Framework for generating covariance-preserving surrogates on directed graphs under a new definition of wide-sense stationarity to support statistical testing.
Title resolution pending
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
verdicts
UNVERDICTED 6representative citing papers
scCBGM adapts concept bottleneck generative models with skip connections and cross-covariance penalties for single-cell data, enabling interpretable counterfactual editing and showing superior combinatorial generalization on real datasets via a new synthetic benchmark.
LLM agents make collective belief dynamics programmable, with simulations showing coordinated agents induce stable belief shifts, and four structural properties that complicate detection and defense.
Unified targeted regularization framework for causal effect estimation with EDF outcomes using neural networks that jointly estimate outcome model, propensity scores, and fluctuation parameter.
A metadata-conditioned causal hierarchical VAE produces age-intervened counterfactual DXA spine images showing strong agreement with observed follow-up vertebral morphometry measurements in UK Biobank.
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
citing papers explorer
-
Statistical Testing on Directed Graphs by Surrogate Data Generation
Framework for generating covariance-preserving surrogates on directed graphs under a new definition of wide-sense stationarity to support statistical testing.
-
scCBGM: Interpretable Single-Cell Counterfactual Editing
scCBGM adapts concept bottleneck generative models with skip connections and cross-covariance penalties for single-cell data, enabling interpretable counterfactual editing and showing superior combinatorial generalization on real datasets via a new synthetic benchmark.
-
LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions
LLM agents make collective belief dynamics programmable, with simulations showing coordinated agents induce stable belief shifts, and four structural properties that complicate detection and defense.
-
Targeted Regularization for Causal Effect Estimation with Exponential Dispersion Family Outcomes
Unified targeted regularization framework for causal effect estimation with EDF outcomes using neural networks that jointly estimate outcome model, propensity scores, and fluctuation parameter.
-
From Baseline to Follow-Up: Counterfactual Spine DXA Image Synthesis in UK Biobank Using a Causal Hierarchical Variational Autoencoder
A metadata-conditioned causal hierarchical VAE produces age-intervened counterfactual DXA spine images showing strong agreement with observed follow-up vertebral morphometry measurements in UK Biobank.
-
ERPPO: Entropy Regularization-based Proximal Policy Optimization
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.