Distribution-Conditioned Transport

arXiv:2603.04736 · cs.LG · Submitted 2026-03-05 · Read on arXiv

cs.LG

Submitted: 2026-03-05

Updated: 2026-09-25

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Embeddings — 45× in this paper · explained in 2 episodes
• Flow Matching — 36× in this paper · explained in 8 episodes
• Generalization — 17× in this paper · explained in 13 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Wasserstein Distance — 9× in this paper · explained in 4 episodes
• Regularization — 8× in this paper · explained in 3 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Latent Space — 6× in this paper · explained in 7 episodes
• Adam — 5× in this paper · explained in 2 episodes
• Alignment — 5× in this paper · explained in 1 episode
• Generative Model — 5× in this paper · explained in 2 episodes
• Optimal Transport — 5× in this paper · explained in 5 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• 2-Wasserstein Distance — 3× in this paper · explained in 1 episode
• Abundance — 3× in this paper · explained in 1 episode
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Generative Modeling — 3× in this paper · explained in 3 episodes
• Meta Flow Matching — 3× in this paper
• Semi-supervised Learning — 3× in this paper · explained in 4 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Generative Distribution Embeddings — 2× in this paper
• Hilbert Space — 2× in this paper · explained in 2 episodes
• Iterative Refinement — 2× in this paper · explained in 3 episodes
• Normalizing Flow — 2× in this paper · explained in 3 episodes
• Normalizing Flows — 2× in this paper · explained in 4 episodes
• Rectified Flow — 2× in this paper · explained in 3 episodes
• Sparsity — 2× in this paper · explained in 4 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• ESM2 D ISCRETE F LOW M ATCHING — 1× in this paper
• Feature Selection — 1× in this paper · explained in 4 episodes
• Flow Matching Model — 1× in this paper · explained in 2 episodes
• G AUSSIAN M IXTURE M ODELS — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• Generative Distribution Embeddings: Lifting autoencoders to the space of distributions for multiscale representation learning
• Multimarginal generative modeling with stochastic interpolants
• Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers
• Improving and generalizing flow-based generative models with minibatch optimal transport
• Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy

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