Book chapters
Hemker, K., Shams, Z. and Jamnik, M., 2023. CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programs
Doi: 10.1007/978-3-031-39539-0_6
Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Liò, P., 2023. Concept Distillation in Graph Neural Networks
Doi: 10.1007/978-3-031-44070-0_12
Cheng, PCH., Stockdill, A., Garcia Garcia, G., Raggi, D. and Jamnik, M., 2022. Representational Interpretive Structure: Theory and Notation
Doi: 10.1007/978-3-031-15146-0_4
McGrath, S., Blake, A., Stapleton, G., Touloumis, A., Chapman, P., Jamnik, M. and Shams, Z., 2022. Evaluating Colour in Concept Diagrams
Doi: 10.1007/978-3-031-15146-0_14
Jamnik, M., 2021. Logical reasoning with diagrams
Stockdill, A., Raggi, D., Jamnik, M., Garcia, GG. and Cheng, PCH., Considerations in Representation Selection for Problem Solving: A Review
Doi: 10.1007/978-3-030-86062-2_4
Cheng, PCH., Garcia, GG., Raggi, D., Stockdill, A. and Jamnik, M., Cognitive Properties of Representations: A Framework
Doi: 10.1007/978-3-030-86062-2_43
Colarusso, F., Cheng, PCH., Garcia, GG., Raggi, D. and Jamnik, M., Observing Strategies of Drawing Data Representations
Doi: 10.1007/978-3-030-86062-2_55
Conference proceedings
Cardozo, S., Montero, GI., Kazhdan, D., Dimanov, B., Wijaya, M., Jamnik, M. and Lio, P., 2022. Explainer Divergence Scores (EDS): Some Post-Hoc Explanations May be Effective for Detecting Unknown Spurious Correlations CEUR Workshop Proceedings, v. 3318
Słowik, A., Bottou, L., Holden, SB. and Jamnik, M., 2022. On the Relation between Distributionally Robust Optimization and Data Curation (Student Abstract) Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, v. 36
Raggi, D., Stapleton, G., Jamnik, M., Stockdill, A., Garcia, GG. and Cheng, PCH., 2022. Oruga: An Avatar of Representational Systems Theory CEUR Workshop Proceedings, v. 3227
Stockdill, A., Garcia, GG., Cheng, PCH., Raggi, D. and Jamnik, M., 2022. Cognitive Analysis for Representation Change CEUR Workshop Proceedings, v. 3227
Stockdill, A., Stapleton, G., Raggi, D., Jamnik, M., Garcia, GG. and Cheng, PCH., 2022. Examining Experts' Recommendations of Representational Systems for Problem Solving Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, v. 2022-September
Doi: http://doi.org/10.1109/VL/HCC53370.2022.9833141
Apperly, I., Bundy, A., Cohn, A., Colton, S., Cussens, J., D'Avila Garcez, A., Hahn, U., Jamnik, M., Jay, C., Mareschal, D., Sammut, C., Schmid, U., Seed, A., Stahl, BC., Steedman, M. and Tamaddoni-Nezhad, A., 2022. Preface CEUR Workshop Proceedings, v. 3227
Deslis, D., Stylianides, A. and Jamnik, M., 2021. Primary school teachers’ mathematical knowledge for Lakatos-style proof instruction Proceedings of the 44th Conference of the International Group for the Psychology of Mathematics Education, v. 2
Ayers, EW., Jamnik, M. and Gowers, WT., 2021. A graphical user interface framework for formal verification Leibniz International Proceedings in Informatics, LIPIcs, v. 193
Doi: http://doi.org/10.4230/LIPIcs.ITP.2021.4
Kazhdan, D., Dimanov, B., Terre, HA., Jamnik, M., Liò, P. and Weller, A., 2021. Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
Stockdill, A., Raggi, D., Jamnik, M., Garcia, GG., Sutherland, HEA., Cheng, PCH. and Sarkar, A., 2020. Correspondence-based analogies for choosing problem representations Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, v. 2020-August
Doi: 10.1109/VL/HCC50065.2020.9127258
Dimanov, B., Bhatt, U., Jamnik, M. and Weller, A., 2020. You shouldn’t trust me: Learning models which conceal unfairness from multiple explanation methods. Frontiers in Artificial Intelligence and Applications: ECAI 2020,
Doi: 10.3233/FAIA200380
Stockdill, A., Raggi, D., Jamnik, M., Garcia, GG., Sutherland, HEA., Cheng, PCH. and Sarkar, A., 2020. Cross-domain correspondences for explainable recommendations CEUR Workshop Proceedings, v. 2582
Wang, D., Jamnik, M. and Lio, P., 2020. Abstract Diagrammatic Reasoning with Multiplex Graph Networks
