Společný seminář katedry teoretické informatiky a matematické logiky MFF UK a oddělění umělé inteligence ústavu informatiky AV ČR
Organized jointly by the Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathemeatics and Physics of the Charles University, and by the Department of Artificial Intelligence, Institute of Computer Science of the Czech Academy of Sciences
Kontakt: / Contact:
Martin Holeňa
www.cs.cas.cz/~martin
martin@cs.cas.cz
Valentin Margraf. Imprecise Acquisitions in Bayesian Optimization
Valentin proposes maintaining multiple GP models with different hyperparameters and evaluating acquisition functions separately under each to capture model disagreement, aggregating either at the acquisition level combining AF values, or decision level comparing optimal points via stochastic dominance.
Přehled seminářů v roce 2025 / List of seminars in 2025
19. 2. |
Ondřej Marálek and Hubert Beck |
Machine-learned interatomic potentials for molecular dynamics: committees and foundation models | |
18. 12. |
Valentin Margraf |
Imprecise Acquisitions in Bayesian Optimization | |
4. 12. |
Matthias Wolf |
CVKAN: Complex-Valued Kolmogorov-Arnold Networks |
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20. 11. |
Antonio Purificato |
Recommendation Systems: From Traditional Approaches to Sheaf Neural Networks |
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6. 11. |
Christos Fragkathoulas |
Counterfactual Explanations in ML: Concepts, Methods, and Fairness Audits |
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23. 10. |
Francesco Caso |
Entropy, Renormalization, and Structure: Toward Physically-Inspired Learning Across Domains |
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9. 10. |
Arno Geimer |
To the Winner Go the Spoils: Decentralized Machine Learning and the Fair Rewarding of Participation |
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22. 5. |
Victor Letzelter |
Learning under ambiguity through multiple hypotheses and quantization |
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24. 4. |
Jelle Hüntelmann |
Learning to be uncertain: Of soft labels and soft losses |
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10. 4. |
Marcel Kühn |
Anti-Correlated Noise in Epoch-Based Stochastic Gradient Descent and its Implications |
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27. 3. |
Andreas Opedal |
Systematic Analysis of the Arithmetic Reasoning Capabilities of LLMs |
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13. 3. |
Antoni Kowalczuk |
Image Autoregressive Models Leak More Training Data Than Diffusion Models |
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27. 2. |
Thomas Kleine Buening |
Strategic Interactive Decision-Making |
Přehled seminářů v roce 2024 / List of seminars in 2024
19. 12. |
Peter Blohm |
Probably Approximately Global Robustness Certification |
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5. 12. |
Vlad Yorsh |
Structured State-Space Neural-Network Models |
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21. 11. |
Jonas Hübotter |
Transductive Active Learning for Fine-Tuning Large (Language) Models |
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7. 11. |
Krzysztof Kacprzyk |
AI4Science: Discovering Governing Equations and Beyond |
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10. 10. |
Ondřej Tichý |
Bayesian Regression and Its Application to Atmospheric Emissions Estimation |
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23. 5. |
Pierre Nicolay |
Kinematic-Aware Neural Network for Dynamics Modeling |
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25. 4. |
Balint Gyevnar |
How Do We make Explainable AI Work for People? |
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11. 4. |
Alex Goodall |
Theory and Applications of Approximate Model-based Shielding for Safe Reinforcement Learning |
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28. 3. |
Simon Rittel |
Bayesian Causal Structure Learning |
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14. 3. |
Oliver Sutton |
Can Adversarial Robustness Be Certified for Classifiers Learning High Dimensional Data? |
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29. 2. |
Michal Znalezniak |
Contrastive Hierarchical Clustering |
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1. 2. |
Tomá Pevný |
Tractable Probabilistic Models for Hierarchical Data |
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