Dr Marek Grzes

School of Computing

University of Kent

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Refereed Publications

  1. Theophile Champion, Marek Grzes, and Howard Bowman: Multimodal and Multifactor Branching Time Active Inference. Neural Computation, vol. 36, issue 11, 2024, p. 2479-2504.
    [doi] [accepted version]
  2. Theophile Champion, Marek Grzes, Lisa Bonheme, and Howard Bowman: Deconstructing deep active inference: a contrarian information gatherer. Neural Computation, vol. 36, issue 11, 2024, p. 2403-2445.
    [doi] [accepted version]
  3. Lisa Bonheme and Marek Grzes: Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders. Journal of Machine Learning Research (JMLR) 24(324): 1-30, 2023.
    [paper] [poster at ICLR'24]
  4. Peter Clapham and Marek Grzes: Posterior Collapse in Variational Gradient Origin Networks. Proc. of the 22nd International Conference on Machine Learning and Applications (ICMLA), Jacksonville, USA, 2023.
    [pdf]
  5. Piotr Sawicki, Marek Grzes, Fabricio Goes, Dan Brown, Max Peeperkorn, and Aisha Khatun: Bits of Grass: Does GPT already know how to write like Whitman? Proc. of the 14th International Conference on Computational Creativity (ICCC), Waterloo, Canada, 2023.
    [pdf] [github] [slides]
  6. Fabricio Goes, Piotr Sawicki, Marek Grzes, Dan Brown, and Marco Volpe: Is GPT-4 Good Enough to Evaluate Jokes?. Proc. of the 14th International Conference on Computational Creativity (ICCC), Waterloo, Canada, 2023.
    [pdf] [github]
  7. Fabricio Goes, Marco Volpe, Piotr Sawicki, Marek Grzes, and Jacob Watson: Pushing GPT's Creativity to Its Limits: Alternative Uses and Torrance Tests. Proc. of the 14th International Conference on Computational Creativity (ICCC), Waterloo, Canada, 2023.
    [pdf] [github]
  8. Piotr Sawicki, Marek Grzes, Fabricio Goes, Dan Brown, Max Peeperkorn, Aisha Khatun, and Simona Paraskevopoulou: On the power of special-purpose GPT models to create and evaluate new poetry in old styles. Proc. of the 14th International Conference on Computational Creativity (ICCC), Waterloo, Canada, 2023.
    [pdf] [github]
  9. Lisa Bonheme and Marek Grzes: The Polarised Regime of identifiable Variational Autoencoders. Proceedings of the First Tiny Papers Track at the International Conference on Learning Representations (Tiny@ICLR). Kigali, Rwanda, 2023.
    [paper on openreview]
  10. Theophile Champion, Marek Grzes, and Howard Bowman: Branching Time Active Inference with Bayesian Filtering. Neural Computation 34(10): 2132-2144, 2022.
    [pdf on KAR]
  11. Theophile Champion, Howard Bowman, and Marek Grzes: Branching Time Active Inference: empirical study and complexity class analysis. Neural Networks, 152: 450-466, 2022.
    [pdf on arXiv]
  12. Piotr Sawicki, Marek Grzes, Anna Jordanous, Dan Brown, and Max Peeperkorn: Training GPT-2 to represent two Romantic-era authors: challenges, evaluations and pitfalls. Proc. of the 13th International Conference on Computational Creativity (ICCC), pp. 34-43, Bozen-Bolzano, Italy, 2022.
    [pdf] [github]
  13. Theophile Champion, Lancelot Da Costa, Howard Bowman, and Marek Grzes: Branching Time Active Inference: The theory and its generality. Neural Networks, 151: 295-316, 2022.
    [pdf on arXiv]
  14. Theophile Champion, Marek Grzes, and Howard Bowman: Realising Active Inference in Variational Message Passing: the Outcome-blind Certainty Seeker. Neural Computation, 33 (10): 2762-2826, ISSN 0899-7667, 2021.
    [pdf on arXiv]
  15. Lisa Bonheme and Marek Grzes: SESAM at SemEval-2020 Task 8: Investigating the relationship between image and text in sentiment analysis of memes. Proceedings of the International Workshop on Semantic Evaluation (SemEval). Barcelona, Spain, 2020.
    [paper]
  16. Anthony Casey, Hannan Azhar, Marek Grzes and Mohamed Sakel: BCI controlled robotic arm as assistance to the rehabilitation of neurologically disabled patients. Disability and Rehabilitation: Assistive Technology. 2019. DOI: 10.1080/17483107.2019.1683239
    [paper]
  17. Lee Harris and Marek Grzes: Comparing Explanations between Random Forests and Artificial Neural Networks. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC). Bari, Italy, 2019.
    [paper]
