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Tag: math.OC
arXiv:2604.04987v1 Announce Type: new Abstract: Speculative sampling (SpS) has been successful in accelerating the decoding throughput of auto-regressive large language models by leveraging smaller draft …
arXiv:2604.05230v1 Announce Type: new Abstract: Efficient and robust optimization is essential for neural networks, enabling scientific machine learning models to converge rapidly to very high …
arXiv:2604.03489v1 Announce Type: new Abstract: Enforcing complex (e.g., nonconvex) operational constraints is a critical challenge in real-world learning and control systems. However, existing methods struggle …
arXiv:2604.03234v1 Announce Type: new Abstract: The Minimum Set Cover Problem (MSCP) is a classical NP-hard combinatorial optimization problem with numerous applications in science and engineering. …
arXiv:2604.02666v1 Announce Type: new Abstract: Optimization is as much about modeling the right problem as solving it. Identifying the right objectives, constraints, and trade-offs demands …
arXiv:2604.02558v1 Announce Type: new Abstract: We address nonconvex learning problems over undirected networks. In particular, we focus on the challenge of designing an algorithm that …
arXiv:2604.02644v1 Announce Type: new Abstract: We present Conditional Wasserstein Autoencoders (CWAEs), a framework for conditional simulation that exploits low-dimensional structure in both the conditioned and …
arXiv:2604.01279v1 Announce Type: new Abstract: We introduce Sven (Singular Value dEsceNt), a new optimization algorithm for neural networks that exploits the natural decomposition of loss …
arXiv:2604.01308v1 Announce Type: new Abstract: Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to …
arXiv:2604.01378v1 Announce Type: new Abstract: Offline reinforcement learning (RL) has received increasing attention for learning policies from previously collected data without interaction with the real …
arXiv:2603.23658v1 Announce Type: new Abstract: Gradient boosting, a method of building additive ensembles from weak learners, has established itself as a practical and theoretically-motivated approach …
arXiv:2603.23783v1 Announce Type: new Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable …