paper-conference

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.

We propose a novel approach which learns straight trajectories in flow matching without iterative rectification or heuristic minibatch optimal transport approximations.

We propose a novel theoretically-justified benchmark for continuous Entropic Optimal Transport (EOT) and Schrodinger Bridge (SB) solvers.

We propose a novel theoretically-justified benchmark for continuous Entropic Optimal Transport (EOT) and Schrodinger Bridge (SB) solvers.

We propose a novel neural algorithm for the fundamental problem of computing the entropic optimal transport (EOT) plan between probability distributions which are accessible by samples.

We show that Neural Optimal Transport (NOT) algorithm with the weak quadratic cost might learn fake plans which are not optimal. We fix this by introducing kernel weak quadratic costs which provide improved theoretical guarantees.