Advances in deep reinforcement learning enable better predictions of human behavior in time-continuous tasks. [PDF]
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MonotonicityTest: An R Package for Efficient Nonparametric Monotonicity Testing. [PDF]
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Kernel covariance series smoothing
2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), 2015In this paper, we provide a new viewpoint of sequential random processes of the kind F(x), where x is a multivariate vector of covariates, in terms of a smoothing operation governed by a covariance function. By exploiting the eigenvalues and eigenvectors of the covariance function, we represent the smooth function in terms of an orthogonal series over ...
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On the Smoothness of General Kernels
Canadian Journal of Mathematics, 1966In (3, §2), the writer and F. E. Browder stated briefly, without proof, some results concerning general distribution kernels. It is our aim here to prove and complete those results.The terminology and notations are introduced in §1.In §2 we define the notion of domain of dependence with respect to the kernel Kx,y (Definition 1) as well as the notion of
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Smooth Bayesian Kernel Machines
2005In this paper, we consider the possibility of obtaining a kernel machine that is sparse in feature space and smooth in output space. Smooth in output space implies that the underlying function is supposed to have continuous derivatives up to some order.
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Efficient Density Evaluation for Smooth Kernels
2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), 2018Given a kernel function k(.,.) and a dataset P⊂ R^d, the kernel density function of P at a point xe R^d is equal to KDF_P(x):= 1/|P| Σ_yeP k(x, y). Kernel density evaluation has numerous applications, in scientific computing, statistics, computer vision, machine learning and other fields.
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