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This work proposes a framework for computing CXs for inputs with missing values using flow-based generative models and Mixed-Integer Linear Programming (MILP) and demonstrates that this method is able to generate CXs with higher validity, and lower cost than state-of-the-art methods relying on imputation.
The Temporal-Logic-based Causal Diagram (TL-CD) in RL is proposed, which captures the temporal causal relationships between different properties of the environment and the benefits of using TL-CDs are demonstrated, particularly the faster convergence of the algorithm to an optimal policy due to reduced exploration of t...
Temporal-Causal Inference for Reinforcement Learning (TCIRL), a framework that jointly learns a control policy and infers the hidden temporal cause of the phase transition and recovers the correct cause DFA and matches the full-information baseline in both domains.
eNNcode is introduced, a user-friendly PyPI library that converts any piecewise-linear neural network in Open Neural Network Exchange (ONNX) format into a MILO instance, and supports arbitrary constraints on input and output nodes, as well as user-defined optimization objectives.
NoBOOM is presented, the first collection of datasets for anomaly detection in real-world chemical process data, including labeled data from a running process at BASF SE, one of the world’s leading chemical companies.
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