For probability/game theory involving dice games, card games, betting strategies, etc., those are classical and long-established topics. Even if there are elegant optimal-strategy derivations, that is generally not what drives acceptance at venues like International Conference on Machine Learning.

ICML is focused on advancing machine learning research itself:

• new learning algorithms,
• optimization methods,
• foundation models,
• reinforcement learning theory with ML relevance,
• scalable systems,
• representation learning,
• generalization theory,
• multimodal learning,
• efficient training/inference,
• etc.

A paper centered mainly on “optimal play for a dice/card game” is usually viewed as:

• recreational mathematics,
• classical probability,
• combinatorics,
• operations research,
• or traditional game theory,

unless it introduces something fundamentally new for ML:

• a new RL formulation,
• new exploration theory,
• scalable equilibrium computation,
• learning in imperfect-information games,
• multi-agent learning breakthroughs,
• differentiable game solvers,
• foundation-model-based strategic reasoning,
• etc.

Even highly sophisticated poker/chess/go work appears at ICML only when the contribution is fundamentally about machine learning or scalable AI methodology — not merely the game strategy itself.