3 papers
cs.LG2026
Differentiating Bisimulation Metrics: A Framework for Parametric Markov Chain Fitting via Bicausal Optimal Transport
Sergio Calo, Amy Zhang, Javier Segovia-Aguas +1
Many problems in sequential decision-making, such as imitation learning from observations, state-space compression, world-model learning, and sim-to-real transfer, can be reduced t…
cs.LG2025
Distances for Markov chains from sample streams
Sergio Calo, Anders Jonsson, Gergely Neu +2
Bisimulation metrics are powerful tools for measuring similarities between stochastic processes, and specifically Markov chains. Recent advances have uncovered that bisimulation me…
cs.LG2024
Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently
Sergio Calo, Anders Jonsson, Gergely Neu +2
We propose a new framework for formulating optimal transport distances between Markov chains. Previously known formulations studied couplings between the entire joint distribution…