Improving Feasibility via Fast Autoencoder-Based Projections
Enforcing complex (e.g., nonconvex) operational constraints is a critical challenge in real-world learning and control systems. However, existing methods struggle to efficiently enforce general classes of constraints. To address this, we propose a novel data-driven amortized approach that uses a trained autoencoder as an approximate projector to provide fast corrections to infeasible predictions. Specifically, we train an autoencoder using an adversarial objective to learn a structured, convex latent representation of the feasible set. This enables rapid correction of neural network outputs by projecting their associated latent representations onto a simple convex shape before decoding in...
| Authors | Maria Chzhen, Priya L. Donti |
|---|---|
| Primary category | cs.LG |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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