Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
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A betweenness centrality for stochastic networks is defined via an absorbing Markov chain on sequences of reported central nodes, with importance given by pre-absorption occupancy and estimated by Monte Carlo.
DeconDTN-Toolkit simulates provenance shifts to expose ERM vulnerabilities and provides tools plus a robust OOD indicator for mitigating confounding by data provenance.
citing papers explorer
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Minimizing Upper Confidence Bounds: A Data-Driven Framework for Stochastic Programming
Proposes APUB optimization framework for stochastic programming, proves asymptotic correctness and consistency of the new bound, and develops bootstrap and L-shaped solvers for two-stage linear problems with empirical tests on a product mix example.
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Betweenness Central Nodes Under Uncertainty: An Absorbing Markov Chain Approach
A betweenness centrality for stochastic networks is defined via an absorbing Markov chain on sequences of reported central nodes, with importance given by pre-absorption occupancy and estimated by Monte Carlo.
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DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift
DeconDTN-Toolkit simulates provenance shifts to expose ERM vulnerabilities and provides tools plus a robust OOD indicator for mitigating confounding by data provenance.