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Zhining Liu

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9 papers

1 author row

NeurIPS Conference 2025 Conference Paper

CLIMB: Class-imbalanced Learning Benchmark on Tabular Data

Zhining Liu
Zihao Li
Ze Yang
Tianxin Wei
Jian Kang
Yada Zhu
Hendrik Hamann
Jingrui He

Class-imbalanced learning (CIL) on tabular data is important in many real-world applications where the minority class holds the critical but rare outcomes. In this paper, we present CLIMB, a comprehensive benchmark for class-imbalanced learning on tabular data. CLIMB includes 73 real-world datasets across diverse domains and imbalance levels, along with unified implementations of 29 representative CIL algorithms. Built on a high-quality open-source Python package with unified API designs, detailed documentation, and rigorous code quality controls, CLIMB supports easy implementation and comparison between different CIL algorithms. Through extensive experiments, we provide practical insights on method accuracy and efficiency, highlighting the limitations of naive rebalancing, the effectiveness of ensembles, and the importance of data quality. Our code, documentation, and examples are available at https: //github. com/ZhiningLiu1998/imbalanced-ensemble.

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NeurIPS Conference 2024 Conference Paper

BackTime: Backdoor Attacks on Multivariate Time Series Forecasting

Xiao Lin
Zhining Liu
Dongqi Fu
Ruizhong Qiu
Hanghang Tong

Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology. While a myriad of powerful deep learning models have been developed for this task, few works have explored the robustness of MTS forecasting models to malicious attacks, which is crucial for their trustworthy employment in high-stake scenarios. To address this gap, we dive deep into the backdoor attacks on MTS forecasting models and propose an effective attack method named BackTime. By subtly injecting a few \textit{stealthy triggers} into the MTS data, BackTime can alter the predictions of the forecasting model according to the attacker's intent. Specifically, BackTime first identifies vulnerable timestamps in the data for poisoning, and then adaptively synthesizes stealthy and effective triggers by solving a bi-level optimization problem with a GNN-based trigger generator. Extensive experiments across multiple datasets and state-of-the-art MTS forecasting models demonstrate the effectiveness, versatility, and stealthiness of BackTime attacks.

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AAAI Conference 2024 Conference Paper

Hierarchical Multi-Marginal Optimal Transport for Network Alignment

Zhichen Zeng
Boxin Du
Si Zhang
Yinglong Xia
Zhining Liu
Hanghang Tong

Finding node correspondence across networks, namely multi-network alignment, is an essential prerequisite for joint learning on multiple networks. Despite great success in aligning networks in pairs, the literature on multi-network alignment is sparse due to the exponentially growing solution space and lack of high-order discrepancy measures. To fill this gap, we propose a hierarchical multi-marginal optimal transport framework named HOT for multi-network alignment. To handle the large solution space, multiple networks are decomposed into smaller aligned clusters via the fused Gromov-Wasserstein (FGW) barycenter. 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. A fast proximal point method is further developed with guaranteed convergence to a local optimum. Extensive experiments and analysis show that our proposed HOT achieves significant improvements over the state-of-the-art in both effectiveness and scalability.

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AAAI Conference 2024 Conference Paper

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

Zhitian Xie
Yinger Zhang
Chenyi Zhuang
Qitao Shi
Zhining Liu
Jinjie Gu
Guannan Zhang

The application of mixture-of-experts (MoE) is gaining popularity due to its ability to improve model's performance. In an MoE structure, the gate layer plays a significant role in distinguishing and routing input features to different experts. This enables each expert to specialize in processing their corresponding sub-tasks. 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 subtask, which in turn limits the MoE to further improve its generalization ability. To effectively address this, we propose a method called Mixture-of-Distilled-Expert (MoDE), which applies moderate mutual distillation among experts to enable each expert to pick up more features learned by other experts and gain more accurate perceptions on their allocated sub-tasks. We conduct plenty experiments including tabular, NLP and CV datasets, which shows MoDE's effectiveness, universality and robustness. Furthermore, we develop a parallel study through innovatively constructing "expert probing", to experimentally prove why MoDE works: moderate distilling knowledge from other experts can improve each individual expert's test performances on their assigned tasks, leading to MoE's overall performance improvement.

