1 citations · 1 across the 5 of their papers we have counts for
5 papers
Amortized Causal Discovery with Prior-Fitted Networks
Mateusz Sypniewski, Mateusz Olko, Mateusz Gajewski +1
In recent years, differentiable penalized likelihood methods have gained popularity, optimizing the causal structure by maximizing its likelihood with respect to the data. However,…
Since Faithfulness Fails: The Performance Limits of Neural Causal Discovery
Mateusz Olko, Mateusz Gajewski, Joanna Wojciechowska +3
Neural causal discovery methods have recently improved in terms of scalability and computational efficiency. However, our systematic evaluation highlights significant room for impr…
tsGT: Stochastic Time Series Modeling With Transformer
Łukasz Kuciński, Witold Drzewakowski, Mateusz Olko +5
Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data. Recently, there has been a surge of deterministic tr…
Trust Your : Gradient-based Intervention Targeting for Causal Discovery
Mateusz Olko, Michał Zając, Aleksandra Nowak +5
Inferring causal structure from data is a challenging task of fundamental importance in science. Observational data are often insufficient to identify a system's causal structure u…
Continuous Control With Ensemble Deep Deterministic Policy Gradients
Piotr Januszewski, Mateusz Olko, Michał Królikowski +4
The growth of deep reinforcement learning (RL) has brought multiple exciting tools and methods to the field. This rapid expansion makes it important to understand the interplay bet…