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This paper proposes a framework for Markov chain choice models with panel data, including estimation via novel EM algorithms that leverage partial-ordering preference information, personalized choice prediction, and assortment optimization. Experimental results on synthetic data and the sushi dataset show improvements over traditional methods.
SAGA introduces a decoder-only transformer for multi-horizon probabilistic forecasting of lifetime earnings, paired with adaptive conformal prediction to provide reliable prediction intervals. Trained on a large Swedish register dataset, it achieves significant improvements over traditional parametric and baseline models.