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Found 24 papers, 17 papers with code
Stochastic Gradient Descent for Gaussian Processes Done Right
We study the optimisation problem associated with Gaussian process regression using squared loss.
The Cambridge Law Corpus: A Dataset for Legal AI Research
We introduce the Cambridge Law Corpus (CLC), a dataset for legal AI research.
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Gaussian processes are used in many machine learning applications that rely on uncertainty quantification.
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute paths in high-dimensional state spaces while accounting for physical constraints related to motors and joints, generating smooth and stable motions, avoiding obstacles, and preventing collisions.
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Gaussian processes are a powerful framework for quantifying uncertainty and for sequential decision-making but are limited by the requirement of solving linear systems.
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
The invariance of a Gaussian process' covariance to such symmetries gives rise to the most natural generalization of the concept of stationarity to such spaces.
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
The invariance of a Gaussian process' covariance to such symmetries gives rise to the most natural generalization of the concept of stationarity to such spaces.
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces
In this dissertation, we develop techniques for broadening the applicability of Gaussian processes.
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Bayesian optimization is a data-efficient technique which can be used for control parameter tuning, parametric policy adaptation, and structure design in robotics.
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Gaussian processes are machine learning models capable of learning unknown functions in a way that represents uncertainty, thereby facilitating construction of optimal decision-making systems.
Learning Contact Dynamics using Physically Structured Neural Networks
Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics.
Sliced Multi-Marginal Optimal Transport
To construct this distance, we introduce a characterization of the one-dimensional multi-marginal Kantorovich problem and use it to highlight a number of properties of the sliced multi-marginal Wasserstein distance.
Pathwise Conditioning of Gaussian Processes
As Gaussian processes are used to answer increasingly complex questions, analytic solutions become scarcer and scarcer.
Matérn Gaussian Processes on Graphs
Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information about their properties.
Aligning Time Series on Incomparable Spaces
Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in comparable spaces.
Matérn Gaussian processes on Riemannian manifolds
Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing predictive uncertainty is of key importance.
Efficiently Sampling Functions from Gaussian Process Posteriors
Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model's success hinges upon its ability to faithfully represent predictive uncertainty.
Variational Integrator Networks for Physically Structured Embeddings
Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas.
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
To scale non-parametric extensions of probabilistic topic models such as Latent Dirichlet allocation to larger data sets, practitioners rely increasingly on parallel and distributed systems.
Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods
Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are finding widespread use in applied statistics and machine learning.
Pólya Urn Latent Dirichlet Allocation: a doubly sparse massively parallel sampler
We conclude by comparing the performance of our algorithm with that of other approaches on well-known corpora.
GPU-accelerated Gibbs sampling: a case study of the Horseshoe Probit model
Gibbs sampling is a widely used Markov chain Monte Carlo (MCMC) method for numerically approximating integrals of interest in Bayesian statistics and other mathematical sciences.
Computation Distributed, Parallel, and Cluster Computing
Asynchronous Gibbs Sampling
We introduce a theoretical framework for analyzing asynchronous Gibbs sampling and other extensions of MCMC that do not possess the Markov property.
Computation
