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Wei Bao

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12 papers

1 author row

AAAI Conference 2026 Conference Paper

Lost in Benchmarks? Rethinking Large Language Model Benchmarking with Item Response Theory

Hongli Zhou
Hui Huang
Ziqing Zhao
Lvyuan Han
Huicheng Wang
Kehai Chen
Muyun Yang
Wei Bao

The evaluation of large language models (LLMs) via benchmarks is widespread, yet inconsistencies between different leaderboards and poor separability among top models raise concerns about their ability to accurately reflect authentic model capabilities. This paper provides a critical analysis of benchmark effectiveness, examining mainstream prominent LLM benchmarks using results from diverse models. We first propose Pseudo-Siamese Network for Item Response Theory (PSN-IRT), an enhanced Item Response Theory framework that incorporates a rich set of item parameters within an IRT-grounded architecture. PSN-IRT can be utilized for accurate and reliable estimations of item characteristics and model abilities. Based on PSN-IRT, we conduct extensive analysis on 11 LLM benchmarks comprising 41,871 items, revealing significant and varied shortcomings in their measurement quality. Furthermore, we demonstrate that leveraging PSN-IRT is able to construct smaller benchmarks while maintaining stronger alignment with human preference.

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AAAI Conference 2026 Conference Paper

Online Capacitated General Matching with Knapsack

Ruoyu Wu
Wei Bao
Ben Liang
Hequn Wang

We study a new online matching problem termed Online Capacitated General Matching with Knapsack (OCGMK), which generalizes the Online General Matching (OGM) problem. In the original OGM, vertices arrive sequentially and need to be paired with other vertices to maximize the total reward of pairing. Our study is the first to consider capacitated vertices in OGM: we allow each vertex to be assigned to multiple vertices up to a capacity limit. We also consider a previously unexamined knapsack constraint in OGM: assigning a pair of vertices has a cost, but the total cost is budgeted. To solve the OCGMK problem, we propose the Online Capacity-Knapsack Assignment (OCKA) algorithm, which constructs capacity-friendly sets and knapsack-friendly sets to simultaneously and effectively address both constraints. OCKA achieves a competitive ratio of ⍺=?/2?, where?=1/(3+e^(-2)) and? is the ratio between the overall cost of all edges and the cost budget. When the knapsack constraint is not imposed but the capacitated vertices remain, the competitive ratio of OCKA is ⍺'=1/2, recovering the previous best result for single-capacity OGM. We implement trace-driven experiments to evaluate the practical performance of OCKA on a real-world dating dataset, demonstrating the superior performance of OCKA in online dating applications.

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AAAI Conference 2026 Conference Paper

Retaliatory Attacks Against Federated Unlearning via Data Leakage

Xinyi Sheng
Wei Bao
Hequn Wang
Yuqin Liu
Sen Fu

Federated unlearning (FU) allows a participating client in a federated learning (FL) system to remove its contribution from the trained global model, thereby enforcing the client’s ``right to be forgotten'' (RTBF). However, from the perspective of a client that does not request unlearning, the activation of the FU process may disrupt ongoing FL training and introduce additional computational and time overhead. In such cases, a client opposed to unlearning may be incentivized to retaliate against the unlearning client(s). In this work, we take the first step toward demonstrating the feasibility of such retaliatory behavior by exploiting the information leakage introduced during the FU process. Specifically, we propose a novel unlearning-induced membership inference attack (MIA) model, followed by a coarse-to-fine data generation method that enables an adversarial client to locally reconstruct the unlearned data. Building on this reconstruction, we introduce two targeted retaliatory attacks: (1) Anti-Unlearning Attack (AUA), which hinders the global model from successfully forgetting the data intended for removal, and (2) Discrimination-Unlearning Attack (DUA), which specifically degrades the global model’s performance on the unlearned data. Extensive experiments across a variety of FU methods and settings validate the effectiveness of the proposed retaliatory attack framework.

