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Lihong Gu

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6 papers

2 author rows

AAAI Conference 2025 Conference Paper

Bagging-Expert Network for Multi-Task Learning: A Depolarization Solution in Multi-Gate Mixture-of-Experts

Gong-Duo Zhang
Ruiqing Chen
Qian Zhao
Zhengwei Wu
Fengyu Han
Huan-Yi Su
Ziqi Liu
Lihong Gu

Multi-task learning (MTL) is widely utilized across a variety of real-world applications, including recommendation systems. For instance, in the field of e-commerce, MTL is commonly employed to simultaneously model click, conversion, and user dwelling time. Among a various of MTL models, the Multi-gate Mixture-of-Experts (MMoE) has gained significant popularity. However, MMoE suffers from the polarization issue during training, where the weights of certain experts tend to converge towards 0. To address this issue, we propose a novel method called Bagging-Expert network (BEnet) for multi-task learning. BEnet effectively mitigates the problem of polarization and achieves excellent performance in multi-task learning. It incorporates a bagging layer and an attention mechanism to encourage experts focusing on diverse knowledge domains. Simultaneously, polarization is avoided as different experts execute respective duties and specialize in distinct domains. Experimental results on real-world datasets demonstrate that BEnet has strong robustness and outperforms other state-of-the-art (SOTA) MTL methods.

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AAAI Conference 2024 Conference Paper

Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations

Junpeng Fang
Gongduo Zhang
Qing Cui
Caizhi Tang
Lihong Gu
Longfei Li
Jinjie Gu
Jun Zhou

Accurate prediction of coupon usage is crucial for promoting user consumption through targeted coupon recommendations. However, in real-world coupon recommendations, the coupon allocation process is not solely determined by the model trained with the history interaction data but is also interfered with by marketing tactics desired to fulfill specific commercial goals.This interference creates an imbalance in the interactions, which causes the data to deviate from the user's natural preferences. We refer to this deviation as the matching bias. Such biased interaction data affects the efficacy of the model, and thus it is necessary to employ debiasing techniques to prevent any negative impact. We investigate the mitigation of matching bias in coupon recommendations from a causal-effect perspective. By treating the attributes of users and coupons associated with marketing tactics as confounders, we find the confounders open the backdoor path between user-coupon matching and the conversion, which introduces spurious correlation. To remove the bad effect, we propose a novel training paradigm named Backdoor Adjustment via Group Adaptation (BAGA) for debiased coupon recommendations, which performs intervened training and inference, i.e., separately modeling each user-coupon group pair. However, modeling all possible group pairs greatly increases the computational complexity and cost. To address the efficiency challenge, we further present a simple but effective dual-tower multi-task framework and leverage the Customized Gate Control (CGC) model architecture, which separately models each user and coupon group with a separate expert module. We instantiate BAGA on five representative models: FM, DNN, NCF, MASKNET, and DEEPFM, and conduct comprehensive offline and online experiments to demonstrate the efficacy of our proposed paradigm.

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ICLR Conference 2023 Conference Paper

Multi-Objective Online Learning

Jiyan Jiang
Wenpeng Zhang 0003
Shiji Zhou
Lihong Gu
Xiaodong Zeng
Wenwu Zhu 0001

This paper presents a systematic study of multi-objective online learning. We first formulate the framework of Multi-Objective Online Convex Optimization, which encompasses a novel multi-objective regret. This regret is built upon a sequence-wise extension of the commonly used discrepancy metric Pareto suboptimality gap in zero-order multi-objective bandits. We then derive an equivalent form of the regret, making it amenable to be optimized via first-order iterative methods. To motivate the algorithm design, we give an explicit example in which equipping OMD with the vanilla min-norm solver for gradient composition will incur a linear regret, which shows that merely regularizing the iterates, as in single-objective online learning, is not enough to guarantee sublinear regrets in the multi-objective setting. To resolve this issue, we propose a novel min-regularized-norm solver that regularizes the composite weights. Combining min-regularized-norm with OMD results in the Doubly Regularized Online Mirror Multiple Descent algorithm. We further derive the multi-objective regret bound for the proposed algorithm, which matches the optimal bound in the single-objective setting. Extensive experiments on several real-world datasets verify the effectiveness of the proposed algorithm.

