-
Adversarial Learning
The objective of adversarial learning is to pinpoint vulnerabilities in machine learning models that traditional testing methods cannot detect. It has proven to be effective in various applications, often centered […]
-
Adversarial Learning: Robust and Reliable Machine Learning Models
Kia Ora! I am Luke Chang and I am passionate about building more reliable machine learning models, an artificial intelligence people can trust. I started my machine learning journey by […]
-
Boolean Matrix Decomposition
The aim of matrix decomposition is to express a provided matrix as the outcome of multiplying two or more factor matrices. In this situation, we possess the Y matrix of […]
-
Cheminformatics
Cheminformatics is a field that combines chemistry and computer science to address challenges in chemistry. Machine learning is a key aspect of cheminformatics, allowing for the processing of large amounts […]
-
Computational Sustainability
Computational sustainability is an interdisciplinary field of sustainability research, including applied science about the research in sustainable solutions and their implementation. Machine Learning and Data Mining is at the center […]
-
Identification and Mitigation of Selection Bias
Kia Ora!I am Katharina Dost, a PhD student with the School of Computer Science. My research topic is “Identification and Mitigation of Selection Bias” and I would like to use […]
-
Reliable Machine Learning
Current Machine Learning model evaluation methods, e.g., the use of test sets, will only detect whether a model’s predictions match the data. They cannot exclude the possibility that both predictions […]
-
Time Series Analysis
Time series analysis focuses on data is a sequence of data points that are collected over time. The data points can be anything that can be measured over time, such […]
You must be logged in to post a comment.