Smoothness assumptions on graphical model kernels produce Wasserstein estimation rates determined by local graph structure rather than ambient dimension.
arXiv preprint arXiv:2109.04173 , year=
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Causal Process Models reframe dynamic causal graph discovery as multi-agent reinforcement learning to build sparse time-varying graphs only at active interactions, outperforming dense baselines on physical prediction.
DeepSWIP supplies exact single-world counterfactuals for DeepProbLog programs by neural materialization followed by SWIP transformation and quotient-WMC.
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
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Fast Wasserstein rates for estimating probability distributions of probabilistic graphical models
Smoothness assumptions on graphical model kernels produce Wasserstein estimation rates determined by local graph structure rather than ambient dimension.
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Causal Process Models reframe dynamic causal graph discovery as multi-agent reinforcement learning to build sparse time-varying graphs only at active interactions, outperforming dense baselines on physical prediction.
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DeepSWIP supplies exact single-world counterfactuals for DeepProbLog programs by neural materialization followed by SWIP transformation and quotient-WMC.
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