About me

I’m a PhD student at the Jagiellonian University, working on machine learning with the GMUM group under supervision of Prof. Jacek Tabor. My main research interests are centered around the issue of efficiency in deep learning, highlighting the following questions in particular:

  • Continual Learning – how to remember the past and reuse it efficiently when learning from a stream of data.
  • Conditional Computation – how to adapt the computing power of the model to a given example.
  • Generative Models – how to perform weakly supervised conditional generation and adapt existing models for conditional generation.
  • Reinforcement Learning – how to increase the sample efficiency and leverage privilged data during training.
  • Imitation Learning – how to combat distribution shift when learning from expert data.


Selected publications

General idea of InterContiNet
Continual Learning with Guarantees via Weight Interval Constraints
Maciej Wołczyk*, Karol Piczak*, Bartosz Wójcik, Łukasz Pustelnik, Paweł Morawiecki, Jacek Tabor, Tomasz Trzciński, Przemysław Spurek
ICML 2022
Scheme of PluGeN
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models
Maciej Wołczyk*, Magdalena Proszewska*, Łukasz Maziarka, Maciej Zięba, Patryk Wielopolski, Rafał Kurczab, Marek Śmieja
AAAI 2022
Scheme of the Zero Time Waste model
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
Maciej Wołczyk*, Bartosz Wójcik*, Klaudia Bałazy, Igor Podolak, Jacek Tabor, Marek Śmieja, Tomasz Trzciński
NeurIPS 2021
[Paper] [Code]
CW20 from Continual World
Continual World: A Robotic Benchmark For Continual Reinforcement Learning
Maciej Wołczyk*, Michał Zając*, Razvan Pascanu, Łukasz Kuciński, Piotr Miłoś
NeurIPS 2021
[Webpage] [Paper] [Code]