Learning in Structured Stackelberg Games

arXiv:2504.09006 · cs.GT, cs.LG · Submitted 2025-04-11 · Read on arXiv

cs.GT, cs.LG

