The independent chip model (ICM) forms a cornerstone of all modern poker tournament strategy. However, despite its prominence, the ICM's performance in the real world has not been sufficiently scrutinized, especially at a large scale. In this paper, we introduce our new dataset of poker tournaments, consisting of results of over ten thousand events. Then, using this dataset, we perform two experiments as part of a large-scale empirical validation of the ICM. First, we verify that the ICM performs more accurately than a baseline we propose. Second, we obtain empirical evidence of the ICM underestimating the performances of players with larger stacks while overestimating those who are short-stacked. Our contributions may be useful to future researchers developing new algorithms for estimating a player's value in poker tournaments.

That idea is not without its detractors. Among other issues, many feel AI is not capable of the creative thought needed in research, makes too many mistakes and hallucinations, and may limit opportunities for young researchers. Nevertheless, a number of scientists and policymakers are very keen on the promise of AI scientists. The US government's AI Action Plan describes the need to "invest in automated cloud-enabled labs for a range of scientific fields." Some researchers think AI scientists could unlock scientific discoveries that humans could never find alone.

They make counterfactual predictions by using observed actions to learn the underlying utility function (a.k.a.

Classical bilevel optimization is referred to as the case where there is no consensus constraint but with only two levels of the minimization subproblems, i.e.,

Equipped with this algorithm, we demonstrate reincarnating RL's gains

The prior and recognition model encodes two types of latent codes for each agent: an inter-agent latent code to represent social relations and an intra-agent latent code to represent agent intentions.

The ability to reuse previous policies is an important aspect of human intelligence.