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NeurIPS Conference 2022 Conference Paper

Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses

Yuzhou Cao
Tianchi Cai
Lei Feng
Lihong Gu
Jinjie Gu
Bo An
Gang Niu
Masashi Sugiyama

\emph{Classification with rejection} (CwR) refrains from making a prediction to avoid critical misclassification when encountering test samples that are difficult to classify. Though previous methods for CwR have been provided with theoretical guarantees, they are only compatible with certain loss functions, making them not flexible enough when the loss needs to be changed with the dataset in practice. In this paper, we derive a novel formulation for CwR that can be equipped with arbitrary loss functions while maintaining the theoretical guarantees. First, we show that $K$-class CwR is equivalent to a $(K\! +\! 1)$-class classification problem on the original data distribution with an augmented class, and propose an empirical risk minimization formulation to solve this problem with an estimation error bound. Then, we find necessary and sufficient conditions for the learning \emph{consistency} of the surrogates constructed on our proposed formulation equipped with any classification-calibrated multi-class losses, where consistency means the surrogate risk minimization implies the target risk minimization for CwR. Finally, experiments on benchmark datasets validate the effectiveness of our proposed method.

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AAAI Conference 2022 Conference Paper

Imbalance-Aware Uplift Modeling for Observational Data

Xuanying Chen
Zhining Liu
Li Yu
Liuyi Yao
Wenpeng Zhang
Yi Dong
Lihong Gu
Xiaodong Zeng

Uplift modeling aims to model the incremental impact of a treatment on an individual outcome, which has attracted great interests of researchers and practitioners from different communities. Existing uplift modeling methods rely on either the data collected from randomized controlled trials (RCTs) or the observational data which is more realistic. However, we notice that on the observational data, it is often the case that only a small number of subjects receive treatment, but finally infer the uplift on a much large group of subjects. Such highly imbalanced data is common in various fields such as marketing and medical treatment but it is rarely handled by existing works. In this paper, we theoretically and quantitatively prove that the existing representative methods, transformed outcome (TOM) and doubly robust (DR), suffer from large bias and deviation on highly imbalanced datasets with skewed propensity scores, mainly because they are proportional to the reciprocal of the propensity score. To reduce the bias and deviation of uplift modeling with an imbalanced dataset, we propose an imbalance-aware uplift modeling (IAUM) method via constructing a robust proxy outcome, which adaptively combines the doubly robust estimator and the imputed treatment effects based on the propensity score. We theoretically prove that IAUM can obtain a better bias-variance trade-off than existing methods on a highly imbalanced dataset. We conduct extensive experiments on a synthetic dataset and two real-world datasets, and the experimental results well demonstrate the superiority of our method over state-of-the-art.

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AAAI Conference 2021 Conference Paper

Joint Incentive Optimization of Customer and Merchant in Mobile Payment Marketing

Li Yu
Zhengwei Wu
Tianchi Cai
Ziqi Liu
Zhiqiang Zhang
Lihong Gu
Xiaodong Zeng
Jinjie Gu

In the mobile Internet era, mobile payment service becomes the foundation of inclusive finance, which brings convenience and security to people. Various marketing strategies are designed to encourage mobile payment activities by allocating incentives such as coupons or commissions to customers or merchants. We summary two significant issues. First, there is a phenomenon of mutual influence between merchants and customers, i. e. , bipartite influence issue, thus making the independent optimization of customers and merchants nonoptimal. Second, the redemptions of coupons are partially observed, as we can only observe that the customer redeems the coupon or not at a specific incentive value, but cannot observe that at other incentive value, i. e. , data censorship issue. In this paper, we propose a novel joint incentive optimization framework to address the above two issues. We propose to use a graph neural network to represent customers and merchants jointly by modeling the underlying bipartite influences. We then formulate the response model under the hazard regression setting and model the hazard rate with a piecewise nonlinear function to capture the changes of responses to different incentive values. Finally, we propose a linear programming method to allocate approximated optimal incentive values to customers and merchants in real-time. Extensive offline and online experimental results demonstrate the effectiveness of our proposed approach.

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