Theodore Papamarkou

50 papers A* 4A 1B 1C 1Misc 1Journal 36Unranked 6
YearRankTypeTitle / Venue / Authors
2025 J jnl
Copresheaf Topological Neural Networks: A Generalized Deep Learning Framework.
CoRR
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
2025 J jnl
Operator Splitting Methods: Numerical Solutions of Ordinary Differential Equations via Separation of Variables.
CoRR
A. Banjara, Ibrahem AlJabea, Theodore Papamarkou, F. Neubrander
2025 J jnl
The Human Brain as a Combinatorial Complex.
CoRR
Valentina Sánchez, Çiçek Güven, Koen Haak, Theodore Papamarkou, Gonzalo Nápoles, Marie Safár Postma
2025 J jnl
TopoBench: A Framework for Benchmarking Topological Deep Learning.
J. Data-centric Mach. Learn. Res.
Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Mustafa Hajij, Martin Carrasco, Pavlo Vasylenko, Mathilde Papillon, Ghada Zamzmi, Michael T. Schaub, Jonas Verhellen, Pavel Snopov, Bertran Miquel-Oliver, Manel Gil-Sorribes, Alexis Molina, Victor Guallar, Theodore Long, Julian Suk, Patryk Rygiel, Alexander V. Nikitin, Giordan Escalona, Michael Banf, Dominik Filipiak, Liliya Imasheva, Max Schattauer, Alvaro L. Martinez, Halley Fritze, Marissa Masden, Valentina Sánchez, Manuel Lecha, Andrea Cavallo, Claudio Battiloro, Matthew Piekenbrock, Mauricio Tec, George Dasoulas, Nina Miolane, Simone Scardapane, Theodore Papamarkou
2024 A* conf
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
ICML
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
2024 J jnl
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks?
CoRR
Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer
2024 — conf
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain.
GRaM
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 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
2024 J jnl
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain.
CoRR
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
2024 J jnl
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach.
Data Min. Knowl. Discov.
Danny Wood, Theodore Papamarkou, Matt Benatan, Richard Allmendinger
2024 J jnl
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
CoRR
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
2024 J jnl
Position Paper: Challenges and Opportunities in Topological Deep Learning.
CoRR
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
2024 A* conf
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI.
ICML
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
2024 A* conf
Position: Topological Deep Learning is the New Frontier for Relational Learning.
ICML
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
2024 — conf
Probability-Generating Function Kernels for Spherical Data.
ICANN (1)
Theodore Papamarkou, Alexey Lindo
2024 J jnl
Spatial-aware decision-making with ring attractors in reinforcement learning systems.
CoRR
Marcos Negre Saura, Richard Allmendinger, Theodore Papamarkou, Wei Pan
2024 J jnl
TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning.
CoRR
Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Pavlo Vasylenko, Ghada Zamzmi, Mustafa Hajij, Michael T. Schaub, Nina Miolane, Simone Scardapane, Theodore Papamarkou
2024 J jnl
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains.
CoRR
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
2024 J jnl
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains.
J. Mach. Learn. Res.
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
2024 A* conf
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract).
IJCAI
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023 J jnl
Approximate blocked Gibbs sampling for Bayesian neural networks.
Stat. Comput.
Theodore Papamarkou
2023 Misc conf
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs.
ACSSC
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, AIdo Guzman-Saenz, Tolga Birdal, Michael T. Schaub
2023 J jnl
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs.
CoRR
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzmán-Sáenz, Tolga Birdal, Michael T. Schaub
2023 J jnl
Depth-2 neural networks under a data-poisoning attack.
Neurocomputing
Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou
2023 J jnl
ICML 2023 Topological Deep Learning Challenge : Design and Results.
CoRR
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.
2023 — conf
ICML 2023 Topological Deep Learning Challenge: Design and Results.
TAG-ML
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
2023 J jnl
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach.
CoRR
Danny Wood, Theodore Papamarkou, Matt Benatan, Richard Allmendinger
2023 — conf
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
ECML/PKDD (1)
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023 J jnl
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry.
CoRR
Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer
2023 — conf
Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection.
EMBC
Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Theodore Papamarkou
2022 J jnl
A random persistence diagram generator.
Stat. Comput.
Theodore Papamarkou, Farzana Nasrin, Austin Lawson, Na Gong, Orlando Rios, Vasileios Maroulas
2022 — conf
Adapting Random Forests to Predict Obesity-Associated Gene Expression.
EMBC
Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Farideh Jalali-najafabadi, Theodore Papamarkou
2022 J jnl
Higher-Order Attention Networks.
CoRR
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy
2022 J jnl
The Inverse Problem for Controlled Differential Equations.
CoRR
Anastasia Papavasiliou, Theodore Papamarkou, Yang Zhao
2022 J jnl
The premise of approximate MCMC in Bayesian deep learning.
CoRR
Theodore Papamarkou
2021 J jnl
Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem.
J. Stat. Softw.
Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, John Pearson
2021 C conf
Hidden Markov models as recurrent neural networks: An application to Alzheimer's disease.
BIBE
Matt Baucum, Anahita Khojandi, Theodore Papamarkou
2021 J jnl
Mixed neural network Gaussian processes.
CoRR
Alexey Lindo, Theodore Papamarkou, Serik Sagitov, Laura Stewart
2021 J jnl
Random Persistence Diagram Generation.
CoRR
Farzana Nasrin, Theodore Papamarkou, Vasileios Maroulas
2020 J jnl
Automated detection of pitting and stress corrosion cracks in used nuclear fuel dry storage canisters using residual neural networks.
CoRR
Theodore Papamarkou, Hayley Guy, Bryce Kroencke, Jordan Miller, Preston Robinette, Daniel Schultz, Jacob D. Hinkle, Laura Pullum, Catherine D. Schuman, Jeremy Renshaw, Stylianos Chatzidakis
2020 J jnl
Bayesian neural networks and dimensionality reduction.
CoRR
Deborshee Sen, Theodore Papamarkou, David B. Dunson
2020 J jnl
Hidden Markov models are recurrent neural networks: A disease progression modeling application.
CoRR
Matt Baucum, Anahita Khojandi, Theodore Papamarkou
2020 J jnl
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes.
CoRR
Devanshu Agrawal, Theodore Papamarkou, Jacob D. Hinkle
2020 J jnl
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes.
J. Mach. Learn. Res.
Devanshu Agrawal, Theodore Papamarkou, Jacob D. Hinkle
2019 J jnl
Challenges in Bayesian inference via Markov chain Monte Carlo for neural networks.
CoRR
Theodore Papamarkou, Jacob D. Hinkle, M. Todd Young, David E. Womble
2019 J jnl
Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem.
CoRR
Mathieu Besançon, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, Theodore Papamarkou, John Pearson
2018 A conf
Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations.
AISTATS
Alan Lazarus, Dirk Husmeier, Theodore Papamarkou
2017 B conf
Gender Differences in Equity Crowdfunding.
HCOMP
Emoke-Ágnes Horvát, Theodore Papamarkou
2016 J jnl
Forward-Mode Automatic Differentiation in Julia.
CoRR
Jarrett Revels, Miles Lubin, Theodore Papamarkou
2014 J jnl
Nonlinear Dynamics of Trajectories Generated by Fully-Stretching Piecewise Linear Maps.
Int. J. Bifurc. Chaos
Theodore Papamarkou, Anthony J. Lawrance
2013 J jnl
Paired Bernoulli Circular Spreading: Attaining the BER Lower Bound in a CSK Setting.
Circuits Syst. Signal Process.
Theodore Papamarkou, Anthony J. Lawrance