Theses / dissertations
Deslis, D., 2025 (No publication date). Lakatos-style proving activity in primary school: A mixed-methods study to explore and support teachers’ mathematical knowledge and views
Doi: http://doi.org/10.17863/CAM.115816
Jiang, Q., 2025 (No publication date). Language models for verifiable mathematical automation: Interaction, integration, and autoformalization
Doi: http://doi.org/10.17863/CAM.115428
Deslis, D., 2025 (No publication date). Lakatos-style proving activity in primary school: A mixed-methods study to explore and support teachers' mathematical knowledge and views
Yang, M., 2024 (No publication date). Chinese Teachers’ Noticing in the Context of Lakatos-Style Proving Activity: A mixed-methods study to investigate its patterns and underlying mechanisms
Doi: http://doi.org/10.17863/CAM.112706
Scherer, P., 2024 (No publication date). Distributional and relational inductive biases for graph representation learning in biomedicine
Doi: 10.17863/CAM.107338
Słowik, A., 2023 (No publication date). Out-of-distribution generalisation in machine learning
Doi: 10.17863/CAM.101537
Ayers, E., 2022 (No publication date). A Tool for Producing Verified, Explainable Proofs
Doi: 10.17863/CAM.81869
Stockdill, A., 2022 (No publication date). Automating representation change across domains for reasoning
Doi: 10.17863/CAM.84749
Dimanov, B., 2021 (No publication date). Interpretable Deep Learning: Beyond Feature-Importance with Concept-based Explanations
Wang, D., 2021 (No publication date). Neural Diagrammatic Reasoning
Conference proceedings
Dong, T., Jamnik, M. and Lio, P., 2025 (No publication date). Neural Reasoning for Sure Through Constructing Explainable Models
Matjasec, U., Simidjievski, N. and Jamnik, M., 2025 (No publication date). RO-FIGS: Efficient and Expressive Tree-Based Ensembles for Tabular Data
Doi: http://doi.org/10.17863/CAM.117035
Hemker, K., Simidjievski, N. and Jamnik, M., 2025 (Accepted for publication). Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine
Doi: http://doi.org/10.17863/CAM.117032
Dong, T., Jamnik, M. and Liò, P., 2025. Neural Reasoning for Sure Through Constructing Explainable Models. AAAI,
Wenderoth, L., Hemker, K., Simidjievski, N. and Jamnik, M., 2025. Measuring Cross-Modal Interactions in Multimodal Models. AAAI,
Cheng, P., Garcia Garcia, G., Raggi, D. and Jamnik, M., 2024 (Published online). Index systems: Enumerating their forms and explaining their diversity with representational interpretive structure theory
Doi: http://doi.org/10.17863/CAM.117030
Colarusso, F., Cheng, P., Grau, R., Garcia Garcia, G., Raggi, D. and Jamnik, M., 2024 (Published online). Decoding expertise: Exploring cognitive micro-behavioural measurements for visualization competence
Doi: http://doi.org/10.17863/CAM.117031
Ciravegna, G., Zarlenga, ME., Barbiero, P., Giannini, F., Shams, Z., Garreau, D., Jamnik, M. and Cerquitelli, T., 2024. Workshop on Human-Interpretable AI CEUR Workshop Proceedings, v. 3841
Doi: 10.1145/3637528.3671499
Raman, N., Zarlenga, ME. and Jamnik, M., 2024. Understanding Inter-Concept Relationships in Concept-Based Models Proceedings of Machine Learning Research, v. 235
Margeloiu, A., Jiang, X., Simidjievski, N. and Jamnik, M., 2024. TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models. NeurIPS,
Jiang, AQ., Ziarko, A., Piotrowski, B., Li, W., Jamnik, M. and Miłos, P., 2024. Repurposing Language Models into Embedding Models: Finding the Compute-Optimal Recipe Advances in Neural Information Processing Systems, v. 37
Jiang, AQ., Li, W. and Jamnik, M., 2024. Multi-language Diversity Benefits Autoformalization Advances in Neural Information Processing Systems, v. 37
Rex, E., Zarlenga, ME., Margeloiu, A. and Jamnik, M., 2024. From Must to May: Enabling Test-Time Feature Imputation and Interventions CEUR Workshop Proceedings, v. 3841
