Tight Regret Bound for Online Inverse Linear Optimization via Multiscale Matrix Weights

arXiv:2609.26978 · stat.ML, cs.DS, cs.LG · Submitted 2026-09-22 · Read on arXiv

stat.ML, cs.DS, cs.LG

Submitted: 2026-09-22

Updated: 2026-09-22

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regret — 54× in this paper · explained in 4 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Trace — 8× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ambient Dimension — 1× in this paper
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Gap-Dependent Regret — 1× in this paper · explained in 2 episodes
• Generalization Bounds — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Reward Function — 1× in this paper · explained in 4 episodes
• Sparsity — 1× in this paper · explained in 4 episodes

Sources

• An Online-Learning Approach to Inverse Optimization
• Efficient Online Inverse Optimization with O(d) Regret
• Multiscale Reward Hedging from Correct Demonstrations
• Tight Generalization Bounds for Noiseless Inverse Optimization
• Online Inverse Integer Linear Optimization via Small-Gradient Skipping: Constant Regret and Finite Mistakes
• Simple Projection-Free Algorithm for Contextual Recommendation with Logarithmic Regret and Robustness

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