Theodore Papamarkou

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 34

Top Topics

Ordinary Differential Equations

Gaussian Processes

Gene Expression

Top Venues

Int. J. Bifurc. Chaos

Publications

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 (2024)
Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar 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 (2024)
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 (2024)
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 (2024)
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 (2024)
Danny Wood, Theodore Papamarkou, Matt Benatan, Richard Allmendinger
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach. CoRR (2023)
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)
Sayar Karmakar, Anirbit Mukherjee, Theodore Papamarkou
Depth-2 neural networks under a data-poisoning attack. Neurocomputing 532 (2023)
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 (2023)
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)
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 Meisner, 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 (2023)
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)
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 (2023)
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)
Theodore Papamarkou
Approximate blocked Gibbs sampling for Bayesian neural networks. Stat. Comput. 33 (5) (2023)
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)
Theodore Papamarkou
The premise of approximate MCMC in Bayesian deep learning. CoRR (2022)
Theodore Papamarkou, Farzana Nasrin, Austin Lawson, Na Gong, Orlando Rios, Vasileios Maroulas
A random persistence diagram generator. Stat. Comput. 32 (1) (2022)
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy
Higher-Order Attention Networks. CoRR (2022)
Anastasia Papavasiliou, Theodore Papamarkou, Yang Zhao
The Inverse Problem for Controlled Differential Equations. CoRR (2022)
Matt Baucum, Anahita Khojandi, Theodore Papamarkou
Hidden Markov models as recurrent neural networks: An application to Alzheimer's disease. BIBE (2021)
Farzana Nasrin, Theodore Papamarkou, Vasileios Maroulas
Random Persistence Diagram Generation. CoRR (2021)
Alexey Lindo, Theodore Papamarkou, Serik Sagitov, Laura Stewart
Mixed neural network Gaussian processes. CoRR (2021)
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)
Deborshee Sen, Theodore Papamarkou, David Dunson
Bayesian neural networks and dimensionality reduction. CoRR (2020)
Devanshu Agrawal, Theodore Papamarkou, Jacob D. Hinkle
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes. J. Mach. Learn. Res. 21 (2020)
Devanshu Agrawal, Theodore Papamarkou, Jacob D. Hinkle
Wide Neural Networks with Bottlenecks are Deep Gaussian Processes. CoRR (2020)
Matt Baucum, Anahita Khojandi, Theodore Papamarkou
Hidden Markov models are recurrent neural networks: A disease progression modeling application. CoRR (2020)
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 (2020)
Theodore Papamarkou, Jacob D. Hinkle, M. Todd Young, David E. Womble
Challenges in Bayesian inference via Markov chain Monte Carlo for neural networks. CoRR (2019)
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 (2019)
Alan Lazarus, Dirk Husmeier, Theodore Papamarkou
Multiphase MCMC Sampling for Parameter Inference in Nonlinear Ordinary Differential Equations. AISTATS (2018)
Emoke-Ágnes Horvát, Theodore Papamarkou
Gender Differences in Equity Crowdfunding. HCOMP (2017)
Jarrett Revels, Miles Lubin, Theodore Papamarkou
Forward-Mode Automatic Differentiation in Julia. CoRR (2016)
Theodore Papamarkou, Anthony J. Lawrance
Nonlinear Dynamics of Trajectories Generated by Fully-Stretching Piecewise Linear Maps. Int. J. Bifurc. Chaos 24 (5) (2014)
Theodore Papamarkou, Anthony J. Lawrance
Paired Bernoulli Circular Spreading: Attaining the BER Lower Bound in a CSK Setting. Circuits Syst. Signal Process. 32 (1) (2013)