Derives the cold Sinkhorn limiting dynamics as tau approaches zero, proving finite-time convergence to unregularized OT and improved O(tau^{-1}) iteration complexity for dual suboptimality.
Optimal transport tools (OTT): A JAX toolbox for all things Wasserstein
11 Pith papers cite this work. Polarity classification is still indexing.
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Proves sharp O(1/k) rate for Sinkhorn via local bipartite graph analysis of positive-mass edges, bootstrapped from prior almost-sharp global bound.
First DP procedure for smooth OT map estimation achieving near-minimax optimality in d≥2 and minimax in d=1, with matching lower bounds.
HyCNNs are a new architecture that learns convex functions with exponentially fewer parameters than ICNNs and outperforms them in convex regression and high-dimensional optimal transport on synthetic and single-cell RNA data.
A new constrained gradient flow on the space of transport maps converges to the OT map and enables more stable and accurate training of convexity-constrained neural networks for learning Monge maps.
FlashSinkhorn delivers up to 32x forward and 161x end-to-end speedups for entropic OT on A100 GPUs via IO-aware Triton kernels that fuse log-domain updates and streaming transport application.
Develops the first provably convergent stochastic fixed-point algorithm for free-support 2-Wasserstein barycenters of continuous measures under Caffarelli regularity, using a modified entropic OT map estimator.
OMT reformulates optimal transport for mixture models as a strictly biconvex optimization with a unique global minimizer and stability guarantees, decoupling complexity from sample size.
Under smooth unit costs and models, empirical subdifferentials of parameterized transport objectives converge graphically almost surely to the population subdifferential, so subgradient methods approach population critical points.
GenSBI delivers JAX-native implementations of generative SBI methods with transformer backbones and reports near-ideal calibration scores on standard benchmarks.
cuRegOT is a new GPU solver for entropic OT that delivers speedups over prior GPU methods via amortized analysis, asynchronous iterates, and fused kernels, backed by convergence guarantees.
citing papers explorer
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Effective dynamics of the Sinkhorn algorithm in the regime of low entropy regularization
Derives the cold Sinkhorn limiting dynamics as tau approaches zero, proving finite-time convergence to unregularized OT and improved O(tau^{-1}) iteration complexity for dual suboptimality.
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Sharp $O(1/k)$ convergence rate for the Sinkhorn algorithm via a local analysis
Proves sharp O(1/k) rate for Sinkhorn via local bipartite graph analysis of positive-mass edges, bootstrapped from prior almost-sharp global bound.
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Minimax Private Estimation of Smooth Optimal-Transport Maps
First DP procedure for smooth OT map estimation achieving near-minimax optimality in d≥2 and minimax in d=1, with matching lower bounds.
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Hyper Input Convex Neural Networks for Shape Constrained Learning and Optimal Transport
HyCNNs are a new architecture that learns convex functions with exponentially fewer parameters than ICNNs and outperforms them in convex regression and high-dimensional optimal transport on synthetic and single-cell RNA data.
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Learning Monge maps with constrained drifting models
A new constrained gradient flow on the space of transport maps converges to the OT map and enables more stable and accurate training of convexity-constrained neural networks for learning Monge maps.
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FlashSinkhorn: IO-Aware Entropic Optimal Transport on GPU
FlashSinkhorn delivers up to 32x forward and 161x end-to-end speedups for entropic OT on A100 GPUs via IO-aware Triton kernels that fuse log-domain updates and streaming transport application.
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Provably convergent stochastic fixed-point algorithm for free-support Wasserstein barycenter of continuous non-parametric measures
Develops the first provably convergent stochastic fixed-point algorithm for free-support 2-Wasserstein barycenters of continuous measures under Caffarelli regularity, using a modified entropic OT map estimator.
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A Biconvex Formulation for Stable Transport of Mixture Models with a Unique Solution
OMT reformulates optimal transport for mixture models as a strictly biconvex optimization with a unique global minimizer and stability guarantees, decoupling complexity from sample size.
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Convergence of empirical subgradients for optimal transport-based objectives
Under smooth unit costs and models, empirical subdifferentials of parameterized transport objectives converge graphically almost surely to the population subdifferential, so subgradient methods approach population critical points.
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GenSBI: Generative Methods for Simulation-Based Inference in JAX
GenSBI delivers JAX-native implementations of generative SBI methods with transformer backbones and reports near-ideal calibration scores on standard benchmarks.
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cuRegOT: A GPU-Accelerated Solver for Entropic-Regularized Optimal Transport
cuRegOT is a new GPU solver for entropic OT that delivers speedups over prior GPU methods via amortized analysis, asynchronous iterates, and fused kernels, backed by convergence guarantees.