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Bayes' Theorem allows a program to infer the probabilities of likely causes from the probabilities of their effects, when what it is given are the probabilities of effects, given the causes.
Yingxiang Yang, Bo Dai, Negar Kiyavash, Niao He
Neural Information Processing SystemsFeb-13-2026, 23:22:22 GMT
Neural Information Processing Systems http://nips.cc/
Kevin Bello, Jean Honorio
Neural Information Processing SystemsFeb-13-2026, 23:21:07 GMT
Shahana Ibrahim, Xiao Fu, Nikolaos Kargas, Kejun Huang
Neural Information Processing SystemsFeb-13-2026, 23:00:03 GMT
Lingge Li, Dustin Pluta, Babak Shahbaba, Norbert Fortin, Hernando Ombao, Pierre Baldi
Neural Information Processing SystemsFeb-13-2026, 22:20:21 GMT
Matthew D. Hoffman
Neural Information Processing SystemsFeb-13-2026, 21:40:35 GMT
Gintare Karolina Dziugaite, Daniel M. Roy
Neural Information Processing SystemsFeb-13-2026, 21:20:29 GMT
Neural Information Processing SystemsFeb-13-2026, 19:53:14 GMT
Marek Petrik, Reazul Hasan Russel
Neural Information Processing SystemsFeb-13-2026, 19:37:58 GMT
Neural Information Processing SystemsFeb-13-2026, 17:56:49 GMT
Neural Information Processing SystemsFeb-13-2026, 17:37:02 GMT
This simple intuition makes Bayesian methods appealing approaches for transfer in RL, and many previous works have been proposed in this direction.