Search Results for author:
Found 8 papers, 4 papers with code
High dimensional change-point detection: a complete graph approach
We propose a complete graph-based, change-point detection algorithm to detect change of mean and variance from low to high-dimensional online data with a variable scanning window.
Manifold-based time series forecasting
Prediction for high dimensional time series is a challenging task due to the curse of dimensionality problem.
Denoising
Time Series Forecasting
Statistics Theory
Statistics Theory
Reinforced optimal control
Least squares Monte Carlo methods are a popular numerical approximation method for solving stochastic control problems.
Adaptive Gradient Descent for Convex and Non-Convex Stochastic Optimization
In this paper we propose several adaptive gradient methods for stochastic optimization.
Optimization and Control
Structure-adaptive manifold estimation
We consider a problem of manifold estimation from noisy observations.
Optimal stopping via reinforced regression
In this note we propose a new approach towards solving numerically optimal stopping problems via reinforced regression based Monte Carlo algorithms.
An adaptive multiclass nearest neighbor classifier
We consider a problem of multiclass classification, where the training sample $S_n = \{(X_i, Y_i)\}_{i=1}^n$ is generated from the model $\mathbb P(Y = m | X = x) = \eta_m(x)$, $1 \leq m \leq M$, and $\eta_1(x), \dots, \eta_M(x)$ are unknown $\alpha$-Holder continuous functions. Given a test point $X$, our goal is to predict its label.
Adaptive Nonparametric Clustering
The idea is to identify the clustering structure by checking at different points and for different scales on departure from local homogeneity.
