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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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2 results for “cut-feedback”

Neural variational inference for cutting feedback during uncertainty propagationstat.ML

NeVI-Cut uses neural networks to efficiently propagate uncertainty without feedback.

problem Efficiently propagating uncertainty in downstream Bayesian analysis without feedback.

method NeVI-Cut combines neural networks and normalizing flows for variational inference.

result NeVI-Cut achieves significant computational gains and higher accuracy than traditional methods.

uncertainty propagation variational inference cut-feedback neural networks normalizing flows

2025-10-11abs ↗pdf ↗

MOCA: A Transformer-based Modular Causal Inference Framework with One-way Cross-attention and Cutting Feedbackstat.ML

MOCA uses modular attention to estimate causal effects from complex data.

problem Estimating causal effects from observational data with complex, non-linear, and high-dimensional treatment and outcome mechanisms.

method MOCA is a transformer-based framework that separates treatment and outcome modeling through modular design and one-way attention mechanism, with cutting-feedback to prevent outcome influence on treatment representations.

result MOCA outperforms classical estimators and machine learning approaches across various simulated and real-world scenarios.

Transformer Causal Inference One-way Attention Confounding Adjustment Machine Learning

2026-04-25abs ↗pdf ↗