Raggi, D., Stockdill, A., Jamnik, M., Garcia Garcia, G., Sutherland, HEA. and Cheng, PCH., 2020. Dissecting Representations Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 12169 LNAI
Doi: 10.1007/978-3-030-54249-8_11
Slowik, A., Mangla, C., Jamnik, M., Holden, SB. and Paulson, LC., 2020. Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), v. 34
Doi: 10.1609/aaai.v34i10.7232
Cheng, PC-H., Garcia, GG., Sutherland, HEA., Raggi, D., Stockdill, A. and Jamnik, M., 2019. Elucidating the Cognitive Anatomy of Representation Systems. CogSci,
Raggi, D., Stockdill, A., Jamnik, M., Garcia Garcia, G., Sutherland, HEA. and Cheng, PCH., 2019. Inspection and Selection of Representations Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11617 LNAI
Doi: 10.1007/978-3-030-23250-4_16
Ayers, EW., Gowers, WT. and Jamnik, M., 2019. A Human-Oriented Term Rewriting System Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11793 LNAI
Doi: http://doi.org/10.1007/978-3-030-30179-8_6
Sato, Y., Stapleton, G., Jamnik, M. and Shams, Z., 2018. Deductive reasoning about expressive statements using external graphical representations. CogSci,
Sato, Y., Stapleton, G., Jamnik, M., Shams, Z. and Blake, A., 2017. How network-based and set-based visualizations aid consistency checking in ontologies ACM International Conference Proceeding Series, v. Part F130152
Doi: http://doi.org/10.1145/3105971.3105988
Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., 2017. Reasoning with concept diagrams about antipatterns in ontologies Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10383 LNAI
Doi: http://doi.org/10.1007/978-3-319-62075-6_18
2016. Diagrammatic Representation and Inference - 9th International Conference, Diagrams 2016, Philadelphia, PA, USA, August 7-10, 2016, Proceedings Diagrams, v. 9781
Stapleton, G., Jamnik, M. and Shimojima, A., 2016. Effective representation of information: Generalizing free rides Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9781
Doi: 10.1007/978-3-319-42333-3_28
Sarkar, A., Spott, M., Blackwell, AF. and Jamnik, M., 2016. Visual discovery and model-driven explanation of time series patterns Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, v. 2016-November
Doi: http://doi.org/10.1109/VLHCC.2016.7739668
Sarkar, A., Jamnik, M., Blackwell, AF. and Spott, M., 2015. Interactive visual machine learning in spreadsheets Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC, v. 2015-December
Doi: 10.1109/VLHCC.2015.7357211
Urbas, M. and Jamnik, M., 2014. A framework for heterogeneous reasoning in formal and informal domains Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 8578 LNAI
Doi: http://doi.org/10.1007/978-3-662-44043-8_28
Urbas, M., Jamnik, M., Stapleton, G. and Flower, J., 2012. Speedith: A Diagrammatic Reasoner for Spider Diagrams. Diagrams, v. 7352
Urbas, M. and Jamnik, M., 2012. Diabelli: A Heterogeneous Proof System. IJCAR, v. 7364
Urbas, M. and Jamnik, M., 2011. Heterogeneous proofs: Spider diagrams meet higher-order provers Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6898 LNCS
Doi: http://doi.org/10.1007/978-3-642-22863-6_29
Urbas, M. and Jamnik, M., 2010. Heterogeneous Reasoning in Real Arithmetic DIAGRAMMATIC REPRESENTATION AND INFERENCE, v. 6170
Ridsdale, M., Jamnik, M., Benton, N. and Berdine, J., 2008. Diagrammatic reasoning in separation logic Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 5223 LNAI
Doi: http://doi.org/10.1007/978-3-540-87730-1_50
Benzmuller, C., Sorge, V., Jamnik, M. and Kerber, M., 2005. Can a higher-order and a first-order theorem prover cooperate? LOGIC FOR PROGRAMMING, ARTIFICIAL INTELLIGENCE, AND REASONING, PROCEEDINGS, v. 3452
Winterstein, D., Bundy, A., Gurr, C. and Jamnik, M., 2004. An experimental comparison of diagrammatic and algebraic logics DIAGRAMMATIC REPRESENTATION AND INFERENCE, v. 2980
Winterstein, D., Bundy, A. and Jamnik, M., 2004. On differences between the real and physical plane DIAGRAMMATIC REPRESENTATION AND INFERENCE, v. 2980