  18. Farhana Ferdousi Liza and Marek Grzes: Relating RNN layers with the spectral WFA ranks in sequence modelling. Proceedings of the ACL workshop on Deep Learning and Formal Languages: Building Bridges. Florence, Italy, 2019.
    [paper]
  19. Rogerio de Lemos and Marek Grzes: Self-adaptive Artificial Intelligence. Proceedings of IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). Montreal, Canada, 2019.
    [paper]
  20. Farhana Ferdousi Liza and Marek Grzes: Improving Language Modelling with Noise Contrastive Estimation. Proceedings of AAAI. New Orleans, USA, 2018.
    [paper] [slides]
  21. Marek Grzes: Reward Shaping in Episodic Reinforcement Learning. Proceedings of the 16th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Sao Paulo, Brazil, 2017.
    [paper] [slides]
  22. Farhana Ferdousi Liza and Marek Grzes: A Spectral Method that Worked Well in the SPiCe'16 Competition. International Conference on Grammatical Inference (ICGI). Delft, The Netherlands, 2016.
    [paper] [software and slides]
  23. Farhana Ferdousi Liza and Marek Grzes: Estimating the Accuracy of Spectral Learning for HMMs. The 17th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA). LNCS vol. 9883. Varna, Bulgaria, 2016.
    [paper]
  24. Farhana Ferdousi Liza and Marek Grzes: An Improved Crowdsourcing Based Evaluation Technique for Word Embedding Methods. The First Workshop on Evaluating Vector Space Representations for NLP (RepEval at ACL). Berlin, Germany, 2016.
    [pdf]
  25. Mauro Vallati, Lukas Chrpa, Marek Grzes, Thomas L McCluskey, Mark Roberts, and Scott Sanner: The 2014 International Planning Competition: Progress and Trends. AI Magazine. Vol. 36(3), pp.90-98, AAAI, 2015.
    [pdf] [doi]
  26. Marek Grzes and Pascal Poupart: Incremental Policy Iteration with Guaranteed Escape from Local Optima in POMDP Planning. Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Istanbul, Turkey, 2015.
    [pdf] [slides] [poster] [bibtex]
  27. Marek Grzes, Pascal Poupart, Xiao Yang, and Jesse Hoey. Energy Efficient Execution of POMDP Policies. IEEE Transactions on Cybernetics. Volume: 45, Issue: 11, Pages: 2484-2497, 2015.
    [doi] [bibtex] ICAPS'15 Journal Track Presentation: [slides] [poster]
  28. Marek Grzes and Pascal Poupart: POMDP Planning and Execution in an Augmented Space. Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Paris, France, 2014.
    [pdf] [slides] [poster] [bibtex]
  29. Marcin Czajkowski, Marek Grzes, and Marek Kretowski: Multi-test Decision Tree and its Application to Microarray Data Classification. Artificial Intelligence in Medicine (AIIM). Elsevier, 2014.
    [doi] [bibtex]
  30. Marek Grzes, Jesse Hoey, Shehroz Khan, Alex Mihailidis, Stephen Czarnuch, Dan Jackson and Andrew Monk: Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems as a Translation of a Psychological Model. International Journal of Approximate Reasoning (IJAR). Special Issue of Applications of Bayesian Networks. Vol. 55(1), Part 1, pages 36-58, Elsevier, 2014.
    [pdf] [doi] [software] [bibtex]
  31. Marek Grzes, Pascal Poupart and Jesse Hoey: Controller Compilation and Compression for Resource Constrained Applications. Proceedings of International Conference on Algorithmic Decision Theory (ADT). Brussels, Belgium, 2013.
    [pdf] [slides] [bibtex]
  32. Marek Grzes, Pascal Poupart and Jesse Hoey: Isomorph-free Branch and Bound Search for Finite State Controllers. Proceedings of International Joint Conference on Artificial Intelligence (IJCAI). Beijing, China, 2013.
    [pdf] [poster] [software] [bibtex]
  33. Marek Grzes and Jesse Hoey: On the Convergence of Techniques that Improve Value Iteration. Proceedings of International Joint Conference on Neural Networks (IJCNN). Dallas, USA, 2013.
    [pdf] [poster] [bibtex]
  34. Jesse Hoey, Xiao Yang, Marek Grzes, Rene Navarro and Jesus Favela. Modeling and Learning for LaCasa, the Location And Context-Aware Safety Assistant. In NIPS 2012 Workshop on Machine Learning Approaches to Mobile Context Awareness. Lake Tahoe, NV, 2012.
  35. Marek Grzes and Jesse Hoey: Analysis of Methods for solving MDPs. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). Valencia, Spain, 2012. (pdf)
  36. Michael G. Melonis, Alex Mihailidis, Ryan Keyfitz, Marek Grzes, Jesse Hoey, and Cathy Bodine: Empowering Adults With a Cognitive Disability Through Inclusion of Non-Linear Context Aware Prompting Technology (N-CAPS). In Proceedings of the RESNA Annual Conference. Baltimore, USA, 2012. (pdf)
