Tag
This paper introduces CG4AI, a framework that uses column generation to train AI models while enforcing hard linear constraints on their outputs, demonstrating applications in digit classification and network routing with improved feasibility and accuracy.
This article discusses the parallels between Soviet economic planning and modern data science practices, focusing on issues like resource allocation and simplifying assumptions based on historical books.
This paper presents a decision-centric causal optimization framework for large-scale targeting and recommendation, combining a causal Transformer, Bayesian bandit layer, and dual-based linear programming. It reports a statistically significant +7.20% lift in LinkedIn Feed marketing traffic via online A/B testing.
This paper proposes a fairness-aware pricing framework for retail food products using Autoregressive Distributed Lag (ARDL) models for sales forecasting and optimizes prices with Linear Programming and Simulated Annealing under CPI-based bounds to prevent consumer exploitation.
This paper introduces a totally unimodular linear programming reformulation for alignment-based conformance checking, which complements A* search by providing speedups for long traces with deviations. The approach achieves 38.6% average runtime savings with 96% selection accuracy.
This is a digitized 1965 IBM document detailing the application of linear programming to ice cream blending optimization.