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1 code implementation • 29 Jan 2023 • Patric Bonnier, Harald Oberhauser, Zoltán Szabó

In $\mathbb R^d$, it is well-known that cumulants provide an alternative to moments that can achieve the same goals with numerous benefits such as lower variance estimators.

no code implementations • 27 Jan 2023 • Masaki Adachi, Satoshi Hayakawa, Saad Hamid, Martin Jørgensen, Harald Oberhauser, Micheal A. Osborne

Batch Bayesian optimisation (BO) has shown to be a sample-efficient method of performing optimisation where expensive-to-evaluate objective functions can be queried in parallel.

no code implementations • 23 Jan 2023 • Satoshi Hayakawa, Harald Oberhauser, Terry Lyons

We analyze the Nystr\"om approximation of a positive definite kernel associated with a probability measure.

2 code implementations • 9 Jun 2022 • Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne

Empirically, we find that our approach significantly outperforms the sampling efficiency of both state-of-the-art BQ techniques and Nested Sampling in various real-world datasets, including lithium-ion battery analytics.

1 code implementation • 27 May 2022 • Csaba Toth, Darrick Lee, Celia Hacker, Harald Oberhauser

This results in a novel tensor-valued graph operator, which we call the hypo-elliptic graph Laplacian.

no code implementations • 12 Oct 2021 • Uzu Lim, Harald Oberhauser, Vidit Nanda

Consider a set of points sampled independently near a smooth compact submanifold of Euclidean space.

1 code implementation • 20 Jul 2021 • Satoshi Hayakawa, Harald Oberhauser, Terry Lyons

We study kernel quadrature rules with convex weights.

1 code implementation • 6 Feb 2021 • Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, Terry Lyons

Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal dynamics.

1 code implementation • 4 Feb 2021 • Alexander Schell, Harald Oberhauser

We study the classical problem of recovering a multidimensional source signal from observations of nonlinear mixtures of this signal.

no code implementations • 1 Jan 2021 • Patrick Kidger, James Foster, Xuechen Li, Harald Oberhauser, Terry Lyons

Several authors have introduced \emph{Neural Stochastic Differential Equations} (Neural SDEs), often involving complex theory with various limitations.

1 code implementation • ICLR 2021 • Csaba Toth, Patric Bonnier, Harald Oberhauser

Sequential data such as time series, video, or text can be challenging to analyse as the ordered structure gives rise to complex dependencies.

Ranked #1 on Time Series Classification on KickvsPunch

1 code implementation • 2 Jun 2020 • Francesco Cosentino, Harald Oberhauser, Alessandro Abate

Various flavours of Stochastic Gradient Descent (SGD) replace the expensive summation that computes the full gradient by approximating it with a small sum over a randomly selected subsample of the data set that in turn suffers from a high variance.

1 code implementation • NeurIPS 2020 • Francesco Cosentino, Harald Oberhauser, Alessandro Abate

Given a discrete probability measure supported on $N$ atoms and a set of $n$ real-valued functions, there exists a probability measure that is supported on a subset of $n+1$ of the original $N$ atoms and has the same mean when integrated against each of the $n$ functions.

1 code implementation • ICML 2020 • Csaba Toth, Harald Oberhauser

We develop a Bayesian approach to learning from sequential data by using Gaussian processes (GPs) with so-called signature kernels as covariance functions.

Ranked #1 on Time Series Classification on DigitShapes

1 code implementation • 25 Oct 2018 • Ilya Chevyrev, Harald Oberhauser

This allows us to derive a metric of maximum mean discrepancy type for laws of stochastic processes and study the topology it induces on the space of laws of stochastic processes.

no code implementations • 1 Jun 2018 • Ilya Chevyrev, Vidit Nanda, Harald Oberhauser

We introduce a new feature map for barcodes that arise in persistent homology computation.

no code implementations • 2 Jan 2018 • Frithjof Gressmann, Franz J. Király, Bilal Mateen, Harald Oberhauser

Predictive modelling and supervised learning are central to modern data science.

no code implementations • 31 Aug 2017 • Terry Lyons, Harald Oberhauser

We introduce features for massive data streams.

no code implementations • 29 Jan 2016 • Franz J. Király, Harald Oberhauser

We present a novel framework for kernel learning with sequential data of any kind, such as time series, sequences of graphs, or strings.

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