163 citations · 1.2k across the 128 of their papers we have counts for
78 papers · 1 filter
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
Haoran Liu, Bokun Wang, Jianling Wang +3
As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation system…
Stability and Generalization of Stochastic Compositional Gradient Descent Algorithms
Ming Yang, Xiyuan Wei, Tianbao Yang +1
Many machine learning tasks can be formulated as a stochastic compositional optimization (SCO) problem such as reinforcement learning, AUC maximization, and meta-learning, where th…
Learning Unnormalized Statistical Models via Compositional Optimization
Wei Jiang, Jiayu Qin, Lingyu Wu +3
Learning unnormalized statistical models (e.g., energy-based models) is computationally challenging due to the complexity of handling the partition function. To eschew this complex…
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
Dixian Zhu, Bokun Wang, Zhi Chen +4
This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e.g., mu…
LibAUC: A Deep Learning Library for X-Risk Optimization
Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu +3
This paper introduces the award-winning deep learning (DL) library called LibAUC for implementing state-of-the-art algorithms towards optimizing a family of risk functions named X-…
Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization
Zi-Hao Qiu, Quanqi Hu, Zhuoning Yuan +3
In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning. The common practice of usi…