1 citations · 1 across the 2 of their papers we have counts for
2 papers
math.OC2026
Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity
Boris Prokhorov, Semyon Chebykin, Alexander Gasnikov +1
This paper deals with stochastic optimization problems involving Markovian noise with a zero-order oracle. We present and analyze a novel derivative-free method for solving such pr…
cs.LG2023★ 1 cited
Correcting sampling biases via importance reweighting for spatial modeling
Boris Prokhorov, Diana Koldasbayeva, Alexey Zaytsev
In machine learning models, the estimation of errors is often complex due to distribution bias, particularly in spatial data such as those found in environmental studies. We introd…