  37. Marcin Czajkowski, Marek Grzes, and Marek Kretowski. Multi-Test Decision Trees for Gene Expression Data Analysis. In Proceedings of the International Joint Conference on Security and Intelligent Information Systems. Warsaw, Poland, Springer, LNCS, 7053: 154-167, 2011. (url)
  38. Veronika Koltunova, Jesse Hoey, and Marek Grzes. Goal-Oriented Sensor Selection for Intelligent Phones (GOSSIP). In Proceedings of the UbiComp International Workshop on Situation, Activity and Goal Awareness. Beijing, China, ACM, 2011. (url)
  39. Marek Grzes, Jesse Hoey, Shehroz Khan, Alex Mihailidis, Stephen Czarnuch, Dan Jackson and Andrew Monk: Relational Approach to Knowledge Engineering for POMDP-based Assistance Systems with Encoding of a Psychological Model. In Proceedings of the ICAPS 2011 Workshop on Knowledge Engineering for Planning and Scheduling (KEPS), Freiburg, Germany, 2011. (pdf).
  40. Sam Devlin, Daniel Kudenko, and Marek Grzes. An empirical study of potential-based reward shaping and advice in complex, multi-agent systems. Advances in Complex Systems, 14(2): 251-278, 2011. (url)
  41. Jesse Hoey and Marek Grzes: Distributed control of situated assistance in large domains with many tasks. In Proceedings of International Conference on Automated Planning and Scheduling (ICAPS), Freiburg, Germany, 2011.
  42. Marek Grzes and Jesse Hoey: Efficient planning in R-max. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Taipei, Taiwan, 2011.
  43. Sam Devlin, Marek Grzes, and Daniel Kudenko: Multi-agent, potential-based reward shaping for robocup keepaway. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Taipei, Taiwan, 2011.
  44. Marek Grzes and Daniel Kudenko: Reward shaping and mixed resolution function approximation. In Developments in Intelligent Agent Technologies and Multi-Agent Systems: Concepts and Applications. IGI Press, 2011.
    [pdf]
  45. Sam Devlin, Marek Grzes, and Daniel Kudenko: Multi-agent Reinforcement Learning with Reward Shaping for KeepAway Takers. In Proceedings of AAMAS 2010 Workshop on Adaptive and Learning Agents (ALA 2010), Toronto, Canada, 2010.
  46. Marek Grzes and Daniel Kudenko: PAC-MDP learning with knowledge-based admissible models. In Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Toronto, Canada, 2010, ACM Press.
    [slides] [poster]
  47. Marek Grzes and Daniel Kudenko: Online learning of shaping rewards in reinforcement learning. Neural Networks, 23(4): 541-550, 2010.
    [pdf]
  48. Marek Grzes and Daniel Kudenko: Theoretical and empirical analysis of reward shaping in reinforcement learning. In Proceesings of International Conference on Machine Learning and Applications, Miami, USA, 2009. IEEE Computer Society.
  49. Marek Grzes and Daniel Kudenko: Learning shaping rewards in model-based reinforcement learning. In Proceedings of AAMAS 2009 Workshop on Adaptive Learning Agents (ALA 2009), Budapest, Hungary, 2009.
    [pdf]
  50. Marek Grzes and Daniel Kudenko: Reinforcement learning with reward shaping and mixed resolution function approximation. International Journal of Agent Technologies and Systems (IJATS), 1(2):36-54, 2009.
  51. Sam Devlin, Marek Grzes, and Daniel Kudenko: Reinforcement learning in robocup keepaway with partial observability. In Proceedings of the IEEE/WIC/ACM International Conferences on Intelligent Agent Technology (IAT 2009), Milan, Italy, 2009. IEEE Computer Society.
  52. Marek Grzes and Daniel Kudenko: Improving optimistic exploration in model-free reinforcement learning. In Proceeding of the International Conference on Adaptive and Natural Computing Algorithms (ICANNGA'09), volume 5495 of LNCS, Kuopio, Finland, 2009. Springer.
  53. Marek Grzes, Daniel Kudenko: Plan-based reward shaping for reinforcement learning. In: Proceedings of the 4th IEEE International Conference on Intelligent Systems (IS 2008), IEEE, vol. 2:22-29, Varna, Bulgaria, 2008.
    [paper] [IEEE link]
  54. Marek Grzes, Daniel Kudenko: Robustness Analysis of SARSA(lambda): Different Models of Reward and Initialisation. In: Proceedings of the 13th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA 2008), Springer LNAI, vol. 5253:144-156, Varna, Bulgaria, 2008. (url)
