Author name cluster

Błażej Osiński

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

NeurIPS Conference 2025 Conference Paper

PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis

Yan Wu
Esther Wershof
Sebastian Schmon
Marcel Nassar
Błażej Osiński
Ridvan Eksi
Zichao Yan
Rory Stark

We introduce a comprehensive framework for modeling single cell transcriptomic responses to perturbations, aimed at standardizing benchmarking in this rapidly evolving field. Our approach includes a modular and user-friendly model development and evaluation platform, a collection of diverse perturbational datasets, and a set of metrics designed to fairly compare models and dissect their performance. Through extensive evaluation of both published and baseline models across diverse datasets, we highlight the limitations of widely used models, such as mode collapse. We also demonstrate the importance of rank metrics which complement traditional model fit measures, such as RMSE, for validating model effectiveness. Notably, our results show that while no single model architecture clearly outperforms others, simpler architectures are generally competitive and scale well with larger datasets. Overall, this benchmarking exercise sets new standards for model evaluation, supports robust model development, and furthers the use of these models to simulate genetic and chemical screens for therapeutic discovery.

PDF Details

AAMAS Conference 2022 Conference Paper

Off-Policy Correction For Multi-Agent Reinforcement Learning

Michał Zawalski
Błażej Osiński
Henryk Michalewski
Piotr Miłoś

Multi-agent reinforcement learning (MARL) provides a framework for problems involving multiple interacting agents. Despite similarity to the single-agent case, multi-agent problems are often harder to train and analyze theoretically. In this work, we propose MA-Trace, a new on-policy actor-critic algorithm, which extends V-Trace to the MARL setting. The key advantage of our algorithm is its high scalability in a multi-worker setting. To this end, MA-Trace utilizes importance sampling as an off-policy correction method, which allows distributing the computations with negligible impact on the quality of training. Furthermore, our algorithm is theoretically grounded – we provide a fixed-point theorem that guarantees convergence. We evaluate the algorithm extensively on the Star- Craft Multi-Agent Challenge, a standard benchmark for multi-agent algorithms. MA-Trace achieves high performance on all its tasks and exceeds state-of-the-art results on some of them.

PDF