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AAAI Conference 2025 Conference Paper

Augmenting Online Algorithms for Knapsack Problem with Total Weight Information

Binghan Wu
Wei Bao
Bing Bing Zhou

In this paper, we augment online algorithms for the knapsack problem using the total weight information. The conventional optimal online algorithm achieves the ln(U/L)+1 competitive ratio where L and U are the upper and lower bounds of the value-to-weight ratio. However, it does not consider that decision makers can know the total weight information or obtain it through machine-learned predictions. To fill this gap, we first propose the Known Weight Algorithm (KWA) which uses the exact total weight information to achieve a competitive ratio of W((U-L)/(eL))+1, where W denotes the Lambert-W function. We prove that it is optimal and tight. After that, we extend KWA to the Predicted Weight Algorithm (PWA), a learning-augmented online algorithm that uses predicted total weight. We show the consistency and robustness of PWA, and prove that its competitive ratio degrades gracefully as the prediction error grows. Finally, we introduce the Limited Volume Algorithm (LWA), which achieves a better competitive ratio than ln(U/L)+1 when the total weight is less than twice the capacity.

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AAMAS Conference 2024 Conference Paper

Competitive Analysis of Online Facility Open Problem

Binghan Wu
Wei Bao
Bing Zhou

We investigate an online cost minimization problem of serving requests in a tree of facilities, referred to as the Online Facility Open Problem (Online FOP). To address this problem, we propose the Anchor-Barrier Algorithm (ABA), a threshold-based algorithm applicable to any tree and any cost assignment, which can work in a distributed manner for scalability. We conduct the competitive analysis and show that ABA’s achieves the optimal competitive ratio Height + 2, where Height is the height of the facility tree.

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IJCAI Conference 2023 Conference Paper

Online Task Assignment with Controllable Processing Time

Ruoyu Wu
Wei Bao
Liming Ge

We study a new online assignment problem, called the Online Task Assignment with Controllable Processing Time. In a bipartite graph, a set of online vertices (tasks) should be assigned to a set of offline vertices (machines) under the known adversarial distribution (KAD) assumption. We are the first to study controllable processing time in this scenario: There are multiple processing levels for each task and higher level brings larger utility but also larger processing delay. A machine can reject an assignment at the cost of a rejection penalty, taken from a pre-determined rejection budget. Different processing levels cause different penalties. We propose the Online Machine and Level Assignment (OMLA) Algorithm to simultaneously assign an offline machine and a processing level to each online task. We prove that OMLA achieves 1/2-competitive ratio if each machine has unlimited rejection budget and Δ/(3Δ-1)- competitive ratio if each machine has an initial rejection budget up to Δ. Interestingly, the competitive ratios do not change under different settings on the controllable processing time and we can conclude that OMLA is "insensitive" to the controllable processing time.

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IJCAI Conference 2022 Conference Paper

Competitive Analysis for Multi-Commodity Ski-Rental Problem

Binghan Wu
Wei Bao
Dong Yuan
Bing Zhou

We investigate an extended version of the classical ski-rental problem with multiple commodities. A customer uses a set of commodities altogether, and he/she needs to choose payment options to cover the usage of each commodity without the knowledge of the future. The payment options of each commodity include (1) renting: to pay for an on-demand usage and (2) buying: to pay for the lifetime usage. It is a novel extension of the classical ski-rental problem which deals with only one commodity. To address this problem, we propose a new online algorithm called the Multi-Object Break-Even (MOBE) algorithm and conduct competitive analysis. We show that the tight lower and upper bounds of MOBE algorithm's competitive ratio are e/e-1 and 2 respectively against adaptive adversary under arbitrary renting and buying prices. We further prove that MOBE algorithm is an optimal online algorithm if commodities have the same rent-to-buy ratio. Numerical results verify our theoretical conclusion and demonstrate the advantages of MOBE in a real-world scenario.

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AAAI Conference 2022 Conference Paper

Partial Multi-Label Learning via Large Margin Nearest Neighbour Embeddings

Xiuwen Gong
Dong Yuan
Wei Bao

To deal with ambiguities in partial multi-label learning (PML), existing popular PML research attempts to perform disambiguation by direct ground-truth label identification. However, these approaches can be easily misled by noisy false-positive labels in the iteration of updating the model parameter and the latent ground-truth label variables. When labeling information is ambiguous, we should depend more on underlying structure of data, such as label and feature correlations, to perform disambiguation for partially labeled data. Moreover, large margin nearest neighbour (LMNN) is a popular strategy that considers data structure in classification. However, due to the ambiguity of labeling information in PML, traditional LMNN cannot be used to solve the PML problem directly. In addition, embedding is an effective technology to decrease the noise information of data. Inspried by LMNN and embedding technology, we propose a novel PML paradigm called Partial Multi-label Learning via Large Margin Nearest Neighbour Embeddings (PML-LMNNE), which aims to conduct disambiguation by projecting labels and features into a lower-dimension embedding space and reorganize the underlying structure by LMNN in the embedding space simultaneously. An efficient algorithm is designed to implement the proposed method and the convergence rate of the algorithm is analyzed. Moreover, we present a theoretical analysis of the generalization error bound for the proposed PML- LMNNE, which shows that the generalization error converges to the sum of two times the Bayes error over the labels when the number of instances goes to infinity. Comprehensive experiments on artificial and real-world datasets demonstrate the superiorities of the proposed PML-LMNNE.