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IJCAI Conference 2023 Conference Paper

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

Xingyu Lu
Zhining Liu
Yanchu Guan
Hongxuan Zhang
Chenyi Zhuang
Wenqi Ma
Yize Tan
Jinjie Gu

Given the enormous number of users and items, industrial cascade recommendation systems (RS) are continuously expanded in size and complexity to deliver relevant items, such as news, services, and commodities, to the appropriate users. In a real-world scenario with hundreds of thousands requests per second, significant computation is required to infer personalized results for each request, resulting in a massive energy consumption and carbon emission that raises concern. This paper proposes GreenFlow, a practical computation allocation framework for RS, that considers both accuracy and carbon emission during inference. For each stage (e. g. , recall, pre-ranking, ranking, etc. ) 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. We refer to the combinations of actions in all stages as action chains. A reward score is estimated for each action chain, followed by dynamic primal-dual optimization considering both the reward and computation budget. Extensive experiments verify the effectiveness of the framework, reducing computation consumption by 41% in an industrial mobile application while maintaining commercial revenue. Moreover, the proposed framework saves approximately 5000kWh of electricity and reduces 3 tons of carbon emissions per day.

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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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NeurIPS Conference 2020 Conference Paper

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

Zhining Liu
Pengfei Wei
Jing Jiang
Wei Cao
Jiang Bian
Yi Chang

Imbalanced learning (IL), i. e. , learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed based on some heuristic assumptions. They often suffer from unstable performance, poor applicability, and high computational cost in complex tasks where their assumptions do not hold. In this paper, we introduce a novel ensemble IL framework named MESA. It adaptively resamples the training set in iterations to get multiple classifiers and forms a cascade ensemble model. MESA directly learns the sampling strategy from data to optimize the final metric beyond following random heuristics. Moreover, unlike prevailing meta-learning-based IL solutions, we decouple the model-training and meta-training in MESA by independently train the meta-sampler over task-agnostic meta-data. This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks. Extensive experiments on both synthetic and real-world tasks demonstrate the effectiveness, robustness, and transferability of MESA. Our code is available at https: //github. com/ZhiningLiu1998/mesa.

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AAAI Conference 2020 Conference Paper

Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk

Zhining Liu
Dawei Zhou
Yada Zhu
Jinjie Gu
Jingrui He

Encoding a large-scale network into a low-dimensional space is a fundamental step for various network analytic problems, such as node classiﬁcation, link prediction, community detection, etc. Existing methods focus on learning the network representation from either the static graphs or timeaggregated graphs (e. g. , time-evolving graphs). However, many real systems are not static or time-aggregated as the nodes and edges are timestamped and dynamically changing over time. For examples, in anti-money laundering analysis, cycles formed with time-ordered transactions might be red ﬂags in online transaction networks; in novelty detection, a star-shaped structure appearing in a short burst might be an underlying hot topic in social networks. Existing embedding models might not be able to well preserve such ﬁnegrained network dynamics due to the incapability of dealing with continuous-time and the negligence of ﬁne-grained interactions. To bridge this gap, in this paper, we propose a ﬁne-grained temporal network embedding framework named FiGTNE, which aims to learn a comprehensive network representation that preserves the rich and complex network context in the temporal network. In particular, we start from the notion of ﬁne-grained temporal networks, where the temporal network can be represented as a series of timestamped nodes and edges. Then, we propose the time-reinforced random walk (TRRW) with a bi-level context sampling strategy to explore the essential structures and temporal contexts in temporal networks. Extensive experimental results on real graphs demonstrate the efﬁcacy of our FiGTNE framework.

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EAAI Journal 1993 Journal Article

Knowledge representation for problem processing in a decision support system

Mengguang Wang
Zhining Liu
Zihou Yang

This paper examines the position and function of a “problem processing system” (PPS) in the context of a decision support system (DSS), and proposes a relatively complete framework for such a problem processing system. Using the concept of a “problem processing mode”, the paper introduces further research into the so-called “representation of procedural knowledge”, and presents a generalized structure for PPSs. This structure has been used in specific problem processing in a production-planning context.

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