Machine Learning Seminar by Pat Langley – Computational Scientific Discovery: Heuristic Search for Communicable Laws and Models

  Short Bio Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise and as a Research Scientist at Stanford University's Center for Design Research. He has contributed to AI and cognitive...

ML Student Seminar on Dec 2 2021: Adversarial Learning on Time Series Forecasting

Where and when: Thursday, Dec 2 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Mark Chen, supervised by Joerg Wicker and Gill Dobbie Abstract: Time series forecasting is one of the main topics which involves time series data. It predicts...

AI Reading Group on Nov 25 2021: The neural architecture of language: Integrative modeling converges on predictive processing

Where and when: Thursday, Nov 25 at 2-3pm in Google Meet (see the calendar invite for the link). Abstract The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are...

ML Student Seminar on Nov 18 2021: Beyond Recall: Teaching High Quality Language Models to Generalise to Unseen Compositional Questions

Where and when: Thursday, Nov 18 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Tim Hartill, supervised by Michael Witbrock and Pat Riddle Abstract: Sequence-to-sequence Transformer-based Language Models pretrained on large text corpora...

Machine Learning Seminar by Zachary Lipton – RATT: Leveraging Unlabeled Data to Guarantee Generalization

  Short Bio Zachary Chase Lipton is the BP Junior Chair Assistant Professor of Operations Research and Machine Learning at Carnegie Mellon University and a Visiting Scientist at Amazon AI. He directs the Approximately Correct...

ML Student Seminar on Nov 4 2021: New Zealand Bat Audio Analysis

Where and when: Thursday, Nov 4 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Tsz Fung Ip (Roy), Master of Data Science student Abstract: Due to urbanization in the past few decades, various species of bats in New Zealand are facing...

AI Reading Group on Oct 28 2021: The Computational Gauntlet of Human-Like Learning

Where and when: Thursday, Oct 28 at 2-3pm in 303S-561 or Google Meet (see the calendar invite for the link). Abstract In this paper, we pose a challenge for AI researchers: to develop systems that learn in a human-like manner. We briefly review the history of machine...

ML Student Seminar on Oct 21 2021: Understanding the mechanisms of multiple epidemic waves of COVID-19

Where and when: Thursday, Oct 21 at 2-3pm in Google Meets (see the calendar invitation for the link) Speaker: Johnny Zhu, supervised by Joerg Wicker and Gill Dobbie Abstract: Covid-19 has been raging around the world for more than 20 months. The epidemic has occurred...

Machine Learning Seminar by Harris Lin – Beyond accuracy: what should you ask from a predictive model?

  Short Bio Harris Lin is a data scientist from Plant and Food Research, whose team has expertise in computer vision technologies, signal processing from smart sensors, natural language processing, data-driven consumer insights,...

AI Reading Group on Oct 14 2021: A Survey of Heterogeneous Information Network Analysis

Where and when: Thursday, Oct 14 at 2-3pm in Google Meet (see the calendar invite for the link) or 303S-561 if possible. Abstract Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as...

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...