Jamnik, M., Kerber, M. and Pollet, M., 2002. Automatic learning in proof planning ECAI 2002: 15TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, PROCEEDINGS, v. 77
Jamnik, M., Kerber, M. and Pollet, M., 2002. Learn Omega-matic: System Description. CADE, v. 2392
Winterstein, D., Bundy, A., Gurr, C. and Jamnik, M., 2002. Using animation in diagrammatic theorem proving DIAGRAMMATIC REPRESENTATION AND INFERENCE, v. 2317
Jamnik, M., Kerber, M. and Benzmuller, C., 2001. Towards learning new methods in proof planning SYMBOLIC COMPUTATION AND AUTOMATED REASONING,
Benzmüller, C., Jamnik, M., Kerber, M. and Sorge, V., 2001. Experiments with an Agent-Oriented Reasoning System. KI/ÖGAI, v. 2174
Benzmuller, C., Jamnik, M., Kerber, M. and Sorge, V., 2001. Resource guided concurrent deduction SYMBOLIC COMPUTATION AND AUTOMATED REASONING,
Winterstein, D., Bundy, A. and Jamnik, M., 2000. A proposal for automating diagrammatic reasoning in continuous domains THEORY AND APPLICATION OF DIAGRAMS, PROCEEDINGS, v. 1889
Jamnik, M., 1997. Automation of Diagrammatic Proofs in Mathematics. IJCAI,
Jamnik, M., Bundy, A. and Green, P., 1997. Automation of diagrammatic reasoning IJCAI-97 - PROCEEDINGS OF THE FIFTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 AND 2,
Stapleton, G., Shimojima, A. and Jamnik, M., The Observational Advantages of Euler Diagrams with Existential Import
Slowik, A., Mangla, C., Jamnik, M., Holden, S. and Paulson, L., Bayesian Optimisation for Premise Selection in Automated Theorem Proving (Student Abstract) Proceedings of the AAAI Conference on Artificial Intelligence,
Słowik, A., Mangla, C., Jamnik, M., Holden, S. and Paulson, L., Bayesian Optimisation for Heuristic Configuration in Automated Theorem Proving EPiC Series in Computing,
Doi: 10.29007/q91g
Yang, M., Stylianides, A. and Jamnik, M., Chinese teachers’ professional noticing of students’ reasoning in the context of Lakatos-style proving activity.
Colarusso, F., Cheng, P., Garcia Garcia, G., Stockdill, A., Raggi, D. and Jamnik, M., A novel interaction for competence assessment using micro-behaviors: Extending CACHET to graphs and charts
Doi: http://doi.org/10.1145/3544548.3581519
Stockdill, A., Stapleton, G., Raggi, D., Jamnik, M., Garcia Garcia, G. and Cheng, P., Examining experts’ recommendations of representational systems for problem solving
Cheng, P., Stockdill, A., Garcia Garcia, G., Raggi, D. and Jamnik, M., Representational interpretive structure: Theory and notation International Conference on Theory and Application of Diagrams,
Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., iCon: A Diagrammatic Theorem Prover for Ontologies Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference (KR2018),
McGrath, S., Blake, A., Stapleton, G., Touloumis, A., Chapman, P., Jamnik, M. and Shams, Z., Evaluating colour in concept diagrams Diagrammatic Representation and Inference, 13th International Conference, Diagrams 2022, v. LNAI 13462
Doi: http://doi.org/10.1007/978-3-031-15146-0_14
Deslis, D., Stylianides, A. and Jamnik, M., Two Primary School Teachers’ Mathematical Knowledge of Content, Students, and Teaching Practices relevant to Lakatos-style Investigation of Proof Tasks
Scherer, P., Lio, P. and Jamnik, M., Distributed representations of graphs for drug pair scoring Proceedings of the First Learning on Graphs Conference (LoG 2022), v. PMLR 198
Wu, Y., Jiang, Q., Li, W., Rabe, M., Staats, C., Jamnik, M. and Szegedy, C., Autoformalization with large language models https://openreview.net/pdf?id=IUikebJ1Bf0,
Jiang, Q., Li, W., Tworkowski, S., Czechowski, K., Odrzygózdz, T., Miłos ́, P., Wu, Y. and Jamnik, M., Thor: Wielding hammers to integrate language models and automated theorem provers Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2022,
Espinosa Zarlenga, M., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Lio, P. and Jamnik, M., Concept embedding models: Beyond the Accuracy-Explainability Trade-Off
Margeloiu, A., Ashman, M., Bhatt, U., Chen, Y., Jamnik, M. and Weller, A., Do Concept Bottleneck Models Learn as Intended?
Jiang, Q., Welleck, S., Zhou, JP., Li, W., Liu, J., Jamnik, M., Lacroix, T., Lample, G. and Wu, Y., Draft, sketch, and prove: Guiding formal theorem provers with informal proofs https://openreview.net/pdf?id=SMa9EAovKMC,
Espinosa Zarlenga, M., Barbiero, P., Shams, Z., Kazhdan, D., Bhatt, U., Weller, A. and Jamnik, M., Towards robust metrics for concept representation evaluation AAAI Press,
Margeloiu, A., Simidjievski, N., Lio, P. and Jamnik, M., Weight predictor network with feature selection for small sample tabular biomedical data
Sarkar, A., Blackwell, A., Jamnik, M. and Spott, M., Interaction with uncertainty in visualisations
Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., Reasoning with Concept Diagrams about Antipatterns Kalpa Publications in Computing,
Doi: 10.29007/4ckv
Kazhdan, D., Dimanov, B., Jamnik, M., Lio, P. and Weller, A., Now You See Me (CME): Concept-based Model Extraction
Zarlenga, ME., Shams, Z. and Jamnik, M., Efficient Decompositional Rule Extraction for Deep Neural Networks Advances in Neural Information Processing Systems,
Raggi, D., Stapleton, G., Stockdill, A., Jamnik, M., Garcia Garcia, G. and Cheng, P., How to (Re)represent it?
Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., Accessible Reasoning with Diagrams: from Cognition to Automation
Journal articles
Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Jamnik, M. and Lio, P., 2022. Encoding Concepts in Graph Neural Networks
Wijaya, MA., Kazhdan, D., Dimanov, B. and Jamnik, M., 2021. Failing Conceptually: Concept-Based Explanations of Dataset Shift
Kazhdan, D., Dimanov, B., Jamnik, M., Liò, P. and Weller, A., 2020. Now You See Me (CME): Concept-based Model Extraction. CoRR, v. abs/2010.13233
Kazhdan, D., Dimanov, B., Jamnik, M. and Liò, P., 2020. MEME: Generating RNN Model Explanations via Model Extraction
Margeloiu, A., Simidjievski, N., Jamnik, M. and Weller, A., 2020. Improving Interpretability in Medical Imaging Diagnosis using
Adversarial Training
Kiat, NQW., Wang, D. and Jamnik, M., 2020. Pairwise Relations Discriminator for Unsupervised Raven's Progressive
Matrices
Scherer, P., Trȩbacz, M., Simidjievski, N., Shams, Z., Terre, HA., Liò, P. and Jamnik, M., 2020. Incorporating network based protein complex discovery into automated
model construction
Zhao, Y., Wang, D., Bates, D., Mullins, R., Jamnik, M. and Lio, P., 2020. Learned Low Precision Graph Neural Networks
Wang, D., Jamnik, M. and Lio, P., 2020. ABSTRACT DIAGRAMMATIC REASONING WITH MULTIPLEX GRAPH NETWORKS 8th International Conference on Learning Representations, ICLR 2020,
Wang, D., Jamnik, M. and Lio, P., 2019. Unsupervised and interpretable scene discovery with
Discrete-Attend-Infer-Repeat
Słowik, A., Mangla, C., Jamnik, M., Holden, SB. and Paulson, LC., 2019. Bayesian Optimisation with Gaussian Processes for Premise Selection
Scherer, P., Andres-Terre, H., Lio, P. and Jamnik, M., 2019. Decoupling feature propagation from the design of graph auto-encoders
Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M. and Liò, P., 2019. Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice. Front Genet, v. 10
Doi: 10.3389/fgene.2019.01205
Linker, S., Burton, J. and Jamnik, M., 2017. Tactical diagrammatic reasoning Electronic Proceedings in Theoretical Computer Science, EPTCS, v. 239
Doi: http://doi.org/10.4204/EPTCS.239.3
