Search Results for author: Zhining Liu

Found 16 papers, 8 papers with code

AIM: Attributing, Interpreting, Mitigating Data Unfairness

1 code implementation13 Jun 2024 Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Yada Zhu, Hendrik Hamann, Hanghang Tong

Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals.


Mitigate Position Bias with Coupled Ranking Bias on CTR Prediction

no code implementations29 May 2024 Yao Zhao, Zhining Liu, Tianchi Cai, Haipeng Zhang, Chenyi Zhuang, Jinjie Gu

Using both synthetic and industrial datasets, we first show how this widely coexisted ranking bias deteriorates the performance of the existing position bias estimation methods.

Click-Through Rate Prediction Position +1

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, Yao Zhao, 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.


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, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, Hanghang Tong

In the ever-evolving landscape of user-item interactions, continual adaptation to newly collected data is crucial for recommender systems to stay aligned with the latest user preferences.

Fairness Recommendation Systems +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.


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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