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ORCIDTheodore Papamarkou
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- affiliation: University of Warwick, Coventry, UK
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2020 – today
- 2024
[j8]Danny Wood, Theodore Papamarkou, Matt Benatan, Richard Allmendinger:
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach. Data Min. Knowl. Discov. 38(6): 4184-4216 (2024)- [c13]Theodore Papamarkou
, Alexey Lindo:
Probability-Generating Function Kernels for Spherical Data. ICANN (1) 2024: 41-59 - [c12]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:
Position: Topological Deep Learning is the New Frontier for Relational Learning. ICML 2024 - [c11]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:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [c10]Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer:
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? ICML 2024 - [c9]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract). IJCAI 2024: 8466-8470 - [i23]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:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i22]Emanuel Sommer, Lisa Wimmer, Theodore Papamarkou, Ludwig Bothmann, Bernd Bischl, David Rügamer:
Connecting the Dots: Is Mode-Connectedness the Key to Feasible Sample-Based Inference in Bayesian Neural Networks? CoRR abs/2402.01484 (2024) - [i21]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:
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. CoRR abs/2402.02441 (2024) - [i20]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:
Position Paper: Challenges and Opportunities in Topological Deep Learning. CoRR abs/2402.08871 (2024) - [i19]Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Pavlo Vasylenko, Ghada Zamzmi, Mustafa Hajij, Michael T. Schaub, Nina Miolane, Simone Scardapane, Theodore Papamarkou:
TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning. CoRR abs/2406.06642 (2024) - [i18]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:
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. CoRR abs/2409.05211 (2024) - [i17]Marcos Negre Saura, Richard Allmendinger, Theodore Papamarkou, Wei Pan:
Spatial-aware decision-making with ring attractors in reinforcement learning systems. CoRR abs/2410.03119 (2024) - 2023
- [j7]Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou:
Depth-2 neural networks under a data-poisoning attack. Neurocomputing 532: 56-66 (2023) - [j6]Theodore Papamarkou:
Approximate blocked Gibbs sampling for Bayesian neural networks. Stat. Comput. 33(5): 119 (2023) - [c8]Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, AIdo Guzman-Saenz, Tolga Birdal, Michael T. Schaub:
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. ACSSC 2023: 799-803 - [c7]Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Theodore Papamarkou:
Towards Faster Gene Expression Prediction via Dimensionality Reduction and Feature Selection. EMBC 2023: 1-4 - [c6]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. ECML/PKDD (1) 2023: 459-474 - [c5]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:
ICML 2023 Topological Deep Learning Challenge: Design and Results. TAG-ML 2023: 3-8 - [i16]Jonas Gregor Wiese, Lisa Wimmer, Theodore Papamarkou, Bernd Bischl, Stephan Günnemann, David Rügamer:
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry. CoRR abs/2304.02902 (2023) - [i15]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.:
ICML 2023 Topological Deep Learning Challenge : Design and Results. CoRR abs/2309.15188 (2023) - [i14]Danny Wood, Theodore Papamarkou, Matt Benatan, Richard Allmendinger:
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach. CoRR abs/2310.12842 (2023) - [i13]Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzmán-Sáenz, Tolga Birdal, Michael T. Schaub:
Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs. CoRR abs/2312.09504 (2023) - 2022
- [j5]Theodore Papamarkou, Farzana Nasrin, Austin Lawson, Na Gong, Orlando Rios, Vasileios Maroulas:
A random persistence diagram generator. Stat. Comput. 32(1) (2022) - [c4]Jeremy Watts, Elexis Allen, Ahmad Mitoubsi, Anahita Khojandi, James Eales, Farideh Jalali-najafabadi, Theodore Papamarkou:
Adapting Random Forests to Predict Obesity-Associated Gene Expression. EMBC 2022: 4407-4410 - [i12]Anastasia Papavasiliou, Theodore Papamarkou, Yang Zhao:
The Inverse Problem for Controlled Differential Equations. CoRR abs/2201.10300 (2022) - [i11]Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy:
Higher-Order Attention Networks. CoRR abs/2206.00606 (2022) - [i10]Theodore Papamarkou:
The premise of approximate MCMC in Bayesian deep learning. CoRR abs/2208.11389 (2022) - 2021
- [j4]Mathieu Besançon, Theodore Papamarkou, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, John Pearson:
Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem. J. Stat. Softw. 98(1) (2021) - [c3]Matt Baucum, Anahita Khojandi, Theodore Papamarkou:
Hidden Markov models as recurrent neural networks: An application to Alzheimer's disease. BIBE 2021: 1-6 - [i9]Farzana Nasrin, Theodore Papamarkou, Vasileios Maroulas:
Random Persistence Diagram Generation. CoRR abs/2104.07737 (2021) - [i8]Alexey Lindo, Theodore Papamarkou, Serik Sagitov, Laura Stewart:
Mixed neural network Gaussian processes. CoRR abs/2112.00365 (2021) - 2020
- [j3]Devanshu Agrawal, Theodore Papamarkou, Jacob D. Hinkle:
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes. J. Mach. Learn. Res. 21: 175:1-175:66 (2020) - [i7]Devanshu Agrawal, Theodore Papamarkou, Jacob D. Hinkle:
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes. CoRR abs/2001.00921 (2020) - [i6]Theodore Papamarkou, Hayley Guy, Bryce Kroencke, Jordan Miller, Preston Robinette, Daniel Schultz, Jacob D. Hinkle, Laura Pullum, Catherine D. Schuman, Jeremy Renshaw, Stylianos Chatzidakis:
Automated detection of pitting and stress corrosion cracks in used nuclear fuel dry storage canisters using residual neural networks. CoRR abs/2003.03241 (2020) - [i5]Matt Baucum, Anahita Khojandi, Theodore Papamarkou:
Hidden Markov models are recurrent neural networks: A disease progression modeling application. CoRR abs/2006.03151 (2020) - [i4]Deborshee Sen, Theodore Papamarkou, David B. Dunson:
Bayesian neural networks and dimensionality reduction. CoRR abs/2008.08044 (2020)
2010 – 2019
- 2019
- [i3]Mathieu Besançon, David Anthoff, Alex Arslan, Simon Byrne, Dahua Lin, Theodore Papamarkou, John Pearson:
Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats Ecosystem. CoRR abs/1907.08611 (2019) - [i2]Theodore Papamarkou, Jacob D. Hinkle, M. Todd Young, David E. Womble:
Challenges in Bayesian inference via Markov chain Monte Carlo for neural networks. CoRR abs/1910.06539 (2019) - 2018
- [c2]Alan Lazarus, Dirk Husmeier, Theodore Papamarkou:
Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. AISTATS 2018: 1252-1260 - 2017
- [c1]Emoke-Ágnes Horvát, Theodore Papamarkou:
Gender Differences in Equity Crowdfunding. HCOMP 2017: 51-60 - 2016
- [i1]Jarrett Revels, Miles Lubin, Theodore Papamarkou:
Forward-Mode Automatic Differentiation in Julia. CoRR abs/1607.07892 (2016) - 2014
- [j2]Theodore Papamarkou, Anthony J. Lawrance:
Nonlinear Dynamics of Trajectories Generated by Fully-Stretching Piecewise Linear Maps. Int. J. Bifurc. Chaos 24(5) (2014) - 2013
- [j1]Theodore Papamarkou, Anthony J. Lawrance:
Paired Bernoulli Circular Spreading: Attaining the BER Lower Bound in a CSK Setting. Circuits Syst. Signal Process. 32(1): 143-166 (2013)
Coauthor Index
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