2 citations · 4 across the 6 of their papers we have counts for
6 papers
Minimax Estimation of Kernel Stein Discrepancy: Trace versus Hilbert-Schmidt Scales
Davit Gogolashvili
Kernel Stein Discrepancy (KSD) compares a sample to a fixed target distribution known only through its score, and is widely used for goodness-of-fit testing, sample quality assessm…
Local regression on path spaces with signature metrics
Christian Bayer, Davit Gogolashvili, Luca Pelizzari
We study nonparametric regression and classification for path-valued data. We introduce a functional Nadaraya-Watson estimator that combines the signature transform from rough path…
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa +2
This paper investigates when the importance weighting (IW) correction is needed to address covariate shift, a common situation in supervised learning where the input distributions…
Estimating the minimizer and the minimum value of a regression function under passive design
Arya Akhavan, Davit Gogolashvili, Alexandre B. Tsybakov
We propose a new method for estimating the minimizer and the minimum value of a smooth and strongly convex regression function from the observations co…
Locally Smoothed Gaussian Process Regression
Davit Gogolashvili, Bogdan Kozyrskiy, Maurizio Filippone
We develop a novel framework to accelerate Gaussian process regression (GPR). In particular, we consider localization kernels at each data point to down-weigh the contributions fro…
Importance Weighting Correction of Regularized Least-Squares for Target Shift
Davit Gogolashvili
Importance weighting is a standard tool for correcting distribution shift, but its statistical behavior under target shift -- where the label distribution changes between training…