Introduces Bayesian Sensitivity Value (BSV) for causal inference sensitivity analysis based on evidence-derived priors and Monte Carlo estimation, applied to diabetes treatment effects.
Convex optimization
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PDOCRL is an oracle-efficient primal-dual method for offline constrained RL under general function approximation that returns near-optimal policies with O(eps^{-2}) samples under partial optimal-policy coverage and a stronger realizability condition.
A primal-dual smoothing reformulation converts discrete binary optimization into a continuous minimax problem solved by a convergent simultaneous gradient descent-ascent algorithm.
CSMC integrates column subset selection with low-rank matrix completion to reduce computation for asymmetric incomplete matrices while claiming competitive accuracy on synthetic and real tasks.
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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Offline Constrained Reinforcement Learning under Partial Data Coverage
PDOCRL is an oracle-efficient primal-dual method for offline constrained RL under general function approximation that returns near-optimal policies with O(eps^{-2}) samples under partial optimal-policy coverage and a stronger realizability condition.
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Smoothing Binary Optimization: A Primal-Dual Perspective
A primal-dual smoothing reformulation converts discrete binary optimization into a continuous minimax problem solved by a convergent simultaneous gradient descent-ascent algorithm.
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Randomized Approach to Matrix Completion: Applications in Recommendation Systems and Image Inpainting
CSMC integrates column subset selection with low-rank matrix completion to reduce computation for asymmetric incomplete matrices while claiming competitive accuracy on synthetic and real tasks.