Quantum image classification using principal component analysis. - Mateusz Ostaszewski; Przemyslaw Sadowski; Piotr Gawron (CoRR, 2015)

Lively quantum walks on cycles. - Przemyslaw Sadowski; Jaroslaw Adam Miszczak; Mateusz Ostaszewski (CoRR, 2015)

Superdiffusive quantum stochastic walk definable on arbitrary directed graph. - Krzysztof Domino; Adam Glos; Mateusz Ostaszewski (Quantum Inf. Comput., 2017)

Geometrical versus time-series representation of data in quantum control learning. - Mateusz Ostaszewski; Jaroslaw Adam Miszczak; Przemyslaw Sadowski (CoRR, 2018)

Properties of quantum stochastic walks from the asymptotic scaling exponent. - Krzysztof Domino; Adam Glos; Mateusz Ostaszewski; Lukasz Pawela; Przemyslaw Sadowski (Quantum Inf. Comput., 2018)

Effective transfer learning for hyperspectral image classification with deep convolutional neural networks. - Wojciech Masarczyk; Przemyslaw Glomb; Bartosz Grabowski; Mateusz Ostaszewski (CoRR, 2019)

QSWalk.jl: Julia package for quantum stochastic walks analysis. - Adam Glos; Jaroslaw Adam Miszczak; Mateusz Ostaszewski (Comput. Phys. Commun., 2019)

Approximation of quantum control correction scheme using deep neural networks. - Mateusz Ostaszewski; J. A. Miszczak; Leonardo Banchi; Przemyslaw Sadowski (Quantum Inf. Process., 2019)

An initialization strategy for addressing barren plateaus in parametrized quantum circuits. - Edward Grant; Leonard Wossnig; Mateusz Ostaszewski; Marcello Benedetti (Quantum, 2019)

Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling. - Wojciech Masarczyk; Przemyslaw Glomb; Bartosz Grabowski; Mateusz Ostaszewski (Remote. Sens., 2020)

Reinforcement learning for optimization of variational quantum circuit architectures. - Mateusz Ostaszewski; Lea M. Trenkwalder; Wojciech Masarczyk; Eleanor Scerri; Vedran Dunjko (NeurIPS, 2021)

Structure optimization for parameterized quantum circuits. - Mateusz Ostaszewski; Edward Grant; Marcello Benedetti (Quantum, 2021)

Reinforcement learning with experience replay and adaptation of action dispersion. - Pawel Wawrzynski; Wojciech Masarczyk; Mateusz Ostaszewski (CoRR, 2022)

Emergency action termination for immediate reaction in hierarchical reinforcement learning. - Michal Bortkiewicz; Jakub Lyskawa; Pawel Wawrzynski; Mateusz Ostaszewski; Artur Grudkowski; Tomasz Trzcinski (CoRR, 2022)

The Effectiveness of World Models for Continual Reinforcement Learning. - Samuel Kessler; Mateusz Ostaszewski; Michal Pawel Bortkiewicz; Mateusz Zarski; Maciej Wolczyk; Jack Parker-Holder; Stephen J. Roberts; Piotr Milos (CoLLAs, 2023)

The Tunnel Effect: Building Data Representations in Deep Neural Networks. - Wojciech Masarczyk; Mateusz Ostaszewski; Ehsan Imani; Razvan Pascanu; Piotr Milos; Tomasz Trzcinski (NeurIPS, 2023)

Enhancing variational quantum state diagonalization using reinforcement learning techniques. - Akash Kundu; Przemyslaw Bedelek; Mateusz Ostaszewski; Onur Danaci; Yash J. Patel; Vedran Dunjko; Jaroslaw Adam Miszczak (CoRR, 2023)

On consequences of finetuning on data with highly discriminative features. - Wojciech Masarczyk; Tomasz Trzcinski; Mateusz Ostaszewski (CoRR, 2023)

Subgoal Reachability in Goal Conditioned Hierarchical Reinforcement Learning. - Michal Bortkiewicz; Jakub Lyskawa; Pawel Wawrzynski; Mateusz Ostaszewski; Artur Grudkowski; Bartlomiej Sobieski; Tomasz Trzcinski (ICAART (1), 2024)

Curriculum reinforcement learning for quantum architecture search under hardware errors. - Yash J. Patel; Akash Kundu; Mateusz Ostaszewski; Xavier Bonet-Monroig; Vedran Dunjko; Onur Danaci (ICLR, 2024)

Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning. - Michal Nauman; Michal Bortkiewicz; Piotr Milos; Tomasz Trzcinski; Mateusz Ostaszewski; Marek Cygan (ICML, 2024)

Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem. - Maciej Wolczyk; Bartlomiej Cupial; Mateusz Ostaszewski; Michal Bortkiewicz; Michal Zajac 005; Razvan Pascanu; Lukasz Kucinski; Piotr Milos (ICML, 2024)

Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control. - Michal Nauman; Mateusz Ostaszewski; Krzysztof Jankowski; Piotr Milos; Marek Cygan (NeurIPS, 2024)

Learning Continually by Spectral Regularization. - Alex Lewandowski; Michal Bortkiewicz; Saurabh Kumar 004; András György 001; Dale Schuurmans; Mateusz Ostaszewski; Marlos C. Machado (ICLR, 2025)

A Case for Validation Buffer in Pessimistic Actor-Critic. - Michal Nauman; Mateusz Ostaszewski; Marek Cygan (IJCAI, 2025)

Unpacking Softmax: How Temperature Drives Representation Collapse, Compression, and Generalization. - Wojciech Masarczyk; Mateusz Ostaszewski; Tin Sum Cheng; Tomasz Trzcinski; Aurélien Lucchi; Razvan Pascanu (CoRR, 2025)

Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning. - Rafal Surdej; Michal Bortkiewicz; Alex Lewandowski; Mateusz Ostaszewski; Clare Lyle (CoRR, 2025)

PhysGym: Benchmarking LLMs in Interactive Physics Discovery with Controlled Priors. - Yimeng Chen; Piotr Piekos; Mateusz Ostaszewski; Firas Laakom; Jürgen Schmidhuber (CoRR, 2025)