arXiv research

Characterizes measures preserving compound mixed renewal process properties.

Study online learning in RKHS with dependent processes, focusing on \(β\)- and \(φ\)-mixing.

This paper investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by \emph{general}, we mean that many stationary stochastic processes can be included. We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a u…

Paper reduces hyperparameters in mixed-categorical Gaussian processes for green aircraft optimization.

We study a special case of the problem of statistical learning without the i.i.d. assumption. Specifically, we suppose a learning method is presented with a sequence of data points, and required to make a prediction (e.g., a classification) for each one, and can then observe the loss incurred by this prediction. We go …

Simple algorithm gives optimal regret bounds for reinforcement learning.

New RL theory reduces sample complexity for mixing MDPs.

The literature on statistical learning for time series assumes the asymptotic independence or ``mixing' of the data-generating process. These mixing assumptions are never tested, nor are there methods for estimating mixing rates from data. We give an estimator for the $β$-mixing rate based on a single stationary sample…

Flexible nonparametric model for discrete choice analysis.

New model handles mixed data types better.

Bayesian optimization tackles mixed discrete-continuous problems with Gaussian processes.

Bayesian test assesses dependence between mixed data types.

The paper explores how mixing and diffusion mechanisms can enhance privacy in data processing.

Mixed likelihood GPs improve model performance in human-in-the-loop experiments.

New GP kernel handles mixed-categorical data, improving model accuracy.

Unsupervised ML method reveals hidden features in reactive-diffusion simulations.

Bayesian machine learning methods improve nowcasting with mixed frequency data.

Bayesian optimization reduces hyperparameters for mixed variable design problems.

New algorithm learns optimal policy for average reward MDPs with sample complexity matching lower bound.

A new data augmentation method selects mixed classes based on class distances for better performance.

Study predicts Gaussian Volterra processes with noisy Brownian motion.

Study on gradient descent in Hilbert spaces with Markov chains, focusing on mixing coefficients.

We introduce a novel multivariate random process producing Bernoulli outputs per dimension, that can possibly formalize binary interactions in various graphical structures and can be used to model opinion dynamics, epidemics, financial and biological time series data, etc. We call this a Bernoulli Autoregressive Proces…

Adapts Bayesian optimization for mixed constraints in aircraft design.

Paper extends nonparametric regression bounds for dependent $\beta$-mixing samples.

GP-MRO discovers robust mixed strategies for unknown objectives.

Combines boosting with Gaussian process and mixed effects models.

Deep normative modeling of clinical neuroimaging data improves diagnostic performance.

A hybrid model for Bayesian optimization handles mixed variables using MCTS for categorical and GP for continuous.

Improves model predictability by mixing forecasts and orthogonalizing models.

The paper analyzes convergence rates of Langevin dynamics and Proximal Sampler using $Φ$-divergence.

Research tackles novelty detection for mixed-type data, proposing probabilistic methods.

In this short paper, in order to price occupation-time options, such as (double-barrier) step options and quantile options, we derive various joint distributions of a mixed-exponential jump-diffusion process and its occupation times of intervals.

Flexible non-stationary modeling of spatial outcomes using a mixed-stationary Gaussian process.

Rapid mixing of Langevin dynamics on Riemannian manifolds

Bayesian Optimization framework tackles materials design challenges with mixed variables.

Estimates mixing coefficients of geometrically ergodic Markov processes from a single sample path.

WE constructs GP kernels for mixed inputs using weighted EDMs.

Bayesian optimization with binary auxiliary info for faster target function optimization.

New method separates mixed distributions without requiring samples of each source.

A new method for Gaussian Processes handles mixed continuous and categorical inputs.

We investigate the systematic mechanism for designing fast mixing Markov chain Monte Carlo algorithms to sample from discrete point processes under the Dobrushin uniqueness condition for Gibbs measures. Discrete point processes are defined as probability distributions $μ(S)\propto \exp(βf(S))$ over all subsets $S\in 2^…

The paper studies convergence of kernel autocovariance operators for stationary processes.

We introduce a general framework of the Mixed-correlated ARFIMA (MC-ARFIMA) processes which allows for various specifications of univariate and bivariate long-term memory. Apart from a standard case when $H_{xy}={1}{2}(H_x+H_y)$, MC-ARFIMA also allows for processes with $H_{xy}<{1}{2}(H_x+H_y)$ but also for long-range …

Develops a deep learning framework for various data types.

Method generates i.i.d. samples from GT data using space-time mixing.

Particle MCMC involves using a particle filter within an MCMC algorithm. For inference of a model which involves an unobserved stochastic process, the standard implementation uses the particle filter to propose new values for the stochastic process, and MCMC moves to propose new values for the parameters. We show how p…

This paper stidies the first passage times to constant boundaries for mixed-exponential jump diffusion processes. Explicit solutions of the Laplace transforms of the distribution of the first passage times, the joint distribution of the first passage times and undershoot (overshoot) are obtained. As applications, we pr…