Sketching the order of events. - Terry J. Lyons; Harald Oberhauser (CoRR, 2017)

Probabilistic supervised learning. - Frithjof Gressmann; Franz J. Király; Bilal A. Mateen; Harald Oberhauser (CoRR, 2018)

Variational Gaussian Processes with Signature Covariances. - Csaba Tóth; Harald Oberhauser (CoRR, 2019)

Kernels for Sequentially Ordered Data. - Franz J. Király; Harald Oberhauser (J. Mach. Learn. Res., 2019)

Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances. - Csaba Tóth; Harald Oberhauser (ICML, 2020)

A Randomized Algorithm to Reduce the Support of Discrete Measures. - Francesco Cosentino; Harald Oberhauser; Alessandro Abate (NeurIPS, 2020)

Acceleration of Descent-based Optimization Algorithms via Carathéodory's Theorem. - Francesco Cosentino; Harald Oberhauser; Alessandro Abate (CoRR, 2020)

Persistence Paths and Signature Features in Topological Data Analysis. - Ilya Chevyrev; Vidit Nanda; Harald Oberhauser (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

An Optimal Polynomial Approximation of Brownian Motion. - James Foster; Terry J. Lyons; Harald Oberhauser (SIAM J. Numer. Anal., 2020)

Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections. - Csaba Tóth; Patric Bonnier; Harald Oberhauser (ICLR, 2021)

The shifted ODE method for underdamped Langevin MCMC. - James Foster; Terry J. Lyons; Harald Oberhauser (CoRR, 2021)

Nonlinear Independent Component Analysis for Continuous-Time Signals. - Harald Oberhauser; Alexander Schell (CoRR, 2021)

Neural SDEs as Infinite-Dimensional GANs. - Patrick Kidger; James Foster; Xuechen Li; Harald Oberhauser; Terry J. Lyons (CoRR, 2021)

Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination. - Masaki Adachi; Satoshi Hayakawa; Martin Jørgensen; Harald Oberhauser; Michael A. Osborne (NeurIPS, 2022)

Positively Weighted Kernel Quadrature via Subsampling. - Satoshi Hayakawa; Harald Oberhauser; Terry J. Lyons (NeurIPS, 2022)

Capturing Graphs with Hypo-Elliptic Diffusions. - Csaba Tóth; Darrick Lee; Celia Hacker; Harald Oberhauser (NeurIPS, 2022)

Hypercontractivity Meets Random Convex Hulls: Analysis of Randomized Multivariate Cubatures. - Satoshi Hayakawa; Harald Oberhauser; Terry J. Lyons (CoRR, 2022)

Signature Moments to Characterize Laws of Stochastic Processes. - Ilya Chevyrev; Harald Oberhauser (J. Mach. Learn. Res., 2022)

Sampling-based Nyström Approximation and Kernel Quadrature. - Satoshi Hayakawa; Harald Oberhauser; Terry J. Lyons (ICML, 2023)

Kernelized Cumulants: Beyond Kernel Mean Embeddings. - Patric Bonnier; Harald Oberhauser; Zoltán Szabó (NeurIPS, 2023)

SOBER: Scalable Batch Bayesian Optimization and Quadrature using Recombination Constraints. - Masaki Adachi; Satoshi Hayakawa; Saad Hamid; Martin Jørgensen; Harald Oberhauser; Michael A. Osborne (CoRR, 2023)

The Signature Kernel. - Darrick Lee; Harald Oberhauser (CoRR, 2023)

Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature. - Masaki Adachi; Satoshi Hayakawa; Xingchen Wan; Martin Jørgensen; Harald Oberhauser; Michael A. Osborne (CoRR, 2023)

HADES: Fast Singularity Detection with Local Measure Comparison. - Uzu Lim; Harald Oberhauser; Vidit Nanda (CoRR, 2023)

Grid-Free Computation of Probabilistic Safety With Malliavin Calculus. - Francesco Cosentino; Harald Oberhauser; Alessandro Abate (IEEE Trans. Autom. Control., 2023)

Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. - Masaki Adachi; Satoshi Hayakawa; Martin Jørgensen; Xingchen Wan; Vu Nguyen; Harald Oberhauser; Michael A. Osborne (AISTATS, 2024)

A Quadrature Approach for General-Purpose Batch Bayesian Optimization via Probabilistic Lifting. - Masaki Adachi; Satoshi Hayakawa; Martin Jørgensen; Saad Hamid; Harald Oberhauser; Michael A. Osborne (CoRR, 2024)

Tangent Space and Dimension Estimation with the Wasserstein Distance. - Uzu Lim; Harald Oberhauser; Vidit Nanda (SIAM J. Appl. Algebra Geom., 2024)

Learning to Forget: Bayesian Time Series Forecasting using Recurrent Sparse Spectrum Signature Gaussian Processes. - Csaba Tóth; Masaki Adachi; Michael A. Osborne; Harald Oberhauser (AISTATS, 2025)

A topological approach to mapping space signatures. - Chad Giusti; Darrick Lee; Vidit Nanda; Harald Oberhauser (Adv. Appl. Math., 2025)

Random Fourier Signature Features. - Csaba Tóth; Harald Oberhauser; Zoltán Szabó (SIAM J. Math. Data Sci., 2025)

A User's Guide to KSig: GPU-Accelerated Computation of the Signature Kernel. - Csaba Tóth; Danilo Jr Dela Cruz; Harald Oberhauser (CoRR, 2025)

Generalized Time Series Classification via Component Decomposition and Alignment. - Yichuan Cheng; Darrick Lee; Harald Oberhauser; Haoliang Li (IEEE Trans. Big Data, 2025)