Theodore Papamarkou

According to our database, Theodore Papamarkou authored at least 39 papers between 2013 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Model-agnostic variable importance for predictive uncertainty: an entropy-based approach.

Danny Wood, Theodore Papamarkou, Matt Benatan, Richard Allmendinger

Data Min. Knowl. Discov., November, 2024

Spatial-aware decision-making with ring attractors in reinforcement learning systems.

Marcos Negre Saura, Richard Allmendinger, Theodore Papamarkou, Wei Pan

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, Scott Mahan, Hansen Lillemark, Sharvaree P. Vadgama, Erik J. Bekkers, Tim Doster, Tegan Emerson, Henry Kvinge, Katrina Agate, Nesreen K. Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matous Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson, Nikos Kanakaris, Boshko Koloski, Veljko Kovac, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Meznar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frédéric Wantiez, Alexander Weers, Patrik Zajec, Blaz Skrlj, Nina Miolane

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

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, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi

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, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane

CoRR, 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

CoRR, 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

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 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

Proceedings of the Forty-first International Conference on Machine Learning, 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 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, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Probability-Generating Function Kernels for Spherical Data.

Theodore Papamarkou, Alexey Lindo

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024

2023

Approximate blocked Gibbs sampling for Bayesian neural networks.

Theodore Papamarkou

Stat. Comput., October, 2023

Depth-2 neural networks under a data-poisoning attack.

Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou

Neurocomputing, May, 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

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, Tim Doster, Tegan Emerson, Gurusankar Gopalakrishnan, Devendra Govil, Vincent P. Grande, Aldo Guzmán-Sáenz, Henry Kvinge, Neal Livesay, Jan Meissner, Soham Mukherjee, Shreyas N. Samaga, Karthikeyan Natesan Ramamurthy, Maneel Reddy Karri, Paul Rosen, Sophia Sanborn, Michael Scholkemper, Robin Walters, Jens Agerberg, Georg Bökman, Sadrodin Barikbin, Claudio Battiloro, Gleb Bazhenov, Guillermo Bernárdez, Aiden Brent, Sergio Escalera, Simone Fiorellino, Dmitrii Gavrilev, Mohammed Hassanin, Paul Häusner, Odin Hoff Gardaa, Abdelwahed Khamis, Manuel Lecha, German Magai, Tatiana Malygina, Pavlo Melnyk, et al.

CoRR, 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, Neal Livesay, Tamal K. Dey, Abraham Rabinowitz, Aiden Brent, Alessandro Salatiello, Alexander Nikitin, Ali Zia, Claudio Battiloro, Dmitrii Gavrilev, Georg Bökman, German Magai, Gleb Bazhenov, Guillermo Bernárdez, Indro Spinelli, Jens Agerberg, Kalyan Varma Nadimpalli, Lev Telyatnikov, Luca Scofano, Lucia Testa, Manuel Lecha, Maosheng Yang, Mohammed Hassanin, Odin Hoff Gardaa, Olga Zaghen, Paul Häusner, Paul Snopoff, Pavlo Melnyk, Rubén Ballester, Sadrodin Barikbin, Sergio Escalera, Simone Fiorellino, Henry Kvinge, Jan Meissner, Karthikeyan Natesan Ramamurthy, Michael Scholkemper, Paul Rosen, Robin Walters, Shreyas N. Samaga, Soham Mukherjee, Sophia Sanborn, Tegan Emerson, Timothy Doster, Tolga Birdal, Vincent P. Grande, Abdelwahed Khamis, Simone Scardapane, Suraj Singh, Tatiana Malygina, Yixiao Yue, Nina Miolane

Proceedings of the Topological, 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

Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023

Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection.

Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Theodore Papamarkou

Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2023

Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs.

Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, AIdo Guzman-Saenz, Tolga Birdal, Michael T. Schaub

Proceedings of the 57th Asilomar Conference on Signals, Systems, and Computers, ACSSC 2023, Pacific Grove, CA, USA, October 29, 2023

2022

A random persistence diagram generator.

Theodore Papamarkou, Farzana Nasrin, Austin Lawson, Na Gong, Orlando Rios, Vasileios Maroulas

Stat. Comput., 2022

The premise of approximate MCMC in Bayesian deep learning.

Theodore Papamarkou

CoRR, 2022

Higher-Order Attention Networks.

Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy

CoRR, 2022

The Inverse Problem for Controlled Differential Equations.

Anastasia Papavasiliou, Theodore Papamarkou, Yang Zhao

CoRR, 2022

Adapting Random Forests to Predict Obesity-Associated Gene Expression.

Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Farideh Jalali-najafabadi, Theodore Papamarkou

Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022

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

Mixed neural network Gaussian processes.

Alexey Lindo, Theodore Papamarkou, Serik Sagitov, Laura Stewart

CoRR, 2021

Random Persistence Diagram Generation.

Farzana Nasrin, Theodore Papamarkou, Vasileios Maroulas

CoRR, 2021

Hidden Markov models as recurrent neural networks: An application to Alzheimer's disease.

Matt Baucum, Anahita Khojandi, Theodore Papamarkou

Proceedings of the 21st IEEE International Conference on Bioinformatics and Bioengineering, 2021

2020

Wide Neural Networks with Bottlenecks are Deep Gaussian Processes.

Devanshu Agrawal, Theodore Papamarkou, Jacob D. Hinkle

J. Mach. Learn. Res., 2020

Bayesian neural networks and dimensionality reduction.

Deborshee Sen, Theodore Papamarkou, David B. Dunson

CoRR, 2020

Hidden Markov models are recurrent neural networks: A disease progression modeling application.

Matt Baucum, Anahita Khojandi, Theodore Papamarkou

CoRR, 2020

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

2019

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

2018

Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations.

Alan Lazarus, Dirk Husmeier, Theodore Papamarkou

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Gender Differences in Equity Crowdfunding.

Emoke-Ágnes Horvát, Theodore Papamarkou

Proceedings of the Fifth AAAI Conference on Human Computation and Crowdsourcing, 2017

2016

Forward-Mode Automatic Differentiation in Julia.

Jarrett Revels, Miles Lubin, Theodore Papamarkou

CoRR, 2016

2014

Nonlinear Dynamics of Trajectories Generated by Fully-Stretching Piecewise Linear Maps.

Theodore Papamarkou, Anthony J. Lawrance

Int. J. Bifurc. Chaos, 2014

2013

Paired Bernoulli Circular Spreading: Attaining the BER Lower Bound in a CSK Setting.

Theodore Papamarkou, Anthony J. Lawrance

Circuits Syst. Signal Process., 2013