Next ML Student Seminar on June 17 2021: Defining Explainability Via Mental Models

Where and when: Thursday, June 17 at 2-3pm in 303S-561 Speaker: Mike Merry, PhD student, supervised by Pat Riddle, and Jim Warren Abstract: Wide-ranging concerns exist regarding the use of black-box artificial intelligence (AI) in sensitive contexts. Despite...

Machine Learning Seminar by Jan N. van Rijn – Automating the Data Science Pipeline: AutoML, Meta-learning and OpenML

RSVP If you want to join us for this seminar, please leave your name and email below and indicate if you plan to join on campus or via Zoom.

AI Reading Group 06/10/21: Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks

Where and when: Thursday, June 10 at 2-3pm in 303S-561 Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transferability has been shown in previous...

ML Student Seminar June 3 2021: Data has shape and shape has meaning

Where and when: Thursday, June 3 at 2-3pm in 303S-561 Speaker: Neset Tan, PhD student, supervised by Mark Gahegan and Michael Witbrock Abstract: Geometric and topological tools can be effective for understanding complex data. Topological data analysis is a young and...

AI Reading Group 05/27/21: Evaluating Saliency Methods for Neural Language Models

Saliency methods are widely used to interpret neural network predictions, but different variants of saliency methods often disagree even on the interpretations of the same prediction made by the same model. In these cases, how do we identify when are these...

ML Student Seminar May 20 2021: Multivariate Sequential Analytics for Long-term Chronic Condition Management

Speaker: William Hsu, PhD student, supervised by Jim Warren and Pat Riddle Abstract: Chronic conditions place a considerable burden on modern healthcare systems. Within New Zealand and worldwide cardiovascular disease (CVD) affects a significant proportion of the...

AI Reading Group 05/13/21: Active Learning Literature Survey

The key idea behind active learning is that a machine learning algorithm can achieve greater accuracy with fewer training labels if it is allowed to choose the data from which it learns. An active learner may pose queries, usually in the form of unlabeled data...

ML Student Seminar May 6 2021: From Symbolic Logic Reasoning to Soft Reasoning: A Neural-Symbolic Paradigm

Speaker: Qiming Bao, PhD student, supervised by Jiamou Liu and Michael Witbrock Abstract: Combining deep learning with symbolic reasoning aims to capitalize on the success of both fields and is drawing increasing attention. However, it is yet unknown how much symbolic...

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 designing a transmission controller for an engine in my third-year undergraduate project. I was fascinated by how artificial intelligence can be integrated into everyday systems. When I learnt a state-of-the-art machine learning model is vulnerable to malicious attacks, even when trained on a large amount of...

Knowledge-Driven Text Generation

I am Beryl Qi and my research topic is ‘Knowledge-Driven Text Generation’. Natural language generation (NLG) is an important research direction in the field of natural language processing. This technology can be applied to various information processing tasks, such as QA (Question Answering), IE (Information Extraction), PD (Problem Decomposition), etc. The wide application of NLG benefits from its learning and processing capabilities when facing to explosion of data. The overall system we...

Identification and Mitigation of Selection Bias

Kia Ora! I am Katharina Dost, a PhD student in my second year with the School of Computer Science. My research topic is “Identification and Mitigation of Selection Bias” and I would like to use this post to talk about my research and my experiences, so read on! Our world runs on data. We gather whatever we can and use it to answer a variety of questions. These can be something as simple as “What is the average age of my customers?” but also as critical as “Which treatment is best suited for...

University of Auckland Machine Learning Group

Machine Learning is a field of Computer Science that aims to develop algorithms that learn from experience. This is in contrast to traditional computer science algorithms where behavior is directly coded into the algorithms. Machine Learning algorithms take experience in form of data and observe patterns in these data to make decisions and predictions. Machine Learning became widely popular in recent years and Machine Learning methods are widely used, having applications in every industry...