paper-conference

We propose a novel algorithm for learning barycentres of distributions using neural networks. The algorithm is scalable and robust to outliers.

We propose a new energy-based method to compute entropic optimal transport barycenters with general cost functions.

We propose a novel theoretically-justified and lightweight solver for continuous unbalanced entropic optimal transport.

We propose a novel bi-level optimization algorithm for solving the continuous Wasserstein Barycenter problem based on Neural Optimal Transport.

We address propose a novel procedure to learn SB which exploits the optimal parameterization of the diffusion process and provably recovers the SB process (a) with a single bridge matching step and (b) with arbitrary transport plan as the input.

We propose a novel algorithm for offline reinforcement learning using optimal transport.

We propose a novel iterative procedure with fast inference to learn the Schrodinger Bridge using the adversarial bridge matching in discrete time.

We propose a novel energy-based method to compute entropic optimal transport with general cost functions.

We introduce a novel light solver for the Schrödinger Bridge problem.

We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals.