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Found 10 papers, 2 papers with code
Transfer-Learning Across Datasets with Different Input Dimensions: An Algorithm and Analysis for the Linear Regression Case
On the other hand, combining these new inputs with historical data remains a challenge that has not yet been studied in enough detail.
Optimal high-dimensional and nonparametric distributed testing under communication constraints
We derive minimax testing errors in a distributed framework where the data is split over multiple machines and their communication to a central machine is limited to $b$ bits.
Optimal distributed composite testing in high-dimensional Gaussian models with 1-bit communication
In this paper we study the problem of signal detection in Gaussian noise in a distributed setting where the local machines in the star topology can communicate a single bit of information.
Distributed function estimation: adaptation using minimal communication
We investigate whether in a distributed setting, adaptive estimation of a smooth function at the optimal rate is possible under minimal communication.
Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularization
This article describes an implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs.
Adaptive distributed methods under communication constraints
We study distributed estimation methods under communication constraints in a distributed version of the nonparametric random design regression model.
An asymptotic analysis of distributed nonparametric methods
We investigate and compare the fundamental performance of several distributed learning methods that have been proposed recently.
Nonparametric Bayesian label prediction on a graph
An implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs is described.
Optimality of Poisson processes intensity learning with Gaussian processes
In this paper we provide theoretical support for the so-called "Sigmoidal Gaussian Cox Process" approach to learning the intensity of an inhomogeneous Poisson process on a $d$-dimensional domain.
Guided proposals for simulating multi-dimensional diffusion bridges
A Monte Carlo method for simulating a multi-dimensional diffusion process conditioned on hitting a fixed point at a fixed future time is developed.
Probability 60J60 (Primary), 65C30 (Secondary), 65C05
