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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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This project focuses on developing algorithms capable of automatically identifying and categorizing mobile ringtones. This involves leveraging machine learning techniques to analyze audio signals
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We are excited to offer a fully funded PhD position at the Faculty of Engineering, Monash University (Australia). This project focuses on developing new algorithms to equip social robots with
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. With the widespread adoption of ML algorithms for data analysis and decision-making, preserving the privacy of individuals' data has become a paramount concern. The project focuses on exploring
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formula is true or false (EXPTIME vs NP). Can we develop and implement efficient algorithms for this problem? This problem has been attacked using multiple different methods for the past 40 years, without
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the given non-classical logic. The proof of the claim contains an algorithm for deciding whether an arbitrary formula is true or else false! This proof can then be exported automatically to produce a formally
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This project aims to employ advanced machine learning techniques to analyse text, audio, images, and videos for signs of harmful behaviour. Natural language processing algorithms are utilized
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The relationship between the information-theoretic Bayesian minimum message length (MML) principle and the notion of Solomonoff-Kolmogorov complexity from algorithmic information theory (Wallace and
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. Wallace (1996). MML estimation of the parameters of the spherical Fisher Distribution. In S. Arikawa and A. K. Sharma (eds.) , Proc. 7th International Workshop on Algorithmic Learning Theory (ALT'96
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the different actors' beliefs and intentions. We will study the properties of such explanations, present algorithms for automatically computing them as well as extensions to existing frameworks and evaluate