TROT: Tsallis-Regularized Optimal Transport Unifies Wasserstein and KL Divergences
FrnkNlsn · x · 2026-09-16
A 2016 paper by Muzellec, Nock, Patrini, and Nielsen proposes Tsallis-regularized optimal transport (TROT): regularizing mass transport with a Tsallis entropy term instead of Shannon entropy, it unifies the Monge-Kantorovitch and Sinkhorn-Cuturi approaches and interpolates a family of divergences from Wasserstein to KL, Pearson, Neyman, and Hellinger, with efficient Sinkhorn-like algorithms and convergence proofs.
It also applies optimal transport to ecological inference — reconstructing joint distributions from marginals — demonstrated on 2012 US presidential election data.
More from Research
- CARLA veteran Ros shares synthetic data workflows to accelerate AV development — abursuc · 2026-09-16
- Grade AI like coworkers: open-source FrontierAgent framework ships with CLI TUI and fully local execution — aakashgupta · 2026-09-16
- CoLLAs 2026 talk: memorization may be unavoidable — curation, unlearning, pruning as strategies — gkdziugaite · 2026-09-16
- ETH Zürich robotic hand walks on its own fingers, no legs or wheels needed — lukas_m_ziegler · 2026-09-16
- Genome Biology opens collection on tumor microenvironment, welcomes AI and multi-omic methods — arjunrajlab · 2026-09-16
- Google Engineers' WikiSkill Turns Agent Execution History Into Validated, Reusable Skills — blaizedsouza · 2026-09-16