CTEM unifies density estimation via a bounded energy-difference transform that yields a sample-only objective with constant target 1, recovering log p without partition functions or unbounded ratio regression.
Advances in Neural Information Processing Systems , year =
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A McKean-Vlasov FBSDE generative model learns stochastic path laws that match observed terminal and time-marginal distributions via soft energy constraints rather than hard interpolation.
Return-conditional diffusion models for policies outperform offline RL on benchmarks by circumventing dynamic programming and enable constraint or skill composition.
citing papers explorer
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Constant-Target Energy Matching: A Unified Framework for Continuous and Discrete Density Estimation
CTEM unifies density estimation via a bounded energy-difference transform that yields a sample-only objective with constant target 1, recovering log p without partition functions or unbounded ratio regression.
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Learning Generative Dynamics with Soft Law Constraints: A McKean-Vlasov FBSDE Approach
A McKean-Vlasov FBSDE generative model learns stochastic path laws that match observed terminal and time-marginal distributions via soft energy constraints rather than hard interpolation.
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Is Conditional Generative Modeling all you need for Decision-Making?
Return-conditional diffusion models for policies outperform offline RL on benchmarks by circumventing dynamic programming and enable constraint or skill composition.