Most recent news & events

Our paper titled "Consistent algorithms for multi-label classification with macro-at-k metrics" was accepted to ICLR 2024.
ML in PL Conference 2023, that I co-organized, took place in Warsaw
Our paper titled "Generalized test utilities for long-tail performance in extreme multi-label classification" was accepted to NeurIPS 2023.
MLSS^S 2023 (Machine Learning Summer School on Applications in Science) that I co-organized took place at Jagiellonian University in Kraków.
A new version of  ViZDoom (1.2.0) library was released with official  Gymnasium wrapper, support for ARM/Arch64 architectures including M1/M2 Macs and other improvments.
A new version of  Vowpal Wabbit (9.7.0) was released with my improved implementation of PLT reduction.
Me and  ViZDoom project joined  The Farama Foundation.
ML in PL Conference 2022, that I co-organized, took place in Warsaw.
I started an internship at  Yahoo in Paris, France.
Our paper titled "On missing labels, long-tails and propensities in extreme multi-label classification" was accepted to KDD'22.
A new version of  ViZDoom (1.1.12) library was released with an official  OpenAI Gym wrapper.
I joined the management board of  ML in PL Association.
A new version of  ViZDoom (1.1.11) library was released that introduce access to the new audio buffer allowing agents to not only see but also hear an environment.
ML in PL Conference 2021, that I co-organized, took place virtualy.
Our paper "ViZDoom Competitions: Playing Doom From Pixels" received  2022 IEEE ToG Outstanding Paper Award.
Our short paper titled "Propensity-scored Probabilistic Label Trees" was accepted to SIGIR'21.
Our paper titled "Efficient Set-Valued Prediction in Multi-Class Classification" was accepted for publication in Data Mining and Knowledge Discovery.
Our paper titled "Online probabilistic label trees" was accepted to AISTATS 2021.
I gave a talk titled "Probabilistic Label Trees in Vowpal Wabbit" at Vowpal Wabbit Workshop at NeurIPS'20. [ video]
I gave a talk titled "Extreme classification: applications and algorithms" on GHOST Day: AMLC'20. [ video]

Scientific publications


ViZDoom is a library that allows creating reinforcement learning environments based on the Doom engine. It is primarily intended for research in machine visual learning and deep reinforcement learning, in particular. The library is written in C++ and provides Python API and wrappers for popular Gymnasium/OpenAI Gym interface. It is multi-platform, lightweight, fast, and provides many useful tools for creating custom environments.

[ docs] [ code] [ website]

xCOLUMNs is a small Python library that aims to implement different methods for the optimization of a general family of metrics that can be defined on a confusion matrix. The library provides an efficient implementation of the different optimization methods that easily scale to problems at a large scale.

[ docs] [ code]


Extremely simple and fast library for extreme multi-class and multi-label classification.
[ code]


Extension of fastText library for multi-label classification including extreme cases with hundreds of thousands and millions of labels.
[ code]

ML in PL

I'm proud to be a member of the awesome ML in PL Association, a non-profit organization devoted to fostering the machine learning community in Poland and around the world.

From 2021 I'm a member of the management board of ML in PL Association and try to support other members of the association in the organization of great machine learning-related events, including taking care of the recruitment of new members of the association and leading the development of new websites for ML in PL Conference 2022, ML in PL Conference 2023 and MLSS^S 2023

Before, I co-organized ML in PL Conference 2019, ML in PL Virtual Event 2020, and ML in PL Conference 2021 serving as Call for Contributions (Talks and Posters) coordinator.

Reviewing scientific papers

I try to review at least a dozen of papers each year. So far I have served as a reviewer for:


The one that learns the most from a course is a teacher.
Wojciech Jaśkowski (my Eng. thesis supervisor)

As part of my PhD/being an assistant, I teach/taught a few courses at Poznan University of Technology (PUT) related to the topics of Machine Learning, Artificial Intelligence, Data Science and Big Data:

  • Elements of Convex Optimization 2024 (classes)
  • Systems That Learn 2024 (classes)
  • Decision Support Systems 2024 (classes, external)
  • Advanced Methods of Computational Intelligence 2021, 2022 and 2023 (lectures + classes)
    based on Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction and selected chapters from Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach.
  • Methods of Artificial and Computational Intelligence 2021 (classes)
    based on selected chapters from Stuart Russell and Peter Norvig, Artificial Intelligence A Modern Approach.
  • Big Data Processing 2019/2020 and Processing of Massive Datasets 2018/2019 (classes)
    based on selected chapters from Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets.
  • Information Theory and Lossless Compression Methods 2018 (classes)
    based on the first chapters from David J.C. MacKay, Information Theory, Inference, and Learning Algorithms.
    Note:  The course was awarded as the best new course in 2018 at the Institute of Computer Science.

All the course materials can be found on PUT's internal e-learning platform (e-Kursy). If you are one of my students, feel free to contact me via e-mail provided on this website.