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09/21/2023
The Cambridge Law Corpus: A Corpus for Legal AI Research
We introduce the Cambridge Law Corpus (CLC), a corpus for legal AI resea...
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09/19/2023
Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds
Gaussian processes are used in many machine learning applications that r...
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09/02/2023
A Unifying Variational Framework for Gaussian Process Motion Planning
To control how a robot moves, motion planning algorithms must compute pa...
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06/20/2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Gaussian processes are a powerful framework for quantifying uncertainty ...
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01/30/2023
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces II: non-compact symmetric spaces
Gaussian processes are arguably the most important class of spatiotempor...
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10/14/2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
As Gaussian processes mature, they are increasingly being deployed as pa...
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08/31/2022
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the Compact Case
Gaussian processes are arguably the most important model class in spatia...
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02/22/2022
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces
Bayesian learning using Gaussian processes provides a foundational frame...
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11/02/2021
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels
Bayesian optimization is a data-efficient technique which can be used fo...
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10/27/2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels
Gaussian processes are machine learning models capable of learning unkno...
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02/22/2021
Learning Contact Dynamics using Physically Structured Neural Networks
Learning physically structured representations of dynamical systems that...
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11/08/2020
Pathwise Conditioning of Gaussian Processes
As Gaussian processes are integrated into increasingly complex problem s...
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10/29/2020
Matern Gaussian Processes on Graphs
Gaussian processes are a versatile framework for learning unknown functi...
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06/22/2020
Aligning Time Series on Incomparable Spaces
Dynamic time warping (DTW) is a useful method for aligning, comparing an...
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06/17/2020
Matern Gaussian processes on Riemannian manifolds
Gaussian processes are an effective model class for learning unknown fun...
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02/21/2020
Efficiently sampling functions from Gaussian process posteriors
Gaussian processes are the gold standard for many real-world modeling pr...
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10/21/2019
Variational Integrator Networks for Physically Meaningful Embeddings
Learning workable representations of dynamical systems is becoming an in...
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06/06/2019
Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
Nonparametric extensions of topic models such as Latent Dirichlet Alloca...
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07/02/2018
A Piecewise Deterministic Markov Process via (r,θ) swaps in hyperspherical coordinates
Recently, a class of stochastic processes known as piecewise determinist...
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12/03/2017
Comment: A brief survey of the current state of play for Bayesian computation in data science at Big-Data scale
We wish to contribute to the discussion of "Comparing Consensus Monte Ca...
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11/17/2017
Techniques for proving Asynchronous Convergence results for Markov Chain Monte Carlo methods
Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling are findi...
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04/12/2017