arXiv research

Paper extends GP-SARSA for more efficient reinforcement learning.

RGPs connect predictive coding to Bayesian inference, providing a neural substrate.

Paper proposes a recursive GPSSM for efficient online learning.

New method speeds up Gaussian process regression for large datasets.

New algorithm speeds up online mapping of unknown terrains.

Improved GP models for scalable large data sets.

Unified bounds for iterative algorithms with Gaussian data matrices.

New method speeds up Gaussian process inference for large datasets.

Optimizes Gaussian process hyperparameters using Bayesian autoregression.

Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing th…

Introduces numerical Gaussian process Kalman filtering for infinite-dimensional systems.

Paper develops efficient recursive learning for multi-channel systems with heterogeneous dynamics.

The paper links deep neural networks to Gaussian processes, showing convergence under certain conditions.

Model collapse occurs quickly for synthetic data generated by previous models.

New Riemannian geometry for Compound Gaussian distributions applied to efficient change detection.

Theoretical study on AI models' resilience to data contamination during recursive training.

Ens-CGP synthesizes ensemble-based inference with Gaussian processes.

Sparse Markovian Gaussian processes improve probabilistic model inference for large datasets.

Recursive KalmanNet combines neural networks with Kalman filters for precise state estimation.

Approximates option prices in Barndorff-Nielsen and Shephard models using Taylor expansion.

Extends GP models for sequential data, scalable and robust.

Extends DRFGP to make GPs more robust and adaptive for dynamic, noisy data.

We use the explicit relation between genus filtrated $s$-loop means of the Gaussian matrix model and terms of the genus expansion of the Kontsevich--Penner matrix model (KPMM), which is the generating function for volumes of discretized (open) moduli spaces $M_{g,s}^{disc}$ (discrete volumes), to express Gaussian means…

Recursive training of generative models can lead to model collapse, and the recursion converges to a unique limiting distribution.

Introduces a new theoretical framework for exponential smoothing.

A novel optimization-based Gaussian mixture reduction method using composite transportation divergence.

Stable processes emerge as limits of deep neural networks with symmetric stable distributions.

To scale Gaussian processes (GPs) to large data sets we introduce the robust Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts model for large-scale distributed GP regression. Unlike state-of-the-art sparse GP approximations, the rBCM is conceptually simple and does not rely on inducing or …

We develop a sequential low-complexity inference procedure for Dirichlet process mixtures of Gaussians for online clustering and parameter estimation when the number of clusters are unknown a-priori. We present an easily computable, closed form parametric expression for the conditional likelihood, in which hyperparamet…

Enhances Gaussian process regression with multi-fidelity models and active subspaces for high-dimensional problems.

We revisit the development of grid based recursive approximate filtering of general Markov processes in discrete time, partially observed in conditionally Gaussian noise. The grid based filters considered rely on two types of state quantization: The \textit{Markovian} type and the \textit{marginal} type. We propose a s…

The paper studies risk-sensitive MDPs with recursive risk measures.

Efficiently learns Gaussian and Boolean hypercube distributions privately.

Bayesian deep neural networks converge to processes with α-stable marginals under infinite variance weights.

Study adds investment gains and losses to recursive utility model, proving existence and uniqueness of utility process.

Warped DLMs improve forecasting for count time series.

New method tackles model uncertainty in stochastic control using Bayesian nonparametrics.

A scalable GP model for online uncertainty quantification over graphs.

In this paper we demonstrate that tempering Markov chain Monte Carlo samplers for Bayesian models by recursively subsampling observations without replacement can improve the performance of baseline samplers in terms of effective sample size per computation. We present two tempering by subsampling algorithms, subsampled…

This paper refines the Gaussian Sinkhorn algorithm for general multivariate models.

Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architectur…

The paper presents a novel approach to multi-output regression using probabilistic circuits.

This paper removes the finite variance assumption for deep convolutional neural networks.

Tab-TRM uses recursive model for insurance pricing on tabular data.

Method calculates financial distributions using recursive relationships.

The paper develops a method to estimate conditional survival probabilities under noisy firm value data.

GP-PSRL achieves sublinear regret for continuous control with unbounded state space.

Deep learning solves dynamic programming with recursive utility.