Hemker, K., Simidjievski, N. and Jamnik, M., 2024. HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data. NeurIPS,
2024. Proceedings of the KDD Workshop on Human-Interpretable AI 2024 co-located with 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2024), Centre de Convencions Internacional de Barcelona, Spain, August 26, 2024. HI-AI@KDD, v. 3841
Lo, A., Jiang, AQ., Li, W. and Jamnik, M., 2024. End-to-End Ontology Learning with Large Language Models Advances in Neural Information Processing Systems, v. 37
Cheng, PCH., Garcia, GG., Raggi, D. and Jamnik, M., 2024. A Human Information Processing Theory of the Interpretation of Visualizations: Demonstrating Its Utility Conference on Human Factors in Computing Systems - Proceedings,
Doi: 10.1145/3613904.3642276
Jiang, X., Margeloiu, A., Simidjievski, N. and Jamnik, M., 2024. ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data Proceedings of Machine Learning Research, v. 235
Wu, L., Choi, S., Raggi, D., Stockdill, A., Garcia, GG., Colarusso, F., Cheng, PCH. and Jamnik, M., 2024. Generation of Visual Representations for Multi-Modal Mathematical Knowledge Proceedings of the AAAI Conference on Artificial Intelligence, v. 38
Doi: 10.1609/aaai.v38i21.30586
Komorowska, UJ., Mathis, S., Didi, K., Vargas, F., Lio, P. and Jamnik, M., 2024. Dynamics-Informed Protein Design with Structure Conditioning
Collins, KM., Barker, M., Espinosa Zarlenga, M., Raman, N., Bhatt, U., Jamnik, M., Sucholutsky, I., Weller, A. and Dvijotham, K., 2023. Human Uncertainty in Concept-Based AI Systems AIES 2023 - Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society,
Doi: 10.1145/3600211.3604692
Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2023. Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, v. 37
Doi: 10.1609/aaai.v37i8.26090
Zarlenga, ME., Barbiero, P., Shams, Z., Kazhdan, D., Bhatt, U., Weller, A. and Jamnik, M., 2023. Towards Robust Metrics For Concept Representation Evaluation Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023, v. 37
Doi: 10.1609/aaai.v37i10.2636126392
Colarusso, F., Cheng, PCH., Garcia Garcia, G., Stockdill, A., Raggi, D. and Jamnik, M., 2023. A novel interaction for competence assessment using micro-behaviors: Extending CACHET to graphs and charts Conference on Human Factors in Computing Systems - Proceedings,
Doi: 10.1145/3544548.3581519
Yang, M., Stylianides, AJ. and Jamnik, M., 2023. Teachers’ multiple and adaptive noticing driven by their framing of professional obligations in the context of a proving activity Proceedings of the International Group for the Psychology of Mathematics Education, v. 4
Sato, Y., Stapleton, G., Jamnik, M., Shams, Z. and Blake, A., 2023. Human Visual Consistency-Checking in the Real World Ontologies Proceedings of IEEE Symposium on Visual Languages and Human-Centric Computing, VL/HCC,
Doi: 10.1109/VL-HCC57772.2023.00044
Zarlenga, ME., Collins, K., Dvijotham, K., Weller, A., Shams, Z. and Jamnik, M., 2023. Learning to Receive Help: Intervention-Aware Concept Embedding Models. NeurIPS,
Collins, KM., Barker, M., Zarlenga, ME., Raman, N., Bhatt, U., Jamnik, M., Sucholutsky, I., Weller, A. and Dvijotham, K., 2023. Human Uncertainty in Concept-Based AI Systems. AIES,
Barbiero, P., Ciravegna, G., Giannini, F., Zarlenga, ME., Magister, LC., Tonda, A., Lió, P., Precioso, F., Jamnik, M. and Marra, G., 2023. Interpretable Neural-Symbolic Concept Reasoning Proceedings of Machine Learning Research, v. 202
Jiang, AQ., Welleck, S., Zhou, JP., Li, W., Liu, J., Jamnik, M., Lacroix, T., Lample, G. and Wu, Y., 2023. DRAFT, SKETCH, AND PROVE: GUIDING FORMAL THEOREM PROVERS WITH INFORMAL PROOFS 11th International Conference on Learning Representations, ICLR 2023,
Yang, M., Stylianides, A. and Jamnik, M., 2022 (Accepted for publication). Chinese teachers’ professional noticing of students’ reasoning in the context of Lakatos-style proving activity.
