CATune is a Bayesian optimization framework that incorporates deterministic inter‑knob ordering constraints from DBMS documentation into the search space. By performing optimization within a constraint‑consistent subspace and using a topology‑aware sampling strategy, it avoids costly post‑hoc constraint handling. Experiments on PostgreSQL and MySQL with TPC‑C and TPC‑H workloads show that CATune achieves up to 12.5× faster convergence to the baseline optimum and improves throughput by up to 63.37%.
arXiv:2607. 23448v1 Announce Type: cross Abstract: Expensive constrained optimization problems in real-world industry design often involve constraint thresholds that are difficult to determine in advance.
arXiv:2603. 24567v2 Announce Type: replace-cross Abstract: Constrained optimization in high-dimensional black-box settings is difficult due to expensive evaluations, the lack of gradient information, and complex feasibility regions.
arXiv:2607. 13735v1 Announce Type: new Abstract: The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering.
The paper introduces ORACLE, a method for constrained learning that evaluates constraint compatibility after the optimizer has taken a step, rather than before. ORACLE performs a joint endpoint linearization of diverse constraint families, constructs an alignment in the optimizer’s geometry, bounds its authority, and commits the adjustment only after validation. Experiments on eight PDE benchmarks with four different optimizers show that ORACLE matches or improves performance in 94% of configurations, outperforming other constraint-handling approaches.
The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering. Despite a rich literature spanning quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference-time optimization, practitioners are often left navigating these techniques through heuristics rather than principled methodology.
arXiv:2608. 00316v1 Announce Type: new Abstract: Bayesian optimization (BO) has become the standard tool for sample-efficient optimization and owes its efficiency to uncertainty-aware search driven by generic statistical priors.
The paper introduces Constraint‑Guided Enterprise Data Mapping (CGM), a neuro‑symbolic approach that uses schema‑grounded admissibility constraints to steer large language models (LLMs) in aligning enterprise data. CGM operates in three stages: defining constraints with metadata, generating candidates under relaxed constraints to ensure feasibility, and ranking them with a bounded LLM. Experiments show that hard constraints dramatically reduce candidate space and improve F1 scores, enabling small models to match or surpass large LLMs at a fraction of the cost while reducing expert effort.
arXiv:2607. 23480v1 Announce Type: new Abstract: Variational autoencoders (VAEs) transform high-dimensional, often noisy data into a compact latent representation, making downstream optimization more tractable.
arXiv:2606. 04816v1 Announce Type: new Abstract: Large language models (LLMs) increasingly translate natural-language optimization problems into executable solver code.
The paper introduces DECO, a non‑parametric hybrid framework that uses directional evidence from observational data to prune the search space of parent‑set configurations before performing exact DAG optimization. By eliminating empirically unsupported parent configurations, DECO achieves an exponential reduction in the admissible configuration space while maintaining flexibility for plausible edge orientations. Experiments on benchmark Bayesian networks and synthetic DAGs show substantial search‑space reduction and competitive structure‑recovery performance, with favorable structural Hamming distances across many settings.
arXiv:2606. 07841v1 Announce Type: cross Abstract: Black-box variational inference (BBVI) is a methodology for posterior approximation that relies on stochastic optimization.
arXiv:2608. 15109v1 Announce Type: new Abstract: Generating structurally valid synthetic tabular data remains difficult: outputs with high statistical fidelity and downstream utility can still violate semantically meaningful domain constraints.