Ivor W. Tsang

Masking: A New Perspective of Noisy Supervision

2018 · arXiv (Cornell University)

It is important to learn various types of classifiers given training data with noisy labels. Noisy labels, in the most popular noise model hitherto, are corrupted from ground-truth labels by an unknown noise transition matrix. …

Geometric Active Learning via Enclosing Ball Boundary

2018 · arXiv (Cornell University)

Active Learning (AL) requires learners to retrain the classifier with the minimum human supervisions or labeling in the unlabeled data pool when the current training set is not enough. However, general AL sampling strategies with …

Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning

2019

Graph neural nets are emerging tools to represent network nodes for classification. However, existing approaches typically suffer from two limitations: (1) they only aggregate information from short distance (e.g., 1-hop neighbors) each round and fail …

The Emerging Trends of Multi-Label Learning

2021 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Exabytes of data are generated daily by humans, leading to the growing needs for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is …

Taming Overconfident Prediction on Unlabeled Data from Hindsight

2021 · arXiv (Cornell University)

Minimizing prediction uncertainty on unlabeled data is a key factor to achieve good performance in semi-supervised learning (SSL). The prediction uncertainty is typically expressed as the \emph{entropy} computed by the transformed probabilities in output space. …

XAI Beyond Classification: Interpretable Neural Clustering

2018 · arXiv (Cornell University)

In this paper, we study two challenging problems in explainable AI (XAI) and data clustering. The first is how to directly design a neural network with inherent interpretability, rather than giving post-hoc explanations of a …

Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis

2021 · arXiv (Cornell University)

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment expressed in a review with respect to a given aspect. The core of ABSA is to model the interaction between the context and given aspect to …

Adversarial Learning for Coordinate Regression Through k-Layer Penetrating Representation

2024 · IEEE Transactions on Dependable and Secure Computing

Adversarial attack is a crucial step when evaluating the reliability and robustness of deep neural networks (DNNs) models. Most existing attack approaches apply an end-to-end gradient update strategy to generate adversarial examples for a classification …

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation

2025 · arXiv (Cornell University)

Guided diffusion-model generation is a promising direction for customizing the generation process of a pre-trained diffusion model to address specific downstream tasks. Existing guided diffusion models either rely on training the guidance model with pre-collected …