Constraint-Aware Flow Matching integrates constraint projections into the flow matching training objective to align model dynamics with constrained sampling and reduce distributional shift.
predict, then optimize
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
Prediction models for linear program right-hand sides are trained via decision error minimization and historical primal-dual solutions to ensure the true optimal solution remains feasible and optimal under the predicted constraints.
BCCB unifies learning of heterogeneous ad responses, exploration of uncertain users, and budget pacing into a single online process that works effectively from the first user on the Criteo Uplift dataset.
A physics-informed neural representation is learned from safe data to support distributional hypothesis testing for dynamical instability in stochastic DAE systems without repeated simulations.
citing papers explorer
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Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling
Constraint-Aware Flow Matching integrates constraint projections into the flow matching training objective to align model dynamics with constrained sampling and reduce distributional shift.
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Decision-Aware Predictions for Right-Hand Side Parameters in Linear Programs
Prediction models for linear program right-hand sides are trained via decision error minimization and historical primal-dual solutions to ensure the true optimal solution remains feasible and optimal under the predicted constraints.
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Budget-Constrained Causal Bandits: Bridging Uplift Modeling and Sequential Decision-Making
BCCB unifies learning of heterogeneous ad responses, exploration of uncertain users, and budget pacing into a single online process that works effectively from the first user on the Criteo Uplift dataset.
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Learning to Test: Physics-Informed Representation for Dynamical Instability Detection
A physics-informed neural representation is learned from safe data to support distributional hypothesis testing for dynamical instability in stochastic DAE systems without repeated simulations.