Search Results for author: Ximing Li

Found 8 papers, 2 papers with code

Statistical Theory of Differentially Private Marginal-based Data Synthesis Algorithms

no code implementations21 Jan 2023 Ximing Li, Chendi Wang, Guang Cheng

To complete the picture, we establish a lower bound for TV accuracy that holds for every $\epsilon$-DP synthetic data generator.

Learning with Partial Labels from Semi-supervised Perspective

1 code implementation24 Nov 2022 Ximing Li, Yuanzhi Jiang, Changchun Li, Yiyuan Wang, Jihong Ouyang

Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP).

Contrastive Learning Partial Label Learning

A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based Sentiment Analysis

1 code implementation COLING 2022 Bing Wang, Liang Ding, Qihuang Zhong, Ximing Li, DaCheng Tao

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, which focuses on detecting the sentiment polarity towards the aspect in a sentence.

Aspect-Based Sentiment Analysis (ABSA) Contrastive Learning +2

Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals

no code implementations20 Nov 2021 Bing Wang, Yue Wang, Ximing Li, Jihong Ouyang

The recent generative dataless methods construct document-specific category priors by using seed word occurrences only, however, such category priors often contain very limited and even noisy supervised signals.

text-classification Text Classification +1

Variational Wasserstein Barycenters with c-Cyclical Monotonicity

no code implementations22 Oct 2021 Jinjin Chi, Zhiyao Yang, Jihong Ouyang, Ximing Li

The basic idea is to introduce a variational distribution as the approximation of the true continuous barycenter, so as to frame the barycenters computation problem as an optimization problem, where parameters of the variational distribution adjust the proxy distribution to be similar to the barycenter.

Stochastic Optimization

Who Is Your Right Mixup Partner in Positive and Unlabeled Learning

no code implementations ICLR 2022 Changchun Li, Ximing Li, Lei Feng, Jihong Ouyang

In this paper, we propose a novel PU learning method, namely Positive and unlabeled learning with Partially Positive Mixup (P3Mix), which simultaneously benefits from data augmentation and supervision correction with a heuristic mixup technique.

Data Augmentation

Semi-Supervised Text Classification with Balanced Deep Representation Distributions

no code implementations ACL 2021 Changchun Li, Ximing Li, Jihong Ouyang

They initialize the deep classifier by training over labeled texts; and then alternatively predict unlabeled texts as their pseudo-labels and train the deep classifier over the mixture of labeled and pseudo-labeled texts.

Semi-Supervised Text Classification text-classification

Cannot find the paper you are looking for? You can Submit a new open access paper.