Search Results for author: Zhining Liu

Found 14 papers, 6 papers with code

MoDE: A Mixture-of-Experts Model with Mutual Distillation among the Experts

no code implementations31 Jan 2024 Zhitian Xie, Yinger Zhang, Chenyi Zhuang, Qitao Shi, Zhining Liu, Jinjie Gu, Guannan Zhang

However, the gate's routing mechanism also gives rise to narrow vision: the individual MoE's expert fails to use more samples in learning the allocated sub-task, which in turn limits the MoE to further improve its generalization ability.

GreenFlow: A Computation Allocation Framework for Building Environmentally Sound Recommendation System

no code implementations15 Dec 2023 Xingyu Lu, Zhining Liu, Yanchu Guan, Hongxuan Zhang, Chenyi Zhuang, Wenqi Ma, Yize Tan, Jinjie Gu, Guannan Zhang

of a cascade RS, when a user triggers a request, we define two actions that determine the computation: (1) the trained instances of models with different computational complexity; and (2) the number of items to be inferred in the stage.

Recommendation Systems

Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads to Answers Faster

1 code implementation14 Nov 2023 Hongxuan Zhang, Zhining Liu, Jiaqi Zheng, Chenyi Zhuang, Jinjie Gu, Guihai Chen

In this work, we propose FastCoT, a model-agnostic framework based on parallel decoding without any further training of an auxiliary model or modification to the LLM itself.

Position

Hierarchical Multi-Marginal Optimal Transport for Network Alignment

no code implementations6 Oct 2023 Zhichen Zeng, Boxin Du, Si Zhang, Yinglong Xia, Zhining Liu, Hanghang Tong

To depict high-order relationships across multiple networks, the FGW distance is generalized to the multi-marginal setting, based on which networks can be aligned jointly.

Ensuring User-side Fairness in Dynamic Recommender Systems

no code implementations29 Aug 2023 Hyunsik Yoo, Zhichen Zeng, Jian Kang, Zhining Liu, David Zhou, Fei Wang, Eunice Chan, Hanghang Tong

However, fairness-constrained re-ranking, a typical method to ensure user-side fairness (i. e., reducing performance disparity), faces two fundamental challenges in the dynamic setting: (1) non-differentiability of the ranking-based fairness constraint, which hinders the end-to-end training paradigm, and (2) time-inefficiency, which impedes quick adaptation to changes in user preferences.

Fairness Recommendation Systems +1

Topological Augmentation for Class-Imbalanced Node Classification

no code implementations27 Aug 2023 Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong

Class imbalance is prevalent in real-world node classification tasks and often biases graph learning models toward majority classes.

Classification Graph Learning +1

UADB: Unsupervised Anomaly Detection Booster

1 code implementation3 Jun 2023 Hangting Ye, Zhining Liu, Xinyi Shen, Wei Cao, Shun Zheng, Xiaofan Gui, Huishuai Zhang, Yi Chang, Jiang Bian

This is a challenging task given the heterogeneous model structures and assumptions adopted by existing UAD methods.

Unsupervised Anomaly Detection

Adversarial Learning for Incentive Optimization in Mobile Payment Marketing

no code implementations28 Dec 2021 Xuanying Chen, Zhining Liu, Li Yu, Sen Li, Lihong Gu, Xiaodong Zeng, Yize Tan, Jinjie Gu

This bias deteriorates the performance of the response model and misleads the linear programming process, dramatically degrading the performance of the resulting allocation policy.

Marketing

IMBENS: Ensemble Class-imbalanced Learning in Python

1 code implementation24 Nov 2021 Zhining Liu, Jian Kang, Hanghang Tong, Yi Chang

imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem.

Ensemble Learning

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

2 code implementations NeurIPS 2020 Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang

This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.

imbalanced classification Meta-Learning

Self-paced Ensemble for Highly Imbalanced Massive Data Classification

1 code implementation8 Sep 2019 Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu

To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.

Classification General Classification +1

Scalable attribute-aware network embedding with locality

no code implementations17 Apr 2018 Weiyi Liu, Zhining Liu, Toyotaro Suzumura, Guangmin Hu

Here we propose \emph{SANE}, a scalable attribute-aware network embedding algorithm with locality, to learn the joint representation from topology and attributes.

Attribute Network Embedding

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