Inspired by the work by Ishida et al. (2023), we propose an estimator

A natural generalization is to replace this input setP of n points by a setP of n sets inX. The distance from such an input setP P to a setC of centers can then be defined as the distance between the closest point-center pair. This problem is calledk-mean for sets; see e.g.

A natural generalization is to replace this input setP of n points by a setP of n sets inX. The distance from such an input setP P to a setC of centers can then be defined as the distance between the closest point-center pair. This problem is calledk-mean for sets; see e.g.

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Continual learning aims to improve the ability of modern learning systems todeal with non-stationary distributions, typically by attempting to learn a seriesof tasks sequentially.

Moreover, we develop stochastic gradient MCMC inference that is scalable to very long multivariate count time series.

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SVR methods use control variates to reduce the variance of the traditional stochastic gradient descent (SGD) estimate f0i(w) of the full gradient f0(w). Control variates are a classical technique for reducing the variance of a stochastic quantity without introducing bias. Say we have some random variable X.

However, for many large-scale problems standard greedy procedures become computationally infeasible and suffer from negligible model change.