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Researcher profile

Hongning Wang

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Intent-aware Query Obfuscation for Privacy Protection in Personalized Web Search

    2018

    Modern web search engines exploit users' search history to personalize search results, with a goal of improving their service utility on a per-user basis. But it is this very dimension that leads to the risk …

  2. Graph Structural Attack by Spectral Distance

    2021 · arXiv (Cornell University)

    Graph Convolutional Networks (GCNs) have fueled a surge of interest due to their superior performance on graph learning tasks, but are also shown vulnerability to adversarial attacks. In this paper, an effective graph structural attack …

  3. Knowledge-to-Jailbreak: Investigating Knowledge-driven Jailbreaking Attacks for Large Language Models

    2025

    Large language models (LLMs) have been increasingly applied to various domains, which triggers increasing concerns about LLMs' safety on specialized domains, e.g. medicine. Despite prior explorations on general jailbreaking attacks, there are two challenges for …

  4. Guiding not Forcing: Enhancing the Transferability of Jailbreaking Attacks on LLMs via Removing Superfluous Constraints

    2025 · arXiv (Cornell University)

    Jailbreaking attacks can effectively induce unsafe behaviors in Large Language Models (LLMs); however, the transferability of these attacks across different models remains limited. This study aims to understand and enhance the transferability of gradient-based jailbreaking …

  5. Factorization Bandits for Interactive Recommendation

    2017 · Proceedings of the AAAI Conference on Artificial Intelligence

    We perform online interactive recommendation via a factorization-based bandit algorithm. Low-rank matrix completion is performed over an incrementally constructed user-item preference matrix, where an upper confidence bound based item selection strategy is developed to balance …

  6. Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

    2018

    Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. …