Introduces Bayesian Sensitivity Value (BSV) for causal inference sensitivity analysis based on evidence-derived priors and Monte Carlo estimation, applied to diabetes treatment effects.
Operations Research Letters , volume=
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HORST uses non-commutative operator composition and a hyperbolic mirror map to combine stability from adaptive optimizers with L1 sparsity bias, outperforming AdamW across sparsity levels on vision and language tasks.
The note claims linear convergence of WPO in entropy-regularized MDPs by combining mean-field gradient flow analysis with a local log-Sobolev inequality under a regularity assumption.
citing papers explorer
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Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors
Introduces Bayesian Sensitivity Value (BSV) for causal inference sensitivity analysis based on evidence-derived priors and Monte Carlo estimation, applied to diabetes treatment effects.
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HORST: Composing Optimizer Geometries for Sparse Transformer Training
HORST uses non-commutative operator composition and a hyperbolic mirror map to combine stability from adaptive optimizers with L1 sparsity bias, outperforming AdamW across sparsity levels on vision and language tasks.
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A note on convergence of Wasserstein policy optimization
The note claims linear convergence of WPO in entropy-regularized MDPs by combining mean-field gradient flow analysis with a local log-Sobolev inequality under a regularity assumption.