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AAAI Conference 2021 Conference Paper

Competitive Analysis for Two-Level Ski-Rental Problem

Binghan Wu
Wei Bao
Dong Yuan

In this paper, we study a two-level ski-rental problem. There are multiple commodities, each one can be “rented” (paying for on-demand usage) or “purchased” (paying for life-time usage). There is also a combo purchase available so that all commodities can be purchased as a combo. Since the usages of the commodities in future are not known in advance, to minimize the overall cost, we design an online algorithm to decide if we rent a commodity, purchase a commodity, or make a combo purchase. We first propose a deterministic online algorithm. It can achieve 3 competitive ratio, which is optimal and tight. Next, we further propose a randomized online algorithm, leading to a eσ eσ−1 competitive ratio, where σ is the ratio between the price of a single commodity and the price of combo purchase. Finally, we apply simulation to verify the theoretical competitive ratios and evaluate the actual performance against benchmarks.

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IJCAI Conference 2021 Conference Paper

Fast Multi-label Learning

Xiuwen Gong
Dong Yuan
Wei Bao

Embedding approaches have become one of the most pervasive techniques for multi-label classification. However, the training process of embedding methods usually involves a complex quadratic or semidefinite programming problem, or the model may even involve an NP-hard problem. Thus, such methods are prohibitive on large-scale applications. More importantly, much of the literature has already shown that the binary relevance (BR) method is usually good enough for some applications. Unfortunately, BR runs slowly due to its linear dependence on the size of the input data. The goal of this paper is to provide a simple method, yet with provable guarantees, which can achieve competitive performance without a complex training process. To achieve our goal, we provide a simple stochastic sketch strategy for multi-label classification and present theoretical results from both algorithmic and statistical learning perspectives. Our comprehensive empirical studies corroborate our theoretical findings and demonstrate the superiority of the proposed methods.

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NeurIPS Conference 2021 Conference Paper

Understanding Partial Multi-Label Learning via Mutual Information

Xiuwen Gong
Dong Yuan
Wei Bao

To deal with ambiguities in partial multilabel learning (PML), state-of-the-art methods perform disambiguation by identifying ground-truth labels directly. However, there is an essential question: “Can the ground-truth labels be identified precisely? ". If yes, “How can the ground-truth labels be found? ". This paper provides affirmative answers to these questions. Instead of adopting hand-made heuristic strategy, we propose a novel Mutual Information Label Identification for Partial Multilabel Learning (MILI-PML), which is derived from a clear probabilistic formulation and could be easily interpreted theoretically from the mutual information perspective, as well as naturally incorporates the feature/label relevancy considerations. Extensive experiments on synthetic and real-world datasets clearly demonstrate the superiorities of the proposed MILI-PML.

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AAAI Conference 2020 Conference Paper

Online Metric Learning for Multi-Label Classification

Xiuwen Gong
Dong Yuan
Wei Bao

Existing research into online multi-label classiﬁcation, such as online sequential multi-label extreme learning machine (OSML-ELM) and stochastic gradient descent (SGD), has achieved promising performance. However, these works lack an analysis of loss function and do not consider label dependency. Accordingly, to ﬁll the current research gap, we propose a novel online metric learning paradigm for multi-label classiﬁcation. More speciﬁcally, we ﬁrst project instances and labels into a lower dimension for comparison, then leverage the large margin principle to learn a metric with an efﬁcient optimization algorithm. Moreover, we provide theoretical analysis on the upper bound of the cumulative loss for our method. Comprehensive experiments on a number of benchmark multi-label datasets validate our theoretical approach and illustrate that our proposed online metric learning (OML) algorithm outperforms state-of-the-art methods.

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