arXiv Machine Learning
Bellman Error Minimization Via Linear Programming Normalization
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arXiv Machine Learning
Jul 7
Deep Learning for Dynamic Programming with Recursive Utility
arXiv:2607. 04278v1 Announce Type: cross Abstract: We propose the first deep learning algorithm, the Certainty Equivalent Learning (CEL) algorithm, for solving high-dimensional discrete-time dynamic programming problems with recursive utility.
arXiv Machine Learning
Jun 9
Bellman Residual Minimization for Control: Geometry, Stationarity, and Convergence
arXiv:2601. 18840v4 Announce Type: replace Abstract: Markov decision problems are most commonly solved via dynamic programming.
arXiv Machine Learning
Sep 24
Learning to Approximate Uniform Facility Location via Graph Neural Networks
The paper introduces a fully differentiable message‑passing neural network (MPNN) designed to approximate the Uniform Facility Location (UniFL) problem. Unlike many learning‑based approaches that require supervision or reinforcement learning, this model incorporates principles from classical approximation algorithms, providing provable approximation guarantees. Empirical results show that it outperforms standard approximation algorithms and reduces the performance gap to integer linear programming solutions.
arXiv Machine Learning
Aug 4
Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection
arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.
arXiv AI
Jun 10
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
arXiv:2606. 10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints.
arXiv Machine Learning
Aug 4
Optimized Piecewise Affine Abstractions of Neural Networks with Learnable Activation Functions
arXiv:2602. 06737v2 Announce Type: replace Abstract: We present a generalized framework for the range verification of neural networks featuring non-linear activation functions.