A constrained optimization framework for diffusion model unlearning via KL and likelihood constraints, with duality results and reported better retention-unlearning tradeoffs than weight-based baselines.
Christopher and Sven Koenig and Ferdinando Fioretto , title =
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Anonymous MAPF is cast as a Markovian multi-marginal optimal transport problem that reduces to a feasible totally unimodular LP, with Schrödinger bridges yielding an entropic regularization solved iteratively by Sinkhorn for near-optimal integral non-overlapping transports at reduced complexity.
A diffusion-based multi-robot planner trained on few agents generalizes to larger numbers during deployment using inter-agent attention and temporal convolution.
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Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
A constrained optimization framework for diffusion model unlearning via KL and likelihood constraints, with duality results and reported better retention-unlearning tradeoffs than weight-based baselines.
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Optimal and Scalable MAPF via Multi-Marginal Optimal Transport and Schr\"odinger Bridges
Anonymous MAPF is cast as a Markovian multi-marginal optimal transport problem that reduces to a feasible totally unimodular LP, with Schrödinger bridges yielding an entropic regularization solved iteratively by Sinkhorn for near-optimal integral non-overlapping transports at reduced complexity.
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Train-Small Deploy-Large: Leveraging Diffusion-Based Multi-Robot Planning
A diffusion-based multi-robot planner trained on few agents generalizes to larger numbers during deployment using inter-agent attention and temporal convolution.