A three-tower embedding model fine-tuned from Fashion CLIP combined with a latent-class deep demand system captures heterogeneous consumer aesthetics, price sensitivities, and substitution patterns from large-scale retail transaction data.
Representing random utility choice models with neural networks
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InvEvolve evolves inventory policies using LLMs with RL and provides statistical safety guarantees, outperforming classical and DL methods on synthetic and real data.
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InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves inventory policies using LLMs with RL and provides statistical safety guarantees, outperforming classical and DL methods on synthetic and real data.