Self-reinforced Meta Learning for Belief Generation. - Alexandros Gkiokas; Alexandra I. Cristea; Matthew Thorpe (SGAI Conf., 2014)

Convergence of the k-Means Minimization Problem using Γ-Convergence. - Matthew Thorpe; Florian Theil; Adam M. Johansen; Neil Cade (SIAM J. Appl. Math., 2015)

Transport-based analysis, modeling, and learning from signal and data distributions. - Soheil Kolouri; Serim Park; Matthew Thorpe; Dejan Slepcev; Gustavo K. Rohde (CoRR, 2016)

Analysis of $p$-Laplacian Regularization in Semi-Supervised Learning. - Dejan Slepcev; Matthew Thorpe (CoRR, 2017)

A Transportation Lp Distance for Signal Analysis. - Matthew Thorpe; Serim Park; Soheil Kolouri; Gustavo K. Rohde; Dejan Slepcev (J. Math. Imaging Vis., 2017)

Optimal Mass Transport: Signal processing and machine-learning applications. - Soheil Kolouri; Se Rim Park; Matthew Thorpe; Dejan Slepcev; Gustavo K. Rohde (IEEE Signal Process. Mag., 2017)

Representing and Learning High Dimensional Data With the Optimal Transport Map From a Probabilistic Viewpoint. - Serim Park; Matthew Thorpe (CVPR, 2018)

Large Data and Zero Noise Limits of Graph-Based Semi-Supervised Learning Algorithms. - Matthew M. Dunlop; Dejan Slepcev; Andrew M. Stuart; Matthew Thorpe (CoRR, 2018)

PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds. - Oliver M. Crook; Tim Hurst; Carola-Bibiane Schönlieb; Matthew Thorpe; Konstantinos C. Zygalakis (CoRR, 2019)

Analysis of p-Laplacian Regularization in Semisupervised Learning. - Dejan Slepcev; Matthew Thorpe (SIAM J. Math. Anal., 2019)

Poisson Learning: Graph Based Semi-Supervised Learning At Very Low Label Rates. - Jeff Calder; Brendan Cook; Matthew Thorpe; Dejan Slepcev (ICML, 2020)

From graph cuts to isoperimetric inequalities: Convergence rates of Cheeger cuts on data clouds. - Nicolás García Trillos; Ryan Murray 001; Matthew Thorpe (CoRR, 2020)

Rates of Convergence for Laplacian Semi-Supervised Learning with Low Labeling Rates. - Jeff Calder; Dejan Slepcev; Matthew Thorpe (CoRR, 2020)

Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review. - Michael Roberts; Derek Driggs; Matthew Thorpe; Julian D. Gilbey; Michael Yeung; Stephan Ursprung; Angelica I. Avilés-Rivero; Christian Etmann; Cathal McCague; Lucian Beer; Jonathan R. Weir-McCall; Zhongzhao Teng; et al. (CoRR, 2020)

A Linear Transportation Lp Distance for Pattern Recognition. - Oliver M. Crook; Mihai Cucuringu; Tim Hurst; Carola-Bibiane Schönlieb; Matthew Thorpe; Konstantinos C. Zygalakis (CoRR, 2020)

Robust Certification for Laplace Learning on Geometric Graphs. - Matthew Thorpe; Bao Wang (MSML, 2021)

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. - Michael Roberts; Derek Driggs; Matthew Thorpe; Julian D. Gilbey; Michael Yeung; Stephan Ursprung; Angelica I. Avilés-Rivero; Christian Etmann; Cathal McCague; Lucian Beer; Jonathan R. Weir-McCall; Zhongzhao Teng; et al. (Nat. Mach. Intell., 2021)

GRAND++: Graph Neural Diffusion with A Source Term. - Matthew Thorpe; Tan Minh Nguyen; Hedi Xia; Thomas Strohmer; Andrea L. Bertozzi; Stanley J. Osher; Bao Wang 001 (ICLR, 2022)

Γ-Convergence of an Ambrosio-Tortorelli approximation scheme for image segmentation. - Irene Fonseca; Lisa Maria Kreusser; Carola-Bibiane Schönlieb; Matthew Thorpe (CoRR, 2022)

Classification of datasets with imputed missing values: does imputation quality matter? - Tolou Shadbahr; Michael Roberts; Jan Stanczuk; Julian D. Gilbey; Philip Teare; Sören Dittmer; Matthew Thorpe; Ramón Viñas Torné 001; Evis Sala; Pietro Lió; Mishal Patel; AIX-COVNET Collaboration; et al. (CoRR, 2022)

Rates of Convergence for Regression with the Graph Poly-Laplacian. - Nicolás García Trillos; Ryan Murray 001; Matthew Thorpe (CoRR, 2022)

The Linearized Hellinger-Kantorovich Distance. - Tianji Cai; Junyi Cheng; Bernhard Schmitzer; Matthew Thorpe (SIAM J. Imaging Sci., 2022)

Sliced Optimal Partial Transport. - Yikun Bai; Bernhard Schmitzer; Matthew Thorpe; Soheil Kolouri (CVPR, 2023)

PTLp: Partial Transport Lp Distances. - Xinran Liu; Yikun Bai; Huy Tran; Zhanqi Zhu; Matthew Thorpe; Soheil Kolouri (CoRR, 2023)

Discrete-to-Continuum Rates of Convergence for $p$-Laplacian Regularization. - Adrien Weihs; Jalal Fadili; Matthew Thorpe (CoRR, 2023)

Navigating the development challenges in creating complex data systems. - Sören Dittmer; Michael Roberts; Julian D. Gilbey; Ander Biguri; Ian Selby; Anna Breger; Matthew Thorpe; Jonathan R. Weir-McCall; Effrossyni Gkrania-Klotsas; Anna Korhonen; Emily R. Jefferson; Georg Langs; et al. (Nat. Mac. Intell., 2023)

Consistency of Fractional Graph-Laplacian Regularization in Semisupervised Learning with Finite Labels. - Adrien Weihs; Matthew Thorpe (SIAM J. Math. Anal., 2024)

A linear transportation Lp distance for pattern recognition. - Oliver M. Crook; Mihai Cucuringu; Tim Hurst; Carola-Bibiane Schönlieb; Matthew Thorpe; Konstantinos C. Zygalakis (Pattern Recognit., 2024)

Expected Sliced Transport Plans. - Xinran Liu; Rocio Diaz Martin; Yikun Bai; Ashkan Shahbazi; Matthew Thorpe; Akram Aldroubi; Soheil Kolouri (ICLR, 2025)

Manifold Learning in Wasserstein Space. - Keaton Hamm; Caroline Moosmüller; Bernhard Schmitzer; Matthew Thorpe (SIAM J. Math. Anal., 2025)