Online Non-Monotone DR-Submodular Maximization Matching the Offline 0.401 Factor

arXiv:2609.02145 · cs.LG, cs.AI, cs.CC, stat.ML · Submitted 2026-09-02 · Read on arXiv

cs.LG, cs.AI, cs.CC, stat.ML

Submitted: 2026-09-02

Updated: 2026-09-02

Terminology

• Regret — 61× in this paper · explained in 3 episodes
• Accuracy — 15× in this paper · explained in 1 episode
• Benchmark — 12× in this paper · explained in 2 episodes
• Admissibility — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Amortization — 1× in this paper · explained in 1 episode
• Modularity — 1× in this paper · explained in 9 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 2 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes

Sources

• Constrained Submodular Maximization via New Bounds for DR-Submodular Functions
• $\gamma$-weakly $\theta$-up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions
• Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks