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Introduces Cluster-Weighted EDMD, a data-driven method that jointly learns a partition and per-cluster Koopman operators via expectation-maximization, improving prediction accuracy over standard EDMD on classical dynamical systems.
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.
This paper identifies a mismatch between training (winner-take-all loss) and inference in trajectory forecasting models, leading to uninformative mode probabilities. It proposes post-hoc treatments using posterior-weighted merging and a one-step EM update to improve mode ranking and forecasting accuracy without retraining.
TEMPO introduces a test-time training framework that alternates policy refinement with critic recalibration to prevent diversity collapse and sustain performance gains in large reasoning models, boosting AIME 2024 scores for Qwen3-14B from 42.3% to 65.8%.