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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.
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).
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).
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.
