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healthcare application needs to analyze sensitive patient data across distributed nodes. Researchers and students can explore privacy-preserving algorithms and technologies like federated learning and zero
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, including: asymptotic enumeration of (hyper)graphs with highly irregular degrees; distribution of small subgraphs in random (hyper)graphs with highly irregular degrees, and extensions of these considerations
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derivation of actionable insights. Identify and use suitable technologies, tools and algorithms which can be applied to research/business activities. Work with research group/business area to employ analysis
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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 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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at one time. In non-stationary environments on the other hand, the same algorithms cannot be applied as the underlying data distributions change constantly and the same models are not valid. Hence, we need
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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The existing deep learning based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them on brain EEG time series (65
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challenging data problem. Weak signals from collisions of compact objects can be dug out of noisy time series because we understand what the signal should look like, and can therefore use simple algorithms
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-readable representations, such as distributed representations of text augmented with random noises [1] or unnatural text curated by replacing sensitive tokens with random non-sensitive ones [2]. First, such