Paired Bernoulli Circular Spreading: Attaining the BER Lower Bound in a CSK Setting. - Theodore Papamarkou; Anthony J. Lawrance (Circuits Syst. Signal Process., 2013)

Nonlinear Dynamics of Trajectories Generated by Fully-Stretching Piecewise Linear Maps. - Theodore Papamarkou; Anthony J. Lawrance (Int. J. Bifurc. Chaos, 2014)

Forward-Mode Automatic Differentiation in Julia. - Jarrett Revels; Miles Lubin; Theodore Papamarkou (CoRR, 2016)

Gender Differences in Equity Crowdfunding. - Emoke-Ágnes Horvát; Theodore Papamarkou (HCOMP, 2017)

Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. - Alan Lazarus; Dirk Husmeier; Theodore Papamarkou (AISTATS, 2018)

Challenges in Bayesian inference via Markov chain Monte Carlo for neural networks. - Theodore Papamarkou; Jacob D. Hinkle; M. Todd Young; David E. Womble (CoRR, 2019)

Automated detection of pitting and stress corrosion cracks in used nuclear fuel dry storage canisters using residual neural networks. - Theodore Papamarkou; Hayley Guy; Bryce Kroencke; Jordan Miller; Preston Robinette; Daniel Schultz; Jacob D. Hinkle; Laura Pullum; Catherine D. Schuman; Jeremy Renshaw; Stylianos Chatzidakis (CoRR, 2020)

Hidden Markov models are recurrent neural networks: A disease progression modeling application. - Matt Baucum; Anahita Khojandi; Theodore Papamarkou (CoRR, 2020)

Bayesian neural networks and dimensionality reduction. - Deborshee Sen; Theodore Papamarkou; David B. Dunson (CoRR, 2020)

Wide Neural Networks with Bottlenecks are Deep Gaussian Processes. - Devanshu Agrawal; Theodore Papamarkou; Jacob D. Hinkle (J. Mach. Learn. Res., 2020)

Hidden Markov models as recurrent neural networks: An application to Alzheimer's disease. - Matt Baucum; Anahita Khojandi; Theodore Papamarkou (BIBE, 2021)

Random Persistence Diagram Generation. - Farzana Nasrin; Theodore Papamarkou; Vasileios Maroulas (CoRR, 2021)

Mixed neural network Gaussian processes. - Alexey Lindo; Theodore Papamarkou; Serik Sagitov; Laura Stewart (CoRR, 2021)

Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem. - Mathieu Besançon; Theodore Papamarkou; David Anthoff; Alex Arslan; Simon Byrne; Dahua Lin; John Pearson (J. Stat. Softw., 2021)

Adapting Random Forests to Predict Obesity-Associated Gene Expression. - Jeremy Watts; Elexis Allen; Ahmad Mitoubsi; Anahita Khojandi; James Eales; Farideh Jalali-najafabadi; Theodore Papamarkou (EMBC, 2022)

The Inverse Problem for Controlled Differential Equations. - Anastasia Papavasiliou; Theodore Papamarkou; Yang Zhao (CoRR, 2022)

Higher-Order Attention Networks. - Mustafa Hajij; Ghada Zamzmi; Theodore Papamarkou; Nina Miolane; Aldo Guzmán-Sáenz; Karthikeyan Natesan Ramamurthy (CoRR, 2022)

The premise of approximate MCMC in Bayesian deep learning. - Theodore Papamarkou (CoRR, 2022)

A random persistence diagram generator. - Theodore Papamarkou; Farzana Nasrin; Austin Lawson; Na Gong; Orlando Rios; Vasileios Maroulas (Stat. Comput., 2022)

Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. - Mustafa Hajij; Ghada Zamzmi; Theodore Papamarkou; AIdo Guzman-Saenz; Tolga Birdal; Michael T. Schaub (ACSSC, 2023)

Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection. - Jeremy Watts; Elexis Allen; Ahmad Mitoubsi; Anahita Khojandi; James Eales; Theodore Papamarkou (EMBC, 2023)

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. - Jonas Gregor Wiese; Lisa Wimmer; Theodore Papamarkou; Bernd Bischl; Stephan Günnemann; David Rügamer (ECML/PKDD (1), 2023)

