Search Results for author: Ning Yang

Found 20 papers, 8 papers with code

Cross-Domain Causal Preference Learning for Out-of-Distribution Recommendation

1 code implementation23 Apr 2024 Zhuhang Li, Ning Yang

Recommender systems use users' historical interactions to learn their preferences and deliver personalized recommendations from a vast array of candidate items.

Out-of-Distribution Generalization Recommendation Systems

Token-level Direct Preference Optimization

1 code implementation18 Apr 2024 Yongcheng Zeng, Guoqing Liu, Weiyu Ma, Ning Yang, Haifeng Zhang, Jun Wang

Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions.

Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment

2 code implementations11 Apr 2024 Xinyu Zhu, Lilin Zhang, Ning Yang

The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy.

Attribute Fairness +1

Multi-objective Deep Reinforcement Learning for Mobile Edge Computing

1 code implementation5 Jul 2023 Ning Yang, Junrui Wen, Meng Zhang, Ming Tang

In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters.

Edge-computing reinforcement-learning +1

Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach

no code implementations3 Jul 2023 Shuo Chen, Ning Yang, Meng Zhang, Jun Wang

In this paper, we consider multiple users offloading tasks to heterogeneous edge servers in a MEC system.


Contrastive Collaborative Filtering for Cold-Start Item Recommendation

1 code implementation4 Feb 2023 Zhihui Zhou, Lilin Zhang, Ning Yang

To address this issue, we propose a novel model called Contrastive Collaborative Filtering for Cold-start item Recommendation (CCFCRec), which capitalizes on the co-occurrence collaborative signals in warm training data to alleviate the issue of blurry collaborative embeddings for cold-start item recommendation.

Collaborative Filtering Contrastive Learning +1

Provable Unrestricted Adversarial Training without Compromise with Generalizability

no code implementations22 Jan 2023 Lilin Zhang, Ning Yang, Yanchao Sun, Philip S. Yu

Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i. e., the accuracy on natural examples) because they make a tradeoff between them.

Adversarial Robustness

Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System

1 code implementation7 Sep 2022 Runmin Cong, Yumo Zhang, Ning Yang, Haisheng Li, Xueqi Zhang, Ruochen Li, Zewen Chen, Yao Zhao, Sam Kwong

The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing.

Time Lag Aware Sequential Recommendation

no code implementations9 Aug 2022 Lihua Chen, Ning Yang, Philip S Yu

First, the existing methods often lack the simultaneous consideration of the global stability and local fluctuation of user preference, which might degrade the learning of a user's current preference.

Sequential Recommendation

AI Enlightens Wireless Communication: A Transformer Backbone for CSI Feedback

no code implementations16 Jun 2022 Han Xiao, Zhiqin Wang, Dexin Li, Wenqiang Tian, Xiaofeng Liu, Wendong Liu, Shi Jin, Jia Shen, Zhi Zhang, Ning Yang

This paper is based on the background of the 2nd Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AIWork Group, where the framework of the eigenvector-based channel state information (CSI) feedback problem is firstly provided.

Data Augmentation

Stochastic gradient descent introduces an effective landscape-dependent regularization favoring flat solutions

no code implementations2 Jun 2022 Ning Yang, Chao Tang, Yuhai Tu

Empirical studies showed a strong correlation between flatness of the loss landscape at a solution and its generalizability, and stochastic gradient descent (SGD) is crucial in finding the flat solutions.

Many Field Packet Classification with Decomposition and Reinforcement Learning

no code implementations16 May 2022 Hasibul Jamil, Ning Yang, Ning Weng

Our method consists of the decomposition of fields into subsets and building separate decision trees on those subsets using a deep reinforcement learning procedure.

Classification Intrusion Detection +2

Global-and-Local Collaborative Learning for Co-Salient Object Detection

2 code implementations19 Apr 2022 Runmin Cong, Ning Yang, Chongyi Li, Huazhu Fu, Yao Zhao, Qingming Huang, Sam Kwong

In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives.

8k Co-Salient Object Detection +2

Multi-Sparse-Domain Collaborative Recommendation via Enhanced Comprehensive Aspect Preference Learning

no code implementations16 Jan 2022 Xiaoyun Zhao, Ning Yang, Philip S. Yu

Meanwhile, we propose a Multi-Domain Adaptation Network (MDAN) for MSDCR to capture a user's domain-invariant aspect preference.

Domain Adaptation Recommendation Systems

Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning

no code implementations16 Jan 2022 Ziwen Du, Ning Yang, Zhonghua Yu, Philip S. Yu

To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users.

reinforcement-learning Reinforcement Learning (RL)

Dual Adversarial Variational Embedding for Robust Recommendation

no code implementations30 Jun 2021 Qiaomin Yi, Ning Yang, Philip S. Yu

First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns.

Variational Inference

AI Enlightens Wireless Communication: Analyses, Solutions and Opportunities on CSI Feedback

no code implementations12 Jun 2021 Han Xiao, Zhiqin Wang, Wenqiang Tian, Xiaofeng Liu, Wendong Liu, Shi Jin, Jia Shen, Zhi Zhang, Ning Yang

In this paper, we give a systematic description of the 1st Wireless Communication Artificial Intelligence (AI) Competition (WAIC) which is hosted by IMT-2020(5G) Promotion Group 5G+AI Work Group.


Deep Collaborative Embedding for information cascade prediction

no code implementations18 Jan 2020 Yuhui Zhao, Ning Yang, Tao Lin, Philip S. Yu

First, the existing works often assume an underlying information diffusion model, which is impractical in real world due to the complexity of information diffusion.

Hybrid Deep Embedding for Recommendations with Dynamic Aspect-Level Explanations

1 code implementation18 Jan 2020 Huanrui Luo, Ning Yang, Philip S. Yu

Particularly, as the aspect preference/quality of users/items is learned automatically, HDE is able to capture the impact of aspects that are not mentioned in reviews of a user or an item.

Explainable Recommendation

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