, Lu Yu, Tiehua Zhang, Jun Zhou, Chaochao Chen, Fei Wu, Guanfeng Liu, Yan Wang

In recommendation scenarios, there are two long-standing challenges, i. e., selection bias and data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through Rate (CTR) and post-click Conversion Rate (CVR) tasks.

counterfactual Multi-Task Learning +1

Unleashing the Power of Graph Data Augmentation on Covariate Distribution Shift

1 code implementation NeurIPS 2023 Yongduo Sui, Qitian Wu, Jiancan Wu, Qing Cui, Longfei Li, Jun Zhou, Xiang Wang, Xiangnan He

From the perspective of invariant learning and stable learning, a recently well-established paradigm for out-of-distribution generalization, stable features of the graph are assumed to causally determine labels, while environmental features tend to be unstable and can lead to the two primary types of distribution shifts.

Data Augmentation Graph Classification +2

Robust Direct Learning for Causal Data Fusion

no code implementations1 Nov 2022 Xinyu Li, Yilin Li, Qing Cui, Longfei Li, Jun Zhou

In the era of big data, the explosive growth of multi-source heterogeneous data offers many exciting challenges and opportunities for improving the inference of conditional average treatment effects.

Expert Knowledge-guided Geometric Representation Learning for Magnetic Resonance Imaging-based Glioma Grading

no code implementations8 Jan 2022 Yeqi Wang, Longfei Li, Cheng Li, Yan Xi, Hairong Zheng, Yusong Lin, Shanshan Wang

Geometric manifolds of hand-crafted features and learned features are constructed to mine the implicit relationship between deep learning and radiomics, and therefore to dig mutual consent and essential representation for the glioma grades.

Lesion Segmentation Representation Learning

Radiomic biomarker extracted from PI-RADS 3 patients support more eìcient and robust prostate cancer diagnosis: a multi-center study

no code implementations23 Dec 2021 Longfei Li, Rui Yang, Xin Chen, Cheng Li, Hairong Zheng, Yusong Lin, Zaiyi Liu, Shanshan Wang

Prostate Imaging Reporting and Data System (PI-RADS) based on multi-parametric MRI classi\^ees patients into 5 categories (PI-RADS 1-5) for routine clinical diagnosis guidance.

A Unified Framework for Cross-Domain and Cross-System Recommendations

no code implementations18 Aug 2021 Feng Zhu, Yan Wang, Jun Zhou, Chaochao Chen, Longfei Li, Guanfeng Liu

Moreover, to avoid negative transfer, we further propose a Personalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i. e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively.

Graph Embedding

Cross-Domain Recommendation: Challenges, Progress, and Prospects

no code implementations2 Mar 2021 Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, Guanfeng Liu

To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain.

Recommendation Systems

Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction

no code implementations13 Dec 2020 Kai Zhang, Hao Qian, Qing Cui, Qi Liu, Longfei Li, Jun Zhou, Jianhui Ma, Enhong Chen

In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest in the recent literature.

Click-Through Rate Prediction

SAIL: Self-Augmented Graph Contrastive Learning

no code implementations2 Sep 2020 Lu Yu, Shichao Pei, Lizhong Ding, Jun Zhou, Longfei Li, Chuxu Zhang, Xiangliang Zhang

This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario.

Contrastive Learning Knowledge Distillation +1

Interpretable MTL from Heterogeneous Domains using Boosted Tree

no code implementations16 Mar 2020 Ya-Lin Zhang, Longfei Li

Multi-task learning (MTL) aims at improving the generalization performance of several related tasks by leveraging useful information contained in them.

Multi-Task Learning Philosophy

SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks

no code implementations5 Mar 2020 Qitao Shi, Ya-Lin Zhang, Longfei Li, Xinxing Yang, Meng Li, Jun Zhou

Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems.

BIG-bench Machine Learning Feature Engineering

A Time Attention based Fraud Transaction Detection Framework

no code implementations26 Dec 2019 Longfei Li, Ziqi Liu, Chaochao Chen, Ya-Lin Zhang, Jun Zhou, Xiaolong Li

With online payment platforms being ubiquitous and important, fraud transaction detection has become the key for such platforms, to ensure user account safety and platform security.

A Boosting Framework of Factorization Machine

no code implementations17 Apr 2018 Longfei Li, Peilin Zhao, Jun Zhou, Xiaolong Li

However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets.

Recommendation Systems

Distributed Collaborative Hashing and Its Applications in Ant Financial

no code implementations13 Apr 2018 Chaochao Chen, Ziqi Liu, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li

The experimental results demonstrate that, comparing with the classic and state-of-the-art (distributed) latent factor models, DCH has comparable performance in terms of recommendation accuracy but has both fast convergence speed in offline model training procedure and realtime efficiency in online recommendation procedure.

Collaborative Filtering

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

3 code implementations3 Feb 2018 Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, Yuan Qi

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.

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