|
Thao Le Email / Google Scholar / DBLP / ORCID / Github / X (Twitter) |
|
I am a PhD Candidate in Explainable AI (XAI) in the AI and
Autonomy Lab,
School of Computing and Information Systems, The
University of Melbourne, under the supervision of
Tim Miller (primary supervisor),
Liz Sonenberg
and
Ronal Singh. My research interests include explainable AI,
human-computer interaction, human-centered decision
support and human-AI planning.
I apply mixed methods in my research by using machine
learning techniques, designing/conducting behavioural
experiments and doing qualitative/quantitative data
analysis. My skills include (1) Python, PyTorch,
Scikit-learn, Pandas, (2) machine learning/reinforcement
learning techniques, (3) high performance computing (HPC),
(4) building web-based applications for human-AI
interaction.
|
[ECAI 2024] Towards the New XAI: A Hypothesis-Driven
Approach to Decision Support Using Evidence
Thao Le, Tim Miller, Liz Sonenberg, Ronal Singh European Conference on Artificial Intelligence (ECAI). Main Track. Acceptance Rate: 547/2,344 (23%). Santiago de Compostela. Spain. [Paper]
Describing and evaluating an approach for
hypothesis-driven XAI based on the Weight of Evidence
(WoE) framework.
|
|
[AAAI 2023] Explaining Model Confidence Using
Counterfactuals
Thao Le, Tim Miller, Ronal Singh, Liz Sonenberg Association for the Advancement of Artificial Intelligence (AAAI). Main Track. Acceptance Rate: 1,721/8,777 (19.6%). Washington DC, USA. [Paper]
Formalising counterfactual explanation of confidence and
showing that this explanation can help users better
understand and trust the model.
|
|
[JAIR 2021] Goal Recognition for Deceptive Human Agents
through Planning and Gaze
Thao Le, Ronal Singh, Tim Miller Journal of Artificial Intelligence Research. IJCAI 2022 Journal Track. [Paper]
Presenting new models for goal recognition under deception
using a combination of gaze behaviour and observed
movements of the agent.
|
|
The original source code of this website is from here. |