Real-Time Density and Mode Estimation With Application to Time-Dynamic Mode Tracking

Hall, Peter; Müller, Hans‐Georg; Wu, Ping-Shi

doi:10.1198/106186006x94333

Cited by 9 publications

(10 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…which is based on [11] and will be used as a prior in order to model a time-variant distribution on the solved tasks. The factor a 0 (−ρ) = 1 −ρ K T (t)dt is used to perform the boundary correction, recovering consistency at the boundaries [13], therefore also in t = 1.…”

Section: Time-variant Kernel Density Estimation For Variational Transfermentioning

confidence: 99%

Time-Variant Variational Transfer for Value Functions

Canonaco,

Soprani,

Roveri

et al. 2020

Preprint

View full text Add to dashboard Cite

In most of the transfer learning approaches to reinforcement learning (RL) the distribution over the tasks is assumed to be stationary. Therefore, the target and source tasks are i.i.d. samples of the same distribution. In the context of this work, we consider the problem of transferring value functions through a variational method when the distribution that generates the tasks is time-variant, proposing a solution that leverages this temporal structure inherent in the task generating process. Furthermore, by means of a finite-sample analysis, the previously mentioned solution is theoretically compared to its time-invariant version. Finally, we will provide an experimental evaluation of the proposed technique with three distinct temporal dynamics in three different RL environments. * equal contribution Preprint. Under review.

show abstract

Section: Time-variant Kernel Density Estimation For Variational Transfermentioning

confidence: 99%

Time-Variant Variational Transfer for Value Functions

Canonaco,

Soprani,

Roveri

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…However, the case of nonstationary processes has been rarely touched. Hall, Müller and Wu (2006) considered the situation that the underlying distribution evolves with time and proposed a nonparametric time-dynamic density estimator. Assuming independence, they proved the consistency of their kernel-type estimators and applied the results to fast mode tracking.…”

Section: Introduction Consider the Time-varying Regression Modelmentioning

confidence: 99%

“…Assuming independence, they proved the consistency of their kernel-type estimators and applied the results to fast mode tracking. Following the spirit of Hall, Müller and Wu (2006), Vogt (2012) considered a kernel estimator of the time-varying regression model (1.1), and established its asymptotic normality and uniform bound under the classical strong mixing conditions. In Sections 3.1 and 3.2, we advance the nonparametric estimation theory for the TIME-VARYING NONLINEAR REGRESSION MODELS 3 time-varying regression model (1.1) under the framework of Draghicescu, Guillas and Wu (2009), which is convenient to use and often leads to optimal asymptotic results.…”

Section: Introduction Consider the Time-varying Regression Modelmentioning

confidence: 99%

See 1 more Smart Citation

Time-varying nonlinear regression models: Nonparametric estimation and model selection

Zhang¹,

Wu²

2015

Ann. Statist.

View full text Add to dashboard Cite

This paper considers a general class of nonparametric time series regression models where the regression function can be timedependent. We establish an asymptotic theory for estimates of the time-varying regression functions. For this general class of models, an important issue in practice is to address the necessity of modeling the regression function as nonlinear and time-varying. To tackle this, we propose an information criterion and prove its selection consistency property. The results are applied to the U.S. Treasury interest rate data.

show abstract

“…For example, Wegman and Marchette (2003) proposed recursive algorithms and evolutionary graphics focusing on Internet traffic data. Also, Hall, Müller, and Wu (2006) introduced a nonparametric time-dynamic kernel type density estimate that is capable of capturing changing trends when multivariate distribution evolves with time. The proposed method in this article aims to solve the problem as it naturally emerged from the machine learning community, but its statistical properties are shown here as well.…”

Section: Introductionmentioning

confidence: 99%

Model Averaging via Penalized Regression for Tracking Concept Drift

Yeon

Song

Kim

et al. 2010

Journal of Computational and Graphical Statistics

View full text Add to dashboard Cite

A supervised learning algorithm aims to build a prediction model using training examples. This paradigm typically has the assumptions that the underlying distribution and the true input-output dependency do not change. However, these assumptions often fail to hold, especially in data streams. This phenomenon is known as concept drift.We propose a new model combining algorithm for tracking concept drift in data streams. The final predictive ensemble model has a form of a weighted average and ridge regression combiner. The coefficients of the combiner are determined by ridge regression with the constraints such that the coefficients are nonnegative and sum to 1. The proposed algorithm is devised via a new measure of concept drift, the angle between the estimated weights from data and the optimal weight vector obtained under no concept drift. It is shown that the ridge tuning parameter plays a crucial role of forcing the proposed algorithm to adapt to concept drift. Our main findings include (i) the proposed algorithm can achieve the optimal weights in the case of no concept drift if the tuning parameter is sufficiently large, and (ii) the angle is monotonically increasing as the tuning parameter decreases. These imply that if the tuning parameter is wellcontrolled, the algorithm can produce weights which reflect the degree of concept drift measured by the angle. Using various numerical examples, it is shown that the proposed algorithm can track concept drift better than other existing ensemble methods. Supplemental materials, computer code and R-package, are available online.

show abstract

Real-Time Density and Mode Estimation With Application to Time-Dynamic Mode Tracking

Cited by 9 publications

References 31 publications

Time-Variant Variational Transfer for Value Functions

Time-Variant Variational Transfer for Value Functions

Time-varying nonlinear regression models: Nonparametric estimation and model selection

Model Averaging via Penalized Regression for Tracking Concept Drift

Contact Info

Product

Resources

About