Yao Zhou
Yao Zhou is a Software Engineer at Google. He holds a Ph.D. in Computer Science from the University of Illinois Urbana-Champaign (UIUC). His research and professional expertise focus on machine learning and data mining, including human-in-the-loop systems, crowdsourcing, adversarial training, and federated learning.
Authored Publications
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Bi-level Hierarchical Neural Contextual Bandits for Online Recommendation
Yunzhe Qi
Yikun Ban
Allan Stewart
Chuanwei Ruan
Jiachuan He
Shishir Kumar Prasad
Haixun Wang
Jingrui He
Transactions on Machine Learning Research (2026)
Preview abstract
Contextual bandit algorithms aim to identify the optimal choice among a set of candidate arms, based on their contextual information. Among others, the neural contextual bandit algorithms have demonstrated generally superior performance compared to traditional linear and kernel-based methods. Nevertheless, neural methods are not inherently suitable to handle a large number of candidate arms due to their high computational cost when performing neural exploration.
Motivated by the widespread availability of arm category information (e.g., movie genres, retailer types), we formulate contextual bandits into a bi-level recommendation problem based on the accessible arm category information, and propose a novel neural bandit framework, named H2N-Bandit, which utilizes a bi-level hierarchical neural structure to mitigate the substantial computational cost found in conventional neural bandit methods.
To demonstrate its effectiveness, we provide the regret bound for H2N-Bandit under the over-parameterized neural bandit settings. Furthermore, to illustrate its efficiency, we conduct extensive experiments on multiple real-world public data sets with various specifications, showing that H2N-Bandit can significantly reduce the computational cost over existing non-linear methods while achieving better or comparable performances against state-of-the-art baselines.
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Bi-NAS: Towards Effective and Personalized Explanation for Recommender Systems via Bi-Level Neural Architecture Search
Longfeng Wu
Tong Zeng
Zhimin Peng
Bhanu Pratap Singh Rawat
Lecheng Zheng
Giovanni Seni
Dawei Zhou
2025
Preview abstract
Recommender systems are vital in helping users navigate vast amounts of information, offering personalized suggestions and effective explanations for these recommendations. While previous efforts have attempted to provide such explanations, evaluating their effectiveness across various scenarios remains a challenge. Enhancing these explanations is essential for improving user engagement, trust, and decision-making.
To facilitate effective explanations within the recommender system, we propose a Bi-level Neural Architecture Search (Bi-NAS) framework to optimize explanations. This approach simultaneously refines cross-attention mechanisms and feature interaction functions by exploring both intra-layer and inter-layer design spaces.
Furthermore, we integrate Large Language Models (LLMs) to enhance explanation generation, leveraging zero-shot prompting to produce more effective and personalized justifications. By aligning user feature preferences with item quality scores, our approach ensures that explanations reflect both user intent and item attributes, improving transparency and reasoning depth. Extensive evaluations on four real-world datasets demonstrate that Bi-NAS not only boosts recommendation accuracy but also significantly improves the effectiveness of explanations for the recommender systems, providing users with clear and reliable insights into the suggestions they receive.
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Graph Neural Networks (GNNs) have become indispensable tools in many domains, such as social network analysis, financial fraud detection, and drug discovery. Prior research primarily concentrated on improving prediction accuracy while overlooking how reliable the model predictions are. Conformal prediction on graphs emerges as a promising solution, offering statistically sound uncertainty estimates with a pre-defined coverage level. Despite the promising progress, existing works only focus on achieving model coverage guarantees without considering fairness in the coverage within different demographic groups. To bridge the gap between conformal prediction and fair coverage across different groups, we pose the fundamental question: Can fair GNNs enable the uncertainty estimates to be fairly applied across demographic groups? To answer this question, we provide a comprehensive analysis of the uncertainty estimation in fair GNNs employing various strategies. We prove theoretically that fair GNNs can enforce consistent uncertainty bounds across different demographic groups, thereby minimizing bias in uncertainty estimates. Furthermore, we conduct extensive experiments on five commonly used datasets across seven state-of-the-art fair GNN models to validate our theoretical findings. Additionally, based on the theoretical and empirical insights, we identify and analyze the key strategies from various fair GNN models that contribute to ensuring equalized uncertainty estimates. Our work estimates a solid foundation for future exploration of the practical implications and potential adjustments needed to enhance fairness in GNN applications across various domains.
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Diffusion-based recommender systems (DR) have gained increasing attention for their advanced generative and denoising capabilities. However, existing DR face two central limitations: (i) a trade-off between enhancing generative capacity via noise injection and retaining the loss of personalized information. (ii) the underutilization of rich item-side information. To address these challenges, we present a Collaborative Diffusion model for Recommender System (CDiff4Rec). Specifically, CDiff4Rec generates pseudo-users from
item features and leverages collaborative signals from both real and pseudo personalized neighbors identified through behavioral similarity, thereby effectively reconstructing nuanced user preferences. Experimental results on three public datasets show that CDiff4Rec outperforms competitors by effectively mitigating the loss of personalized information through the integration of item content and collaborative signals.
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