14 citations · 15 across the 5 of their papers we have counts for
5 papers
On semi-supervised estimation using exponential tilt mixture models
Ye Tian, Xinwei Zhang, Zhiqiang Tan
Consider a semi-supervised setting with a labeled dataset of binary responses and predictors and an unlabeled dataset with only the predictors. Logistic regression is equivalent to…
Persistently Trained, Diffusion-assisted Energy-based Models
Xinwei Zhang, Zhiqiang Tan, Zhijian Ou
Maximum likelihood (ML) learning for energy-based models (EBMs) is challenging, partly due to non-convergence of Markov chain Monte Carlo.Several variations of ML learning have bee…
Grow and Merge: A Unified Framework for Continuous Categories Discovery
Xinwei Zhang, Jianwen Jiang, Yutong Feng +6
Although a number of studies are devoted to novel category discovery, most of them assume a static setting where both labeled and unlabeled data are given at once for finding new c…
On loss functions and regret bounds for multi-category classification
Zhiqiang Tan, Xinwei Zhang
We develop new approaches in multi-class settings for constructing proper scoring rules and hinge-like losses and establishing corresponding regret bounds with respect to the zero-…
Semi-supervised Logistic Learning Based on Exponential Tilt Mixture Models
Xinwei Zhang, Zhiqiang Tan
Consider semi-supervised learning for classification, where both labeled and unlabeled data are available for training. The goal is to exploit both datasets to achieve higher predi…