Doi: 10.17863/CAM.95065
Cheng, P., Stockdill, A., Garcia Garcia, G., Raggi, D. and Jamnik, M., 2022 (Accepted for publication). Representational interpretive structure: Theory and notation
Doi: 10.17863/CAM.94952
McGrath, S., Blake, A., Stapleton, G., Touloumis, A., Chapman, P., Jamnik, M. and Shams, Z., 2022 (Accepted for publication). Evaluating colour in concept diagrams
Doi: 10.1007/978-3-031-15146-0_14
Deslis, D., Stylianides, A. and Jamnik, M., 2022 (Accepted for publication). Two Primary School Teachers’ Mathematical Knowledge of Content, Students, and Teaching Practices relevant to Lakatos-style Investigation of Proof Tasks
Doi: 10.17863/CAM.94953
Wu, Y., Jiang, AQ., Li, W., Rabe, MN., Staats, C., Jamnik, M. and Szegedy, C., 2022. Autoformalization with Large Language Models Advances in Neural Information Processing Systems, v. 35
Scherer, P., Liò, P. and Jamnik, M., 2022. Distributed Representations of Graphs for Drug Pair Scoring Proceedings of Machine Learning Research, v. 198
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: 10.1109/VL/HCC53370.2022.9833141
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
Doi: 10.1609/aaai.v36i11.21663
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
Wu, Y., Jiang, AQ., Li, W., Rabe, MN., Staats, C., Jamnik, M. and Szegedy, C., 2022. Autoformalization with Large Language Models. NeurIPS,
Stockdill, A., Garcia, GG., Cheng, PCH., Raggi, D. and Jamnik, M., 2022. Cognitive Analysis for Representation Change CEUR Workshop Proceedings, v. 3227
Jiang, AQ., Li, W., Tworkowski, S., Czechowski, K., Odrzygózdz, T., Milos, P., Wu, Y. and Jamnik, M., 2022. Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers. NeurIPS,
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
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
Zarlenga, ME., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Lio, P. and Jamnik, M., 2022. Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off Advances in Neural Information Processing Systems, v. 35
Jiang, AQ., Li, W., Tworkowski, S., Czechowski, K., Odrzygóźdz, T., Miłos, P., Wu, Y. and Jamnik, M., 2022. Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers Advances in Neural Information Processing Systems, v. 35
Slowik, A., Gupta, A., Hamilton, WL., Jamnik, M., Holden, SB. and Pal, C., 2021. Structural Inductive Biases in Emergent Communication. CogSci,
Deslis, D., Stylianides, AJ. and Jamnik, M., 2021. PRIMARY SCHOOL TEACHERS’ MATHEMATICAL KNOWELEDGE FOR LAKATOS-STYLE PROOF INSTRUCTION Proceedings of the International Group for the Psychology of Mathematics Education, v. 2
Kazhdan, D., Dimanov, B., Terre, HA., Jamnik, M., Liò, P. and Weller, A., 2021. Is Disentanglement all you need? Comparing Concept-based &
Disentanglement Approaches
Margeloiu, A., Ashman, M., Bhatt, U., Chen, Y., Jamnik, M. and Weller, A., 2021. Do Concept Bottleneck Models Learn as Intended?
