Zongben Xu

Model selection of polynomial kernel regression

2015 · arXiv (Cornell University)

Polynomial kernel regression is one of the standard and state-of-the-art learning strategies. However, as is well known, the choices of the degree of polynomial kernel and the regularization parameter are still open in the realm …

Margin Based PU Learning

2018 · Proceedings of the AAAI Conference on Artificial Intelligence

The PU learning problem concerns about learning from positive and unlabeled data. A popular heuristic is to iteratively enlarge training set based on some margin-based criterion. However, little theoretical analysis has been conducted to support …

Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting

2019 · Neural Information Processing Systems

Current deep neural networks(DNNs) can easily overfit to biased training data with corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to alleviate this issue by designing a weighting function mapping from training …

Training Networks in Null Space of Feature Covariance for Continual Learning

2021 · arXiv (Cornell University)

In the setting of continual learning, a network is trained on a sequence of tasks, and suffers from catastrophic forgetting. To balance plasticity and stability of network in continual learning, in this paper, we propose …

LDP-IDS: Local Differential Privacy for Infinite Data Streams

2022 · Proceedings of the 2022 International Conference on Management of Data

Local differential privacy (LDP) is promising for private streaming data collection and analysis. However, existing few LDP studies over streams either apply to finite streams only or may suffer from insufficient protection. This paper investigates …

Variational Reinforcement Learning for Hyper-Parameter Tuning of Adaptive Evolutionary Algorithm

2022 · IEEE Transactions on Emerging Topics in Computational Intelligence

The performance of an evolutionary algorithm (EA) is deeply affected by its control parameter's setting. It has become a trend in recent studies to treat the control parameter as a random variable. In these studies, …

DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning

2023 · PubMed

Meta learning recently has been heavily researched and helped advance the contemporary machine learning. However, achieving a well-performing meta-learning model requires a large amount of training tasks with high-quality meta-data representing the underlying task generalization …