Recursive generative retraining with heterogeneous rewards converges to a stable distribution satisfying a weighted Nash bargaining solution, preserving diversity under stated conditions.
Advances in Neural Information Processing Systems , volume=
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
Decentralized SGD and SGDA under Markovian sampling admit non-asymptotic generalization bounds that incorporate network topology, Markov mixing rates, and primal-dual dynamics.
SPACO is a single-loop penalty-based stochastic algorithm for minimax optimization with nonlinear coupled constraints, achieving non-asymptotic complexity bounds and asymptotic convergence to enhanced KKT points.
APEX is an assumption-free image quality metric using Sliced Wasserstein Distance on CLIP and DINOv2 embeddings that claims superior robustness to degradations and cross-dataset stability.
SABRE is a simulation-based bias correction framework that reduces finite-sample bias for the parametric component and dispersion parameter in semiparametric regression models, with asymptotic bias reduction without variance inflation shown for generalized partially linear models.
citing papers explorer
-
Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences
Recursive generative retraining with heterogeneous rewards converges to a stable distribution satisfying a weighted Nash bargaining solution, preserving diversity under stated conditions.
-
Stability and Generalization for Decentralized Markov SGD
Decentralized SGD and SGDA under Markovian sampling admit non-asymptotic generalization bounds that incorporate network topology, Markov mixing rates, and primal-dual dynamics.
-
A Single-Loop Penalty-based Algorithm for Stochastic Minimax Optimization with Nonlinear Coupled Constraints
SPACO is a single-loop penalty-based stochastic algorithm for minimax optimization with nonlinear coupled constraints, achieving non-asymptotic complexity bounds and asymptotic convergence to enhanced KKT points.
-
APEX: Assumption-free Projection-based Embedding eXamination Metric for Image Quality Assessment
APEX is an assumption-free image quality metric using Sliced Wasserstein Distance on CLIP and DINOv2 embeddings that claims superior robustness to degradations and cross-dataset stability.
-
Bias Correction for Semiparametric Regression Models
SABRE is a simulation-based bias correction framework that reduces finite-sample bias for the parametric component and dispersion parameter in semiparametric regression models, with asymptotic bias reduction without variance inflation shown for generalized partially linear models.