4 papers
Sliced Wasserstein Sketches with Error Certificates
Elaheh Akbari, James Liu, Keaton Hamm +1
Compact representations that support geometrically meaningful comparisons are valuable for learning from probability distributions. We study optimal, free-weight -atom quantizat…
Recovering Wasserstein Distance Matrices from Few Measurements
Muhammad Rana, Abiy Tasissa, HanQin Cai +2
This paper proposes two algorithms for estimating square Wasserstein distance matrices from a small number of entries. These matrices are used to compute manifold learning embeddin…
Neighbor Embeddings Using Unbalanced Optimal Transport Metrics
Muhammad Rana, Keaton Hamm
This paper proposes the use of the Hellinger--Kantorovich metric from unbalanced optimal transport (UOT) in a dimensionality reduction and learning (supervised and unsupervised) pi…
LOTFormer: Doubly-Stochastic Linear Attention via Low-Rank Optimal Transport
Ashkan Shahbazi, Chayne Thrash, Yikun Bai +3
Transformers have proven highly effective across modalities, but standard softmax attention scales quadratically with sequence length, limiting long context modeling. Linear attent…