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ORCIDJohan Kwisthout
Person information
- affiliation: Radboud University Nijmegen, Donders Center for Cognition, The Netherlands
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Books and Theses
- 2019
[b1]Iris van Rooij
, Mark Blokpoel, Johan Kwisthout, Todd Wareham:
Cognition and intractability: a guide to classical and parameterized complexity analysis. Cambridge University Press 2019, ISBN 9781107358331
Journal Articles
- 2024
- [j15]Mark Peelen, Leila Bagheriye, Johan Kwisthout:
Cancer Subtype Identification Through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data. IEEE Access 12: 27768-27783 (2024) - 2023
- [j14]Johan Kwisthout:
Motivating explanations in Bayesian networks using MAP-independence. Int. J. Approx. Reason. 153: 18-28 (2023) - 2022
- [j13]Danaja Rutar, Wanja Wiese, Johan Kwisthout:
From representations in predictive processing to degrees of representational features. Minds Mach. 32(3): 461-484 (2022) - 2019
- [j12]Lieke Heil, Olympia Colizoli, Egbert Hartstra, Johan Kwisthout, Stan van Pelt, Iris van Rooij, Harold Bekkering:
Processing of Prediction Errors in Mentalizing Areas. J. Cogn. Neurosci. 31(6): 900-912 (2019) - 2018
- [j11]Johan Kwisthout:
Approximate inference in Bayesian networks: Parameterized complexity results. Int. J. Approx. Reason. 93: 119-131 (2018) - [j10]Iris van Rooij, Cory D. Wright, Johan Kwisthout, Todd Wareham:
Rational analysis, intractability, and the prospects of 'as if'-explanations. Synth. 195(2): 491-510 (2018) - 2017
- [j9]Arastoo Bozorgi, Saeed Samet, Johan Kwisthout, Todd Wareham:
Community-based influence maximization in social networks under a competitive linear threshold model. Knowl. Based Syst. 134: 149-158 (2017) - 2015
- [j8]Johan Kwisthout:
Most frugal explanations in Bayesian networks. Artif. Intell. 218: 56-73 (2015) - [j7]Johan Kwisthout:
Tree-Width and the Computational Complexity of MAP Approximations in Bayesian Networks. J. Artif. Intell. Res. 53: 699-720 (2015) - 2013
- [j6]Johan Kwisthout, Iris van Rooij:
Bridging the gap between theory and practice of approximate Bayesian inference. Cogn. Syst. Res. 24: 2-8 (2013) - 2012
- [j5]Johan Kwisthout:
Relevancy in Problem Solving: A Computational Framework. J. Probl. Solving 5(1) (2012) - 2011
- [j4]Johan Kwisthout, Todd Wareham, Iris van Rooij:
Bayesian Intractability Is Not an Ailment That Approximation Can Cure. Cogn. Sci. 35(5): 779-784 (2011) - [j3]Johan Kwisthout:
Most probable explanations in Bayesian networks: Complexity and tractability. Int. J. Approx. Reason. 52(9): 1452-1469 (2011) - 2008
- [j2]Johan Kwisthout, Paul Vogt, Pim Haselager, Ton Dijkstra:
Joint attention and language evolution. Connect. Sci. 20(2&3): 155-171 (2008) - [j1]Johan Kwisthout, Gerard Tel:
Complexity results for enhanced qualitative probabilistic networks. Int. J. Approx. Reason. 48(3): 879-888 (2008)
Conference and Workshop Papers
- 2024
- [c32]Charles Theodore Kent, Leila Bagheriye, Johan Kwisthout:
Neural Population Decoding and Imbalanced Multi-Omic Datasets for Cancer Subtype Diagnosis. BIOSTEC (1) 2024: 391-403 - 2022
- [c31]Hans L. Bodlaender, Nils Donselaar, Johan Kwisthout:
