analiza aiqbench
OneFlowSBI proposes a single masked flow-matching network trained on the joint distribution of parameters and observations, so that any conditional or marginal can be sampled by changing an input mask rather than training a new model. Its main technical claim is that treating the mask as an input, instead of tokenizing each variable as transformer generalists do, avoids quadratic attention cost as dimensionality grows. The reported gains are mostly on specific benchmarks (SBIBM tasks, image denoising, shallow water inference), so readers should check how well they transfer to their own problems.
OneFlowSBI: One Model, Many Queries for Simulation-Based Inference
arXiv:2601.22951v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) methods typically amortize a single query, usually the posterior, so each further conditional or marginal needs its own model. Existing generalists amortize many queries in one transformer with one token per variable, so their attention cost grows quadratically with the number of variables. We introduce OneFlowSBI, a masked flow matching framework that trains one network on the joint distribution of parameters and observations. A ternary mask marks each coordinate as observed, generated or absent, so any conditional or marginal is sampled by changing the mask. The mask is an input, rather than a tokenization, so the backbone can follow the data's structure and scale to many variables. On high-dimensional image denoising and shallow water inference, OneFlowSBI achieves lower posterior error than the baselines, samples far faster than a transformer-based generalist, and handles missing regions and partial observation windows without retraining. On most of the ten SBIBM tasks at the largest simulation budget, it matches or exceeds neural posterior estimation and is comparable to transformer-based generalists, while also providing the likelihood and marginals.
Abstract:Simulation-based inference (SBI) methods typically amortize a single query, usually the posterior, so each further conditional or marginal needs its own model. Existing generalists amortize many queries in one transformer with one token per variable, so their attention cost grows quadratically with the number of variables. We introduce OneFlowSBI, a masked flow matching framework that trains one network on the joint distribution of parameters and observations. A ternary mask marks each coordinate as observed, generated or absent, so any conditional or marginal is sampled by changing the mask. The mask is an input, rather than a tokenization, so the backbone can follow the data's structure and scale to many variables. On high-dimensional image denoising and shallow water inference, OneFlowSBI achieves lower posterior error than the baselines, samples far faster than a transformer-based generalist, and handles missing regions and partial observation windows without retraining. On most of the ten SBIBM tasks at the largest simulation budget, it matches or exceeds neural posterior estimation and is comparable to transformer-based generalists, while also providing the likelihood and marginals.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2601.22951 [stat.ML]
(or arXiv:2601.22951v2 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2601.22951 Focus to learn more arXiv-issued DOI via DataCite
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