  55. Marek Grzes, Daniel Kudenko: Multigrid reinforcement learning with reward shaping. In: Proceedings of the 18th International Conference on Artificial Neural Networks (ICANN 2008), Springer LNCS, vol. 5163:357-366, Prague, Czech Republic, 2008. (url)
  56. Marek Grzes, Daniel Kudenko: An Empirical Analysis of the Impact of Prioritised Sweeping on the DynaQ's Performance. In: Proceedings of the 9th International Conference on Artificial Intelligence and Soft Computing (ICAISC 2008), Springer LNCS, vol. 5097:1041-1051, Zakopane, Poland, 2008. (url)
  57. Marek Grzes, Daniel Kudenko: Learning potential for reward shaping in reinforcement learning with tile coding. In Proceedings of the AAMAS'08 Workshop on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS-ALAg 2008), pages 17-23, Estoril, Portugal, 2008.
    [paper]
  58. Marek Grzes, Daniel Kudenko: Plan-based reward shaping for reinforcement learning. In Proceedings of the AAMAS'08 Workshop on Adaptive and Learning Agents and Multi-Agent Systems (ALAMAS-ALAg 2008), pages 9-16, Estoril, Portugal, 2008.
    [paper]
  59. Marek Kretowski, Marek Grzes: Global Induction of Decision Trees. Encyclopedia of Data Warehousing and Mining - 2nd Edition, Idea Group Inc., 2008.
  60. Marek Kretowski, Marek Grzes: Evolutionary Induction of Mixed Decision Trees. In Wang, J. (Ed.), Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications, Idea Group Inc., 2008.
  61. Marek Kretowski, Marek Grzes: Evolutionary Induction of Mixed Decision Trees. International Journal of Data Warehousing and Mining, vol. 3(4): 68-82, 2007. (url)
  62. Marek Grzes, Marek Kretowski: Decision Tree Approach to Microarray Data Analysis. Biocybernetics and Biomedical Engineering, vol. 27(3): 29-42, 2007.
    [pdf]
  63. Marek Kretowski, Marek Grzes: Evolutionary Induction of Decision Trees for Misclassification Cost Minimization. In: Proceedings of 8th International Conference on Adaptive and Natural Computing Algorithms (ICANNGA 2007), Warsaw, Poland, Springer LNCS, vol. 4431:1-10, 2007.
    [pdf]
  64. Marek Kretowski, Marek Grzes: Evolutionary Induction of Cost-Sensitive Decision Trees. In: Proceedings of 16th International Symposium on Methodologies for Intelligent Systems (ISMIS 2006), Bari, Italy, Springer LNAI, vol. 4203:121-126, 2006.
    [pdf] [software]
  65. Marek Kretowski, Marek Grzes: Mixed Decision Trees: An Evolutionary Approach. In: Proceedings of 8th International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2006), Krakow, Poland, Springer LNCS, vol. 4081:260-269, 2006.
  66. Marek Kretowski, Marek Grzes: Evolutionary Learning of Linear Trees with Embedded Feature Selection. In: Proceedings of 8th International Conference on Artificial Intelligence and Soft Computing (ICAISC 06), Zakopane, Poland, Springer LNCS, vol. 4029:400-409, 2006.
  67. Marek Grzes, Marek Kretowski: Decision tree approach to microarray data analysis. In: Proceedings of 6th International Seminar on Statistics and Clinical Practice. Lecture Notes in The ICB Seminar, vol. 70:105-111, 2005.
  68. Marek Kretowski, Marek Grzes: Global learning of decision trees by an evolutionary algorithm. In: Information Processing and Security Systems. Springer, pages 401-410, 2005.
    [pdf] [bibtex] [software]
  69. Marek Kretowski, Marek Grzes: Global Induction of Oblique Decision Trees: An Evolutionary Approach. In: Intelligent Information Processing and Web Mining. Proceedings of Intelligent Information Systems (IIS 2005), Springer, pages 309-319, 2005.
    [pdf] [bibtex] [software]
  70. Marek Kretowski, Marek Grzes: An evolutionary algorithm for global induction of decision trees. ACS-CISIM 04, pages 63-70, Elk, Poland, 2004.

Other Publications

  1. Theophile Champion, Marek Grzes, Lisa Bonheme, and Howard Bowman: Deconstructing deep active inference. CoRR abs/2303.01618, 1-59, 2023.
    [pdf]
  2. Jack Shannon and Marek Grzes: Reinforcement Learning using Augmented Neural Networks. CoRR abs/1806.07692, 2018.
    [paper]
  3. Daniel Kudenko and Marek Grzes: Knowledge-based reinforcement learning for data mining. In Proceedings of the AAMAS workshop on Agents and Data Mining Interaction, volume 5680 of LNCS, Budapest, Hungary, 2009. Springer-Verlag.
    [pdf]

Dissertations

Useful Links

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