Parameterized Completeness Results for Bayesian Inference. PGM 2022: 145-156 - [c30]Johan Kwisthout:
Speeding up approximate MAP by applying domain knowledge about relevant variables. PGM 2022: 229-240 - 2021
- [c29]Johan Kwisthout:
Explainable AI Using MAP-Independence. ECSQARU 2021: 243-254 - 2020
- [c28]Siegfried Ludwig, Joeri Hartjes, Bram Pol, Gabriela Rivas, Johan Kwisthout:
A Spiking Neuron Implementation of Genetic Algorithms for Optimization. BNAIC/BENELEARN (Selected Papers) 2020: 91-105 - [c27]Stefan Iacob, Johan Kwisthout, Serge Thill:
From Models of Cognition to Robot Control and Back Using Spiking Neural Networks. Living Machines 2020: 176-191 - [c26]Johan Kwisthout, Nils Donselaar:
On the computational power and complexity of Spiking Neural Networks. NICE 2020: 4:1-4:7 - 2018
- [c25]Johan Kwisthout:
Finding Dissimilar Explanations in Bayesian Networks: Complexity Results. BNCAI 2018: 65-72 - 2017
- [c24]Sarit Pink-Hashkes, Iris van Rooij, Johan Kwisthout:
Perception is in the Details: A Predictive Coding Account of the Psychedelic Phenomenon. CogSci 2017 - 2016
- [c23]Maria Otworowska, Lorijn Zaadnoordijk, Erwin de Wolff, Johan Kwisthout, Iris van Rooij:
Causal learning in the Crib: A predictive processing formalization and babybot simulation. ICDL-EPIROB 2016: 39-40 - [c22]Lorijn Zaadnoordijk, Maria Otworowska, Johan Kwisthout, Sabine Hunnius, Iris van Rooij:
The mobile-paradigm as measure of infants' sense of agency? Insights from babybot simulations. ICDL-EPIROB 2016: 41-42 - [c21]Johan Kwisthout:
The Parameterized Complexity of Approximate Inference in Bayesian Networks. Probabilistic Graphical Models 2016: 264-274 - 2015
- [c20]Lorijn Zaadnoordijk, Sabine Hunnius, Marlene Meyer, Johan Kwisthout, Iris van Rooij:
What senses of agency can infants have? CogSci 2015 - [c19]Lorijn Zaadnoordijk, Sabine Hunnius, Marlene Meyer, Johan Kwisthout, Iris van Rooij:
The developing sense of agency: Implications from cognitive phenomenology. ICDL-EPIROB 2015: 114-115 - 2014
- [c18]Johan Kwisthout, Maria Otworowska, Harold Bekkering, Iris van Rooij:
Leaving Andy Clark's 'safe shores': Scaling predictive processing to higher cognition. CogSci 2014 - [c17]Johan Kwisthout:
Minimizing Relative Entropy in Hierarchical Predictive Coding. Probabilistic Graphical Models 2014: 254-270 - [c16]Johan Kwisthout:
Treewidth and the Computational Complexity of MAP Approximations. Probabilistic Graphical Models 2014: 271-285 - 2013
- [c15]Johan Kwisthout, Iris van Rooij:
Predictive coding and the Bayesian brain: Intractability hurdles that are yet to be overcome. CogSci 2013 - [c14]Johan Kwisthout, Iris van Rooij, Matteo Colombo, Carlos Zednik, William A. Phillips:
Constraints on Bayesian Explanation. CogSci 2013 - [c13]Iris van Rooij, Johan Kwisthout, Mark Blokpoel, Todd Wareham:
Computational complexity analysis for cognitive scientists. CogSci 2013 - [c12]Johan Kwisthout:
Most Inforbable Explanations: Finding Explanations in Bayesian Networks That Are Both Probable and Informative. ECSQARU 2013: 328-339 - [c11]Johan Kwisthout:
Structure Approximation of Most Probable Explanations in Bayesian Networks. ECSQARU 2013: 340-351 - 2011
