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Found 7 papers, 5 papers with code
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent factors of interest depend sparsely on past latent factors and/or observed auxiliary variables.
On the Convergence of Continuous Constrained Optimization for Structure Learning
Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a continuous optimization problem by leveraging an algebraic characterization of acyclicity.
Differentiable Causal Discovery from Interventional Data
This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data.
Gradient-Based Neural DAG Learning
We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data.
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
We show that causal structures can be parameterized via continuous variables and learned end-to-end.
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm.
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
We aim to predict at a high speed the expected TDOS associated with the second stage problem, conditionally on the first stage variables.
