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Shenbao Yu

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4 papers

1 author row

AAAI Conference 2026 Conference Paper

BiO-HMC: Dynamic Human-Machine Collaboration for Consensus Decision-Making via Bilevel Optimization

Yinghui Pan
Shuaijie Zhao
Shenbao Yu
Zongyang Liu
Yifeng Zeng
Han Liu
Mingwei Lin

Consensus decision-making uses crowd responses (usually from non-experts) to questions to reach a consensus answer based on human-machine collaboration. The crucial point is dynamic, which should not only enable rapid self-iteration toward the correct answer through crowd workers' responses but also adaptively suggest the next most valuable question(s) to accelerate the integration of the answer. However, existing methods reach consensus using either offline data or fixed question search structures, thereby largely sidestepping this dynamic nature. In response, we propose a bilevel optimization-based human-machine collaboration (BiO-HMC), which explores an inner & outer-level optimization to enable effective answer integration and efficient question selection. The resulting optimization problem is intractable because there is no closed-form expression in the inner-level optimization. We employ a gradient-based method and guarantee the method's theoretical convergence. Experimental results on synthetic and real-world datasets demonstrate the effectiveness and efficiency of the BiO-HMC model, i.e., achieving the highest confidence in the correct answer with the lowest labor cost.

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NeurIPS Conference 2024 Conference Paper

An Autoencoder-Like Nonnegative Matrix Co-Factorization for Improved Student Cognitive Modeling

Shenbao Yu
Yinghui Pan
Yifeng Zeng
Prashant Doshi
Guoquan Liu
Kim-Leng Poh
Mingwei Lin

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models.

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AAAI Conference 2024 Conference Paper

Causal-Driven Skill Prerequisite Structure Discovery

Shenbao Yu
Yifeng Zeng
Fan Yang
Yinghui Pan

Knowing a prerequisite structure among skills in a subject domain effectively enables several educational applications, including intelligent tutoring systems and curriculum planning. Traditionally, educators or domain experts use intuition to determine the skills' prerequisite relationships, which is time-consuming and prone to fall into the trap of blind spots. In this paper, we focus on inferring the prerequisite structure given access to students' performance on exercises in a subject. Nevertheless, it is challenging since students' mastery of skills can not be directly observed, but can only be estimated, i.e., its latency in nature. To tackle this problem, we propose a causal-driven skill prerequisite structure discovery (CSPS) method in a two-stage learning framework. In the first stage, we learn the skills' correlation relationships presented in the covariance matrix from the student performance data while, through the predicted covariance matrix in the second stage, we consider a heuristic method based on conditional independence tests and standardized partial variance to discover the prerequisite structure. We demonstrate the performance of the new approach with both simulated and real-world data. The experimental results show the effectiveness of the proposed model for identifying the skills' prerequisite structure.

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EAAI Journal 2024 Journal Article

Revisiting the loss functions in sequential recommendation

Fangyu Li
Hong Peng
Shenbao Yu
Feng Zeng
Fei Chen
Fan Yang

There has been a growing interest in benchmarking sequential recommendation models and reproducing/improving existing models. However, the exploration of loss functions in this context has been relatively limited. To address this gap, we investigate the application of diverse loss functions in sequential recommendation, focusing on Cross-Entropy (CE), Binary Cross-Entropy (BCE), and Bayesian Personalized Ranking (BPR) losses. Our objective is to enhance model performance through refining these loss functions. Existing loss functions in sequential recommendation are discussed and analyzed, summarizing their pros and cons. Following this, we identify the two crucial characteristics that an efficient loss function in sequential recommendation should embody, namely Time-Awareness and Efficiency. Subsequently, we introduce a straightforward yet impactful approach that seamlessly integrates time-awareness and efficiency into the computation of the loss function. Further, by utilizing meta-loss learning, we train time-aware parameters and integrate them into the loss function, enabling the model to adaptively capture significant user interactions with items. Our methodology is extensively validated through comprehensive experiments involving various classical and advanced sequential recommendation models on multiple publicly available datasets. Impressively, our approach not only leads to substantial performance improvements but also enables baseline models to surpass state-of-the-art models.

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