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Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation

12 May 2023arXiv:2305.07247archive 2025-07-28

Yu Chen, Wei Deng, Shikai Fang, Fengpei Li, Nicole Tianjiao Yang, Yikai Zhang, Kashif Rasul, Shandian Zhe, Anderson Schneider, Yuriy Nevmyvaka

The Schr\"odinger bridge problem (SBP) is gaining increasing attention in generative modeling and showing promising potential even in comparison with the score-based generative models (SGMs). SBP can be interpreted as an entropy-regularized optimal transport problem, which conducts projections onto every other marginal alternatingly. However, in practice, only approximated projections are accessible and their convergence is not well understood. To fill this gap, we present a first convergence analysis of the Schr\"odinger bridge algorithm based on approximated projections. As for its practical applications, we apply SBP to probabilistic time series imputation by generating missing values conditioned on observed data. We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.

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Syntology Ran 5 of 7 code samples harvested from 1 repository linked to this paper; 2 have no recorded run. Of those that ran: 1 ran · honoured contract; 3 ran · our draft was wrong; 1 ran · fixture could not drive it.

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morganstanley/MSML officialpytorch report

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7 samples harvested; 5 ran; 1 honoured the contract we drafted; 2 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
3ran · our draft was wrong
1ran · fixture could not drive it
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get_timestep_embedding morganstanley/msml/papers/Conditional_Schrodinger_Bridge_Imputation/models/DGLSB/dglsb.py official repository ran · fixture could not drive it fingerprinted Apache-2.0 (permissive) · cb49209c125de1b4 · report
nonlinearity morganstanley/msml/papers/Conditional_Schrodinger_Bridge_Imputation/models/DGLSB/dglsb.py official repository ran · our draft was wrong fingerprinted Apache-2.0 (permissive) · 3137073275f8c21a · report
sample_e morganstanley/MSML/papers/Conditional_Schrodinger_Bridge_Imputation/loss.py official repository ran · our draft was wrong Apache-2.0 (permissive) · 6caf373f11b8fb5b · report
sample_gaussian_like morganstanley/MSML/papers/Conditional_Schrodinger_Bridge_Imputation/loss.py official repository ran · honoured contract fingerprinted Apache-2.0 (permissive) · 97f801e33d907e8a · report
compute_div_gz morganstanley/MSML/papers/Conditional_Schrodinger_Bridge_Imputation/loss.py official repository unverified Apache-2.0 (permissive) · f644acc4e4a21590 · report
compute_sb_nll_alternate_train morganstanley/MSML/papers/Conditional_Schrodinger_Bridge_Imputation/loss.py official repository unverified Apache-2.0 (permissive) · dbbdb9695d090609 · report
sample_rademacher_like identical code first harvested elsewhere ran · our draft was wrong fingerprinted licence of this copy not recorded · 7b0725913f7abbbb · report

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