Zarlenga, ME., Shams, Z. and Jamnik, M., 2021. Efficient Decompositional Rule Extraction for Deep Neural Networks
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: 10.4230/LIPIcs.ITP.2021.4
Słowik, A., Mangla, C., Jamnik, M., Holden, S. and Paulson, L., 2020 (No publication date). Bayesian Optimisation for Heuristic Configuration in Automated Theorem Proving EPiC Series in Computing, v. 71
Doi: 10.29007/q91g
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
Gupta, A., Słowik, A., Hamilton, WL., Jamnik, M., Holden, SB. and Pal, C., 2020. Analyzing structural priors in multi-agent communication ALA 2020 - Adaptive and Learning Agents Workshop at AAMAS 2020,
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
Kazhdan, D., Dimanov, B., Jamnik, M., Liò, P. and Weller, A., 2020. Now you see me (CME): Concept-based model extraction CEUR Workshop Proceedings, v. 2699
Raggi, D., Stapleton, G., Stockdill, A., Jamnik, M., Garcia, GG. and Cheng, PCH., 2020. How to (Re)represent it? Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, v. 2020-November
Doi: 10.1109/ICTAI50040.2020.00185
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
Słowik, A., Mangla, C., Jamnik, M., Holden, SB. and Paulson, LC., 2020. Bayesian optimisation for premise selection in automated theorem proving (student abstract) AAAI 2020 - 34th AAAI Conference on Artificial Intelligence,
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
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: 10.1007/978-3-030-30179-8_6
Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., 2018 (No publication date). Reasoning with Concept Diagrams about Antipatterns Kalpa Publications in Computing, v. 1
Doi: 10.29007/4ckv
Wang, D., Jamnik, M. and Lio, P., 2018 (Accepted for publication). Investigating diagrammatic reasoning with deep neural networks
Doi: 10.1007/978-3-319-91376-6_36
Shams, Z., Jamnik, M., Stapleton, G. and Sato, Y., 2018. Icon: A diagrammatic theorem prover for ontologies Principles of Knowledge Representation and Reasoning: Proceedings of the 16th International Conference, KR 2018,
Shams, Z., Sato, Y., Jamnik, M. and Stapleton, G., 2018. Accessible reasoning with diagrams: From cognition to automation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10871 LNAI
Doi: 10.1007/978-3-319-91376-6_25
Sato, Y., Stapleton, G., Jamnik, M. and Shams, Z., 2018. Deductive reasoning about expressive statements using external graphical representations. CogSci,
Stapleton, G., Shimojima, A. and Jamnik, M., 2018. The observational advantages of euler diagrams with existential import Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 10871 LNAI
Doi: 10.1007/978-3-319-91376-6_29
Shams, Y., Jamnik, M., Stapleton, G. and Sato, Y., 2017 (Accepted for publication). Reasoning with concept diagrams about antipatterns
Doi: 10.1007/978-3-319-62075-6_18
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: 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: 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: 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
Sarkar, A., Blackwell, AF., Jamnik, M. and Spott, M., 2015. Interaction with uncertainty in visualisations Eurographics Conference on Visualization, EuroVis 2015 - Short Papers,
Doi: 10.2312/eurovisshort.20151138
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: 10.1007/978-3-662-44043-8_28
Urbas, M. and Jamnik, M., 2012. Diabelli: A Heterogeneous Proof System. IJCAR, v. 7364
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., 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: 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: 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
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,
Jamnik, M., Kerber, M. and Benzmuller, C., 2001. Towards learning new methods in proof planning 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., Bundy, A. and Green, I., 1998. DIAMOND: Diagrammatic Reasoning System Demonstration AAAI Fall Symposium - Technical Report, v. FS-98-04
Jamnik, M., Bundy, A. and Green, I., 1998. Verification of Diagrammatic Proofs AAAI Fall Symposium - Technical Report, v. FS-98-04
