Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Deep learning has been the mainstream technique in natural language processing (NLP)
area. However, the techniques require many labeled data and are less generalizable across …

Given a long list of anomaly detection algorithms developed in the last few decades, how do
they perform with regard to (i) varying levels of supervision,(ii) different types of anomalies …

Supervised deep learning methods require a large repository of annotated data; hence,
label noise is inevitable. Training with such noisy data negatively impacts the generalization …

Abstract Setting up a well-designed reward function has been challenging for many
reinforcement learning applications. Preference-based reinforcement learning (PbRL) …

Despite its flexibility to learn diverse inductive biases in machine learning programs, meta
learning (ie,\learning to learn) has long been recognized to suffer from poor scalability due …

Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

Learning with noisy labels (LNL) aims to ensure model generalization given a label-
corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine …

Abstract Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen
target data with a few labeled and lots of unlabeled target data, along with many labeled …

Few-shot learning (FSL) methods typically assume clean support sets with accurately
labeled samples when training on novel classes. This assumption can often be unrealistic …