Stapleton, G., Jamnik, M. and Shimojima, A., 2017. What Makes an Effective Representation of Information: A Formal Account of Observational Advantages Journal of Logic, Language and Information, v. 26
Doi: 10.1007/s10849-017-9250-6
Urbas, M., Jamnik, M. and Stapleton, G., 2015. Speedith: A Reasoner for Spider Diagrams Journal of Logic, Language and Information, v. 24
Doi: 10.1007/s10849-015-9229-0
Sarkar, A., Blackwell, AF., Jamnik, M. and Spott, M., 2014. Teach and try: A simple interaction technique for exploratory data modelling by end users Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC,
Doi: http://doi.org/10.1109/VLHCC.2014.6883022
Stapleton, G., Jamnik, M. and Urbas, M., 2013. Designing inference rules for spider diagrams Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC,
Doi: http://doi.org/10.1109/VLHCC.2013.6645238
Jamnik, M., 2008. How can machines reason? AISB 2008 Convention: Communication, Interaction and Social Intelligence - Proceedings of the AISB 2008 Symposium on Logic and the Simulation of Interaction and Reasoning,
Dennis, LA., Jamnik, M. and Pollet, M., 2006. On the comparison of proof planning systems: γcLAM Ωmega and IsaPlanner Electronic Notes in Theoretical Computer Science, v. 151
Doi: http://doi.org/10.1016/j.entcs.2005.11.025
Jamnik, M. and Bundy, A., 2005. Psychological validity of schematic proofs LECT NOTES ARTIF INT, v. 2605
Jamnik, M., Bundy, A. and Green, I., 1999. On automating diagrammatic proofs of arithmetic arguments Journal of Logic, Language and Information, v. 8
Doi: http://doi.org/10.1023/A:1008323427489
Trębacz, M., Shams, Z., Jamnik, M., Scherer, P., Simidjievski, N., Terre, HA. and Liò, P., Using ontology embeddings for structural inductive bias in gene
expression data analysis arxiv,
Bundy, A. and Jamnik, M., A Common Type of Rigorous Proof that Resists Hilbert’s Programme Proof Technology in Mathematics Research and Teaching,
Sato, Y., Stapleton, G., Jamnik, M. and Shams, Z., Human inference beyond syllogisms: an approach using external graphical representations Cognitive Processing,
Scherer, P., Trębacz, M., Simidjievski, N., Viñas, R., Shams, Z., Terre, HA., Jamnik, M. and Liò, P., Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. Bioinformatics,
Doi: 10.1093/bioinformatics/btab830
Oliver, I., Howse, J., Gem, S., Shams, Z. and Jamnik, M., Exploring and Conceptualising Attestation International Conference on Conceptual Structures ICCS2019,
Theses / dissertations
Ayers, E., 2022. A Tool for Producing Verified, Explainable Proofs
Stockdill, A., 2022. Automating representation change across domains for reasoning
Dimanov, B., Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations
Wang, D., Neural Diagrammatic Reasoning
Internet publications
Słowik, A., Gupta, A., Hamilton, WL., Jamnik, M. and Holden, SB., 2020. Towards Graph Representation Learning in Emergent Communication
Słowik, A., Gupta, A., Hamilton, WL., Jamnik, M., Holden, SB. and Pal, C., 2020. Structural Inductive Biases in Emergent Communication
Zhao, Y., Wang, D., Gao, X., Mullins, R., Lio, P. and Jamnik, M., 2020. Probabilistic Dual Network Architecture Search on Graphs
Books
Jamnik, M., Uesaka, Y. and Schwartz, SE., 2016. Preface
Jamnik, M., Goel, AK. and Narayanan, NH., 2010. Preface
Jamnik, M., Goel, A. and Narayanan, H., 2010. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface
2010. Diagrammatic Representation and Inference, 6th International Conference, Diagrams 2010, Portland, OR, USA, August 9-11, 2010. Proceedings
