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cs.LG2026
Dimensional Peeking for Low-Variance Gradients in Zeroth-Order Discrete Optimization via Simulation
Philipp Andelfinger, Wentong Cai
Gradient-based optimization methods are commonly used to identify local optima in high-dimensional spaces. When derivatives cannot be evaluated directly, stochastic estimators can…
cs.LG2025
Learning to Optimize Capacity Planning in Semiconductor Manufacturing
Philipp Andelfinger, Jieyi Bi, Qiuyu Zhu +7
In manufacturing, capacity planning is the process of allocating production resources in accordance with variable demand. The current industry practice in semiconductor manufacturi…
cs.LG2021
Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization
Philipp Andelfinger
Simulation-based optimization using agent-based models is typically carried out under the assumption that the gradient describing the sensitivity of the simulation output to the in…