arXiv research

New method aggregates Gaussian experts by detecting conditional independence violations.

This work improves Gaussian process inference using mixtures of experts and nested SMC samplers.

A method to select important experts for Gaussian processes to balance computational efficiency and uncertainty quantification.

New method calibrates Gaussian product experts for better predictions.

Proposes GPHMEs using Gaussian processes for hierarchical expert models.

Adaptive Bayesian learning aggregates experts to improve performance.

Solves challenges in estimating parameters of softmax gating Gaussian mixture models.

Paper proposes a novel method to accurately determine the number of experts in Gaussian-gated Gaussian MoE models.

Improved Gaussian process experts model for complex data.

We introduce a framework for analyzing transductive combination of Gaussian process (GP) experts, where independently trained GP experts are combined in a way that depends on test point location, in order to scale GPs to big data. The framework provides some theoretical justification for the generalized product of GP e…

The paper proposes a semi-parametric Bayesian network model using Gaussian Processes and Horseshoe priors.

A new method for sparse Gaussian process regression using correlated experts.

Estimates parameters in a deviated Gaussian mixture model.

Study optimal trading strategies with expert signals in a hidden Gaussian drift market.

Paper introduces deep structured mixtures of Gaussian processes for scalable GP approximations.

A fast method combines deep mixtures of sparse GPs for flexible modeling.

In this work, we propose a generalized product of experts (gPoE) framework for combining the predictions of multiple probabilistic models. We identify four desirable properties that are important for scalability, expressiveness and robustness, when learning and inferring with a combination of multiple models. Through a…

We provide a theoretical treatment of over-specified Gaussian mixtures of experts with covariate-free gating networks. We establish the convergence rates of the maximum likelihood estimation (MLE) for these models. Our proof technique is based on a novel notion of \emph{algebraic independence} of the expert functions. …

Mixtures of experts probabilistically divide the input space into regions, where the assumptions of each expert, or conditional model, need only hold locally. Combined with Gaussian process (GP) experts, this results in a powerful and highly flexible model. We focus on alternative mixtures of GP experts, which model th…

Study examines Lasso performance in high-dimensional MoE models.

Analyzes convergence rates for Gaussian-gated MoE model.

In this note, we present a version of the Thompson sampling algorithm for the problem of online linear generalization with full information (i.e., the experts setting), studied by Kalai and Vempala, 2005. The algorithm uses a Gaussian prior and time-varying Gaussian likelihoods, and we show that it essentially reduces …

This paper investigates a financial market where returns depend on an unobservable Gaussian drift process. While the observation of returns yields information about the underlying drift, we also incorporate discrete-time expert opinions as an external source of information. For estimating the hidden drift it is crucial…

Training Gaussian process-based models typically involves an $O(N^3)$ computational bottleneck due to inverting the covariance matrix. Popular methods for overcoming this matrix inversion problem cannot adequately model all types of latent functions, and are often not parallelizable. However, judicious choice of model…

We present a novel approach for supervised domain adaptation that is based upon the probabilistic framework of Gaussian processes (GPs). Specifically, we introduce domain-specific GPs as local experts for facial expression classification from face images. The adaptation of the classifier is facilitated in probabilistic…

SkyGP improves Gaussian process scalability for real-time learning.

We propose a practical and scalable Gaussian process model for large-scale nonlinear probabilistic regression. Our mixture-of-experts model is conceptually simple and hierarchically recombines computations for an overall approximation of a full Gaussian process. Closed-form and distributed computations allow for effici…

New insights into the top-K sparse softmax gating function for deep learning.

Unified framework for SGMoE resolves estimation and selection issues.

We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12, 18, and 24 months. We start by training a population-level model using multi-modal data …

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

This paper tackles model selection for MoE models in high-dimensional data.

This paper investigates a financial market where stock returns depend on a hidden Gaussian mean reverting drift process. Information on the drift is obtained from returns and expert opinions in the form of noisy signals about the current state of the drift arriving at the jump times of a homogeneous Poisson process. Dr…

A new method for faster prediction in distributed Gaussian processes.

A new method combines Gaussian graphical models for better distributed Gaussian process predictions.

We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims t…

The paper optimizes portfolios in a market with hidden drift and random expert opinions.

PIMA autoencoders discover shared features in multimodal scientific data.

Investigates least squares estimation in deterministic MoE models.

We prove non-asymptotic lower bounds on the expectation of the maximum of $d$ independent Gaussian variables and the expectation of the maximum of $d$ independent symmetric random walks. Both lower bounds recover the optimal leading constant in the limit. A simple application of the lower bound for random walks is an (…

Unified sampling approach for Bayesian imaging problems.

Bayesian algorithms improve online learning with adversaries over infinite action spaces.

New method uses temperature to control sparse MoE convergence rates.

This paper investigates optimal portfolio strategies in a financial market where the drift of the stock returns is driven by an unobserved Gaussian mean reverting process. Information on this process is obtained from observing stock returns and expert opinions. The latter provide at discrete time points an unbiased est…

Mixtures-of-Experts models and their maximum likelihood estimation (MLE) via the EM algorithm have been thoroughly studied in the statistics and machine learning literature. They are subject of a growing investigation in the context of modeling with high-dimensional predictors with regularized MLE. We examine MoE with …

The paper learns compact implicit surface maps from streaming data using an ensemble of sparse Gaussian processes.

A recommender system based on ranks is proposed, where an expert's ranking of a set of objects and a user's ranking of a subset of those objects are combined to make a prediction of the user's ranking of all objects. The rankings are assumed to be induced by latent continuous variables corresponding to the grades assig…

Model accurately gates ocean microbes from high-frequency flow cytometry data.