- [c10]Mark Blokpoel, Johan Kwisthout, Todd Wareham, Pim Haselager, Ivan Toni, Iris van Rooij:
The computational costs of recipient design and intention recognition in communication. CogSci 2011 - [c9]Todd Wareham, Johan Kwisthout, Pim Haselager, Iris van Rooij:
Ignorance is bliss: A complexity perspective on adapting reactive architectures. ICDL-EPIROB 2011: 1-5 - [c8]Johan Kwisthout, Hans L. Bodlaender, Linda C. van der Gaag:
The Complexity of Finding kth Most Probable Explanations in Probabilistic Networks. SOFSEM 2011: 356-367 - 2010
- [c7]Johan Kwisthout, Hans L. Bodlaender, Linda C. van der Gaag:
The Necessity of Bounded Treewidth for Efficient Inference in Bayesian Networks. ECAI 2010: 237-242 - 2008
- [c6]Johan Kwisthout, Linda C. van der Gaag:
The Computational Complexity of Sensitivity Analysis and Parameter Tuning. UAI 2008: 349-356 - 2007
- [c5]Johan Kwisthout, Hans L. Bodlaender, Gerard Tel:
Local Monotonicity in Probabilistic Networks. ECSQARU 2007: 548-559 - [c4]Johan Kwisthout:
The Computational Complexity of Monotonicity in Probabilistic Networks. FCT 2007: 388-399 - 2006
- [c3]Johan Kwisthout, Gerard Tel:
Complexity Results for Enhanced Qualitative Probabilistic Networks. Probabilistic Graphical Models 2006: 171-178 - 2005
- [c2]Johan Kwisthout, Mehdi Dastani:
Modelling Uncertainty in Agent Programming. BNAIC 2005: 361-362 - [c1]Johan Kwisthout, Mehdi Dastani:
Modelling Uncertainty in Agent Programming. DALT 2005: 17-32
Informal and Other Publications
- 2024
- [i10]Charles Theodore Kent, Leila Bagheriye, Johan Kwisthout:
Neural Population Decoding and Imbalanced Multi-Omic Datasets For Cancer Subtype Diagnosis. CoRR abs/2401.10844 (2024) - 2023
- [i9]Otto van der Himst, Leila Bagheriye, Johan Kwisthout:
Bayesian Integration of Information Using Top-Down Modulated WTA Networks. CoRR abs/2308.15390 (2023) - [i8]Mark Peelen, Leila Bagheriye, Johan Kwisthout:
Cancer Subtype Identification through Integrating Inter and Intra Dataset Relationships in Multi-Omics Data. CoRR abs/2312.02195 (2023) - 2022
- [i7]Hans L. Bodlaender, Nils Donselaar, Johan Kwisthout:
Parameterized Complexity Results for Bayesian Inference. CoRR abs/2206.07172 (2022) - [i6]Johan Kwisthout:
Motivating explanations in Bayesian networks using MAP-independence. CoRR abs/2208.03121 (2022) - [i5]Dominique J. Kösters, Bryan A. Kortman, Irem Boybat, Elena Ferro, Sagar Dolas, Roberto de Austri, Johan Kwisthout, Hans Hilgenkamp, Theo Rasing, Heike Riel, Abu Sebastian, Sascha Caron, Johan H. Mentink:
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics. CoRR abs/2209.10481 (2022) - 2020
- [i4]Johan Kwisthout, Nils Donselaar:
On the computational power and complexity of Spiking Neural Networks. CoRR abs/2001.08439 (2020) - 2019
- [i3]Abdullahi Ali, Johan Kwisthout:
A spiking neural algorithm for the Network Flow problem. CoRR abs/1911.13097 (2019) - 2018
- [i2]Johan Kwisthout:
Finding dissimilar explanations in Bayesian networks: Complexity results. CoRR abs/1810.11391 (2018) - 2012
- [i1]Johan Kwisthout, Linda C. van der Gaag:
The Computational Complexity of Sensitivity Analysis and Parameter Tuning. CoRR abs/1206.3265 (2012)
Coauthor Index
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