Generative Robust Optimisation defines uncertainty sets via neural network decoders over latent spaces and evaluates them with a five-point framework, validated on planning problems using Wasserstein autoencoders.
An Exact Solution Approach for Portfolio Op- timization Problems Under Stochastic and Integer Constraints
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
representative citing papers
MIGP uses integer servings and goal deviations to produce feasible, practical meal plans that outperform post-hoc rounding of continuous solutions in 66% of cases while always succeeding.
The Keplerian TSP models time-dependent interplanetary rendezvous missions as a discrete optimization problem using time-unfolding and ILP solvers, with released benchmarks and heuristics.
EBBS augments the MIO best-subsets objective with an aggregated expert prior expressed as a log-odds penalty so that selected features align with domain consensus while reducing to ordinary best subsets when experts provide no input.
MMPO introduces Belief Entropy as a self-supervised signal to provide fine-grained supervision for memory policies in LLM agents, outperforming outcome-based RL on long-horizon tasks up to 1.75M tokens.
Establishes a comparison principle for two-population killed-particle HJB equations on decomposed state spaces of alive measures and cemetery masses, plus mean-field limit and particle convergence results.
MLMC and MLQMC with h- and p-refinement hierarchies achieve significant speedups over standard MC for UQ in cantilever beam problems, with MLQMC showing optimal cost scaling under certain conditions.
citing papers explorer
-
Generative Robust Optimisation
Generative Robust Optimisation defines uncertainty sets via neural network decoders over latent spaces and evaluates them with a five-point framework, validated on planning problems using Wasserstein autoencoders.
-
A Mathematical Optimization Approach for Expert-Informed Bayesian Best Subset Selection
EBBS augments the MIO best-subsets objective with an aggregated expert prior expressed as a log-odds penalty so that selected features align with domain consensus while reducing to ordinary best subsets when experts provide no input.