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A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
other_papers
Explainable Machine Learning at Microsoft MLADS
Published:
Had the privilege to lead explainable AI research at Microsoft Ireland during an internship, utilizing petabytes of data to develop interpretable machine learning solutions for both theoretical and practical applications, presented at the internal MLADS conference.
Recommended citation: Tyrolski, M. (2022). Explainable machine learning at Microsoft MLADS (Internal Microsoft MLADS Conference presentation).
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Enhancing Antigenic Peptide Discovery
Published:
This study pinpoints evaluation pitfalls in MHC-I presentation prediction and proposes a unified framework to standardize methodology. It also introduces a transformer model trained on interspecies data, markedly improving peptide–MHC-I binding accuracy and generalization across diverse peptides, alleles, and proteins.
Recommended citation: Giziński, S., Preibisch, G., Kucharski, P., Tyrolski, M., Rembalski, M., Grzegorczyk, P., & Gambin, A. (2024). Enhancing antigenic peptide discovery: Improved MHC-I binding prediction and methodology. Methods, 224, 1–9. https://doi.org/10.1016/j.ymeth.2024.01.016
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publications
Hierarchical Transformers Are More Efficient Language Models
Published in NAACL 2022, 2022
Hourglass: a hierarchical Transformer with down/upsampling layers that improves long-sequence modeling efficiency; SOTA on ImageNet32 and strong enwik8 performance.
Recommended citation: Nawrot, P., Tworkowski, S., Tyrolski, M., Kaiser, Ł., Wu, Y., Szegedy, C., & Michalewski, H. (2021). Hierarchical transformers are more efficient language models. arXiv preprint arXiv:2110.13711.
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Adjusting Planning Horizon with Adaptive Subgoal Search
Published in ICLR 2023 (Top-5%, Oral), 2023
AdaSubS adaptively adjusts planning horizon via diverse subgoals + fast reachability filtering; efficient on Sokoban, Rubik’s Cube, INT.
Recommended citation: Zawalski, M., Tyrolski, M., Czechowski, K., Odrzygóźdź, T., Stachura, D., Piękos, P., Wu, Y., Kuciński, Ł., & Miłoś, P. (2022). Fast and precise: Adjusting planning horizon with adaptive subgoal search. arXiv preprint arXiv:2206.00702.
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What Matters in Hierarchical Search for Combinatorial Problems?
Published in ICLR 2024 (Generative Models for Decision Making), 2024
Empirical analysis of properties shaping hierarchical search in combinatorial reasoning; guidelines for robust comparison + future design.
Recommended citation: Zawalski, M., Góral, G., Tyrolski, M., Wiśnios, E., Budrowski, F., Cygan, M., Kuciński, Ł., & Miłoś, P. (2024). What matters in hierarchical search for combinatorial reasoning problems? arXiv preprint arXiv:2406.03361.
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OpenGVL: Benchmarking Visual Temporal Progress for Data Curation
Published in CoRL 2025 workshop, 2025
Benchmark and toolkit for evaluating VLMs’ sense of progress in robotics via Value-Order Correlation (VOC); enables automated dataset curation from videos.
Recommended citation: Budzianowski, P., Wiśnios, E., Góral, G., Tyrolski, M., Kulakov, I., Petrenko, V., & Walas, K. (2025). OpenGVL - Benchmarking Visual Temporal Progress for Data Curation. arXiv:2509.17321.
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