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cs.LG2025
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,…
cs.LG2025
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…
cs.LG2024
tsGT: Stochastic Time Series Modeling With Transformer
Åukasz KuciÅski, 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…