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,
Jamnik, M., Bundy, A. and Green, I., 1997. Automation of Diagrammatic Reasoning AAAI Fall Symposium - Technical Report, v. FS-97-03
Book chapters
Espinosa Zarlenga, M., Sankaranarayanan, S., Andrews, JTA., Shams, Z., Jamnik, M. and Xiang, A., 2025. Efficient Bias Mitigation Without Privileged Information
Doi: http://doi.org/10.1007/978-3-031-73220-1_9
Raggi, D., Stapleton, G., Stockdill, A., Garcia, GG., Cheng, PCH. and Jamnik, M., 2024. Oruga: Implementation and Use of Representational Systems Theory
Doi: http://doi.org/10.1007/978-3-031-66997-2_20
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
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
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
Stockdill, A., Raggi, D., Jamnik, M., Garcia, GG. and Cheng, PCH., 2021 (Accepted for publication). 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., 2021 (Accepted for publication). 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., 2021. Observing Strategies of Drawing Data Representations
Doi: 10.1007/978-3-030-86062-2_55
Journal articles
Buczynski, W., Steffek, F., Jamnik, M., Cuzzolin, F. and Sahakian, B., 2025. Future themes in regulating artificial intelligence in investment management Computer Law and Security Review, v. 56
Doi: http://doi.org/10.1016/j.clsr.2025.106111
Collins, KM., Jiang, Q., Frieder, S., Wong, L., Zilka, M., Bhatt, U., Lukasiewicz, T., Wu, Y., Tenenbaum, JB., Hart, W., Gowers, T., Li, W., Weller, A. and Jamnik, M., 2024 (Accepted for publication). Evaluating Language Models for Mathematics through Interactions. Proceedings of the National Academy of Sciences of the United States of America, v. 121
Doi: 10.1073/pnas.2318124121
Zarlenga, ME., Sankaranarayanan, S., Andrews, JTA., Shams, Z., Jamnik, M. and Xiang, A., 2024. Efficient Bias Mitigation Without Privileged Information. CoRR, v. abs/2409.17691
Hemker, K., Simidjievski, N. and Jamnik, M., 2024. HEALNet: Multimodal Fusion for Heterogeneous Biomedical Data Advances in Neural Information Processing Systems, v. 37
Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2024. GCondNet: A Novel Method for Improving Neural Networks on Small High-Dimensional Tabular Data Transactions on Machine Learning Research, v. 2024
Margeloiu, A., Jiang, X., Simidjievski, N. and Jamnik, M., 2024. TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models Advances in Neural Information Processing Systems, v. 37
Raman, N., Zarlenga, ME., Heo, J. and Jamnik, M., 2024. Do Concept Bottleneck Models Obey Locality? CoRR, v. abs/2401.01259
Buzzard, Z., Hemker, K., Simidjievski, N. and Jamnik, M., 2024. PATHS: A Hierarchical Transformer for Efficient Whole Slide Image Analysis. CoRR, v. abs/2411.18225
Dong, T., Jamnik, M. and Liò, P., 2024. Sphere Neural-Networks for Rational Reasoning. CoRR, v. abs/2403.15297
Wenderoth, L., Hemker, K., Simidjievski, N. and Jamnik, M., 2024. Measuring Cross-Modal Interactions in Multimodal Models. CoRR, v. abs/2412.15828
Margeloiu, A., Simidjievski, N., Liò, P. and Jamnik, M., 2023 (Published online). Weight Predictor Network with Feature Selection for Small Sample Tabular Biomedical Data Proceedings of the AAAI Conference on Artificial Intelligence, v. 37
Doi: 10.1609/aaai.v37i8.26090
Espinosa Zarlenga, M., Barbiero, P., Shams, Z., Kazhdan, D., Bhatt, U., Weller, A. and Jamnik, M., 2023 (Published online). Towards Robust Metrics for Concept Representation Evaluation Proceedings of the AAAI Conference on Artificial Intelligence, v. 37
Doi: 10.1609/aaai.v37i10.26392
Kazhdan, D., Dimanov, B., Magister, LC., Barbiero, P., Jamnik, M. and Liò, P., 2023. GCI: A (G)raph (C)oncept (I)nterpretation Framework. CoRR, v. abs/2302.04899
Zarlenga, ME., Weller, A., Collins, KM., Shams, Z., Dvijotham, K. and Jamnik, M., 2023. Learning to Receive Help: Intervention-Aware Concept Embedding Models Advances in Neural Information Processing Systems, v. 36
Jiang, AQ., Li, W. and Jamnik, M., 2023. Multilingual Mathematical Autoformalization. CoRR, v. abs/2311.03755
Leelarathna, N., Margeloiu, A., Jamnik, M. and Simidjievski, N., 2023. Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs. CoRR, v. abs/2306.15661
Lishkova, Y., Scherer, P., Ridderbusch, S., Jamnik, M., Liò, P., Ober-Blöbaum, S. and Offen, C., 2022 (Accepted for publication). Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery IFAC-PapersOnLine, v. 56
Doi: 10.1016/j.ifacol.2023.10.1457
Scherer, P., Trebacz, M., Simidjievski, N., Viñas, R., Shams, Z., Andrés-Terré, H., Jamnik, M. and Liò, P., 2022. Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. Bioinform., v. 38
Raggi, D., Stapleton, G., Jamnik, M., Stockdill, A., Garcia, GG. and Cheng, PC-H., 2022. Representational Systems Theory: A Unified Approach to Encoding, Analysing and Transforming Representations. CoRR, v. abs/2206.03172
Magister, LC., Barbiero, P., Kazhdan, D., Siciliano, F., Ciravegna, G., Silvestri, F., Liò, P. and Jamnik, M., 2022. Encoding Concepts in Graph Neural Networks. CoRR, v. abs/2207.13586
Scherer, P., Trębacz, M., Simidjievski, N., Viñas, R., Shams, Z., Terre, HA., Jamnik, M. and Liò, P., 2022. Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases. Bioinformatics, v. 38
Doi: 10.1093/bioinformatics/btab830
Buczynski, W., Steffek, F., Cuzzolin, F., Jamnik, M. and Sahakian, B., 2022. Hard Law and Soft Law Regulations of Artificial Intelligence in Investment Management Cambridge Yearbook of European Legal Studies, v. 24
Doi: 10.1017/cel.2022.10
Zarlenga, ME., Barbiero, P., Ciravegna, G., Marra, G., Giannini, F., Diligenti, M., Shams, Z., Precioso, F., Melacci, S., Weller, A., Liò, P. and Jamnik, M., 2022. Concept Embedding Models. CoRR, v. abs/2209.09056
Margeloiu, A., Simidjievski, N., Lio', P. and Jamnik, M., 2022. Graph-Conditioned MLP for High-Dimensional Tabular Biomedical Data. CoRR, v. abs/2211.06302
Zarlenga, ME., Shams, Z. and Jamnik, M., 2021. Efficient Decompositional Rule Extraction for Deep Neural Networks. CoRR, v. abs/2111.12628
Trębacz, M., Shams, Z., Jamnik, M., Scherer, P., Simidjievski, N., Terre, HA. and Liò, P., 2020 (Published online). Using ontology embeddings for structural inductive bias in gene
expression data analysis arxiv,
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,
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
Sato, Y., Stapleton, G., Jamnik, M. and Shams, Z., 2019. Human inference beyond syllogisms: an approach using external graphical representations. Cogn Process, v. 20
Doi: 10.1007/s10339-018-0877-2
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
Oliver, I., Howse, J., Stapleton, G., Shams, Z. and Jamnik, M., 2019. Exploring and conceptualising attestation Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11530 LNAI
Doi: 10.1007/978-3-030-23182-8_10
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: 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: 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: 10.1109/VLHCC.2013.6645238
Benzmüller, C., Sorge, V., Jamnik, M. and Kerber, M., 2008. Combined reasoning by automated cooperation Journal of Applied Logic, v. 6
Doi: 10.1016/j.jal.2007.06.003
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: 10.1016/j.entcs.2005.11.025
Jamnik, M. and Bundy, A., 2005. Psychological validity of schematic proofs LECT NOTES ARTIF INT, v. 2605
Bundy, A., Jamnik, M. and Fugard, A., 2005. What is a proof? PHILOS T ROY SOC A, v. 363
Doi: 10.1098/rsta.2005.1651
Jamnik, M. and Janičić, P., 2003. Learning strategies for mechanised building of decision procedures Electronic Notes in Theoretical Computer Science, v. 86
Doi: 10.1016/S1571-0661(04)80662-5
Jamnik, M., Kerber, M., Pollet, M. and Benzmuller, C., 2003. Automatic learning of proof methods in proof planning LOGIC J IGPL, v. 11
Doi: 10.1093/jigpal/11.6.647
Jamnik, M., Bundy, A. and Green, I., 1999. On automating diagrammatic proofs of arithmetic arguments Journal of Logic, Language and Information, v. 8
Doi: 10.1023/A:1008323427489
Benzmüller, C., Jamnik, M., Kerber, M. and Sorge, V., 1999. Agent based mathematical reasoning Electronic Notes in Theoretical Computer Science, v. 23
Doi: 10.1016/S1571-0661(05)82522-8
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
