MA-BC partitions conflicting expert trajectories while pooling compatible data to recover Pareto-optimal policies in multi-objective imitation with minimax optimal rates.
SAMoR encodes cross-topology articulated motion into shared part tokens via graph-transformer encoding and attention supervision, achieving 5.8× lower reconstruction error than adapted baselines across arbitrary skeletons.
Gaussian processes are recast as linear diffusion models to enable conditioning on arbitrary likelihoods, including language and physics, via ODE sampling without bespoke derivations.
Closed-form last-layer optimization treats final weights as backbone-dependent functions, yielding convergence guarantees and outperforming SGD and Adam on regression tasks.