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.
Mathematical Programming , volume =
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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.
WARDEN is a new adversarial training framework for large language models that minimizes worst-case loss over an f-divergence ambiguity set, reducing attack success rates while keeping utility comparable to recent baselines.
A method that runs static traffic assignment on hypothetical demand for each road network and compares the resulting traffic-weighted geographic distributions via 2D Wasserstein distance.
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.
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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Information Theoretic Adversarial Training of Large Language Models
WARDEN is a new adversarial training framework for large language models that minimizes worst-case loss over an f-divergence ambiguity set, reducing attack success rates while keeping utility comparable to recent baselines.
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Distance between Road Networks: A Macroscopic Method for Road Network Datasets Comparison Using Traffic-weighted Geographic Distribution
A method that runs static traffic assignment on hypothetical demand for each road network and compares the resulting traffic-weighted geographic distributions via 2D Wasserstein distance.
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Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
A tutorial framing deep learning as a complement to optimization for sequential decision-making under uncertainty, with applications in supply chains, healthcare, and energy.