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Researcher profile

Heng Huang

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Improved Bilevel Model: Fast and Optimal Algorithm with Theoretical Guarantee

    2020 · arXiv (Cornell University)

    Due to the hierarchical structure of many machine learning problems, bilevel programming is becoming more and more important recently, however, the complicated correlation between the inner and outer problem makes it extremely challenging to solve. …

  2. A Law of Robustness beyond Isoperimetry

    2022 · arXiv (Cornell University)

    We study the robust interpolation problem of arbitrary data distributions supported on a bounded space and propose a two-fold law of robustness. Robust interpolation refers to the problem of interpolating $n$ noisy training data points …

  3. Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets

    2023 · arXiv (Cornell University)

    Despite incredible advances, deep learning has been shown to be susceptible to adversarial attacks. Numerous approaches have been proposed to train robust networks both empirically and certifiably. However, most of them defend against only a …

  4. Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining

    2023 · arXiv (Cornell University)

    Multi-party collaborative training, such as distributed learning and federated learning, is used to address the big data challenges. However, traditional multi-party collaborative training algorithms were mainly designed for balanced data mining tasks and are intended …

  5. Learning with Diversity: Self-Expanded Equalization for Better Generalized Deep Metric Learning

    2023

    Exploring good generalization ability is essential in deep metric learning (DML). Most existing DML methods focus on improving the model robustness against category shift to keep the performance on unseen categories. However, in addition to …

  6. Deep Attributed Network Embedding

    2018

    Network embedding has attracted a surge of attention in recent years. It is to learn the low-dimensional representation for nodes in a network, which benefits downstream tasks such as node classification and link prediction. Most …