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Found 8 papers, 1 papers with code
LeanML: A Design Pattern To Slash Avoidable Wastes in Machine Learning Projects
We illustrate the efficacy of the LeanML design pattern on a wide range of regression and classification problems, synthetic and real-life.
MIND: Inductive Mutual Information Estimation, A Convex Maximum-Entropy Copula Approach
We propose a novel estimator of the mutual information between two ordinal vectors $x$ and $y$.
Stochastic Portfolio Theory: A Machine Learning Perspective
In this paper we propose a novel application of Gaussian processes (GPs) to financial asset allocation.
p-Markov Gaussian Processes for Scalable and Expressive Online Bayesian Nonparametric Time Series Forecasting
In this paper we introduce a novel online time series forecasting model we refer to as the pM-GP filter.
String and Membrane Gaussian Processes
In particular, we prove that some string GPs are Gaussian processes, which provides a complementary global perspective on our framework.
Generalized Spectral Kernels
In this paper we propose a family of tractable kernels that is dense in the family of bounded positive semi-definite functions (i. e. can approximate any bounded kernel with arbitrary precision).
String Gaussian Process Kernels
We introduce a new class of nonstationary kernels, which we derive as covariance functions of a novel family of stochastic processes we refer to as string Gaussian processes (string GPs).
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
In this paper we propose the first non-parametric Bayesian model using Gaussian Processes to make inference on Poisson Point Processes without resorting to gridding the domain or to introducing latent thinning points.
