arXiv research

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

Proposes GPHMEs using Gaussian processes for hierarchical expert models.

Improved Gaussian process experts model for complex data.

New method aggregates Gaussian experts by detecting conditional independence violations.

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.

A new model for Gaussian process experts tackles scalability and uncertainty issues.

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

Adaptive Bayesian learning aggregates experts to improve performance.

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

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…

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

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…

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…

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…

Personalized model improves ADAS-Cog13 cognitive score forecasting.

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…

A new method for faster prediction in distributed Gaussian processes.

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…

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

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

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…

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

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

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

The study analyzes the convergence rates of Gaussian mixtures of experts.

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…

Paper investigates IRL for learning expert agents' reward functions in LOB dynamics.

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…

Meta-learn sparse Gaussian process inference for faster predictions.

Improved text-conditioned regression using LLMs and diffusion-based neural processes.

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.

PIMA autoencoders discover shared features in multimodal scientific data.

LINTEL improves INTEL's time series prediction by optimizing computation and accuracy.

Efficiently searches through Gaussian process kernels using symbolic representation and Bayesian optimization.

This paper improves prediction in small data sets by eliciting expert knowledge about feature similarities.

A digital twin for multi-scale systems uses physics-based and machine learning models.

The paper integrates AI and expert knowledge to optimize radiotherapy decisions.

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

Estimates parameters in a deviated Gaussian mixture model.

In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregatio…

Online learning of nonstationary functions using Gaussian processes.

Heteroscedastic regression considering the varying noises among observations has many applications in the fields like machine learning and statistics. Here we focus on the heteroscedastic Gaussian process (HGP) regression which integrates the latent function and the noise function together in a unified non-parametric B…

We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumi…

Machine learning algorithms frequently require careful tuning of model hyperparameters, regularization terms, and optimization parameters. Unfortunately, this tuning is often a "black art" that requires expert experience, unwritten rules of thumb, or sometimes brute-force search. Much more appealing is the idea of deve…