
A Uniformly Consistent Estimator of nonGaussian Causal Effects Under the kTriangleFaithfulness Assumption
Kalisch and Bühlmann (2007) showed that for linear Gaussian models, unde...
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(Theta, triangle)free and (even hole, K_4)free graphs. Part 2 : bounds on treewidth
A theta is a graph made of three internally vertexdisjoint chordless p...
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Monochromatic Triangles, Triangle Listing and APSP
One of the main hypotheses in finegrained complexity is that AllPairs ...
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A simple combinatorial algorithm for restricted 2matchings in subcubic graphs – via halfedges
We consider three variants of the problem of finding a maximum weight re...
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Reconstructing dynamical networks via feature ranking
Empirical data on real complex systems are becoming increasingly availab...
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Robustness to fundamental uncertainty in AGI alignment
The AGI alignment problem has a bimodal distribution of outcomes with mo...
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SingleStrip Triangulation of Manifolds with Arbitrary Topology
Triangle strips have been widely used for efficient rendering. It is NP...
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An algorithm for reconstruction of trianglefree linear dynamic networks with verification of correctness
Reconstructing a network of dynamic systems from observational data is an active area of research. Many approaches guarantee a consistent reconstruction under the relatively strong assumption that the network dynamics is governed by strictly causal transfer functions. However, in many practical scenarios, strictly causal models are not adequate to describe the system and it is necessary to consider models with dynamics that include direct feedthrough terms. In presence of direct feedthroughs, guaranteeing a consistent reconstruction is a more challenging task. Indeed, under no additional assumptions on the network, we prove that, even in the limit of infinite data, any reconstruction method is susceptible to inferring edges that do not exist in the true network (false positives) or not detecting edges that are present in the network (false negative). However, for a class of trianglefree networks introduced in this article, some consistency guarantees can be provided. We present a method that either exactly recovers the topology of a trianglefree network certifying its correctness or outputs a graph that is sparser than the topology of the actual network, specifying that such a graph has no false positives, but there are false negatives.
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