ICML 2023 Topological Deep Learning Challenge: Design and Results. - Mathilde Papillon; Mustafa Hajij; Audun Myers; Florian Frantzen; Ghada Zamzmi; Helen Jenne; Johan Mathe; Josef Hoppe; Michael T. Schaub; Theodore Papamarkou; Aldo Guzmán-Sáenz; Bastian Rieck; et al. (TAG-ML, 2023)

ICML 2023 Topological Deep Learning Challenge : Design and Results. - Mathilde Papillon; Mustafa Hajij; Florian Frantzen; Josef Hoppe; Helen Jenne; Johan Mathe; Audun Myers; Theodore Papamarkou; Michael T. Schaub; Ghada Zamzmi; Tolga Birdal; Tamal K. Dey; et al. (CoRR, 2023)

Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. - Mustafa Hajij; Ghada Zamzmi; Theodore Papamarkou; Aldo Guzmán-Sáenz; Tolga Birdal; Michael T. Schaub (CoRR, 2023)

Depth-2 neural networks under a data-poisoning attack. - Sayar Karmakar; Anirbit Mukherjee; Theodore Papamarkou (Neurocomputing, 2023)

Approximate blocked Gibbs sampling for Bayesian neural networks. - Theodore Papamarkou (Stat. Comput., 2023)

Probability-Generating Function Kernels for Spherical Data. - Theodore Papamarkou; Alexey Lindo (ICANN (1), 2024)

Position: Topological Deep Learning is the New Frontier for Relational Learning. - Theodore Papamarkou; Tolga Birdal; Michael M. Bronstein; Gunnar E. Carlsson; Justin Curry; Yue Gao; Mustafa Hajij; Roland Kwitt; Pietro Lio; Paolo Di Lorenzo; Vasileios Maroulas; Nina Miolane; et al. (ICML, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? - Emanuel Sommer; Lisa Wimmer; Theodore Papamarkou; Ludwig Bothmann; Bernd Bischl; David Rügamer (ICML, 2024)

Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). - Jonas Gregor Wiese; Lisa Wimmer; Theodore Papamarkou; Bernd Bischl; Stephan Günnemann; David Rügamer (IJCAI, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. - Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Rubén Ballester; Claudio Battiloro; Guillermo Bernárdez; Tolga Birdal; Aiden Brent; Peter Chin 001; Sergio Escalera; et al. (CoRR, 2024)

Position Paper: Challenges and Opportunities in Topological Deep Learning. - Theodore Papamarkou; Tolga Birdal; Michael M. Bronstein; Gunnar E. Carlsson; Justin Curry; Yue Gao; Mustafa Hajij; Roland Kwitt; Pietro Liò; Paolo Di Lorenzo; Vasileios Maroulas; Nina Miolane; et al. (CoRR, 2024)

TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning. - Lev Telyatnikov; Guillermo Bernárdez; Marco Montagna; Pavlo Vasylenko; Ghada Zamzmi; Mustafa Hajij; Michael T. Schaub; Nina Miolane; Simone Scardapane; Theodore Papamarkou (CoRR, 2024)

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. - Guillermo Bernárdez; Lev Telyatnikov; Marco Montagna; Federica Baccini; Mathilde Papillon; Miquel Ferriol Galmés; Mustafa Hajij; Theodore Papamarkou; Maria Sofia Bucarelli; Olga Zaghen; Johan Mathe; Audun Myers; et al. (CoRR, 2024)

Spatial-aware decision-making with ring attractors in reinforcement learning systems. - Marcos Negre Saura; Richard Allmendinger 001; Theodore Papamarkou; Wei Pan 004 (CoRR, 2024)

Model-agnostic variable importance for predictive uncertainty: an entropy-based approach. - Danny Wood; Theodore Papamarkou; Matt Benatan; Richard Allmendinger 001 (Data Min. Knowl. Discov., 2024)

Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework. - Mustafa Hajij; Lennart Bastian; Sarah Osentoski; Hardik Kabaria; John L. Davenport; Sheik Dawood; Balaji Cherukuri; Joseph G. Kocheemoolayil; Nastaran Shahmansouri; Adrian Lew; Theodore Papamarkou; Tolga Birdal (CoRR, 2025)

Operator Splitting Methods: Numerical Solutions of Ordinary Differential Equations via Separation of Variables. - A. Banjara; Ibrahem AlJabea; Theodore Papamarkou; F. Neubrander (CoRR, 2025)