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cs.LG2025
Denoising the Future: Top-p Distributions for Moving Through Time
Florian Andreas Marwitz, Ralf Möller, Magnus Bender +1
Inference in dynamic probabilistic models is a complex task involving expensive operations. In particular, for Hidden Markov Models, the whole state space has to be enumerated for…
cs.AI2025
Lifted Forward Planning in Relational Factored Markov Decision Processes with Concurrent Actions
Florian Andreas Marwitz, Tanya Braun, Ralf Möller +1
When allowing concurrent actions in Markov Decision Processes, whose state and action spaces grow exponentially in the number of objects, computing a policy becomes highly ineffici…
cs.AI2025
Approximate Lifted Model Construction
Malte Luttermann, Jan Speller, Marcel Gehrke +3
Probabilistic relational models such as parametric factor graphs enable efficient (lifted) inference by exploiting the indistinguishability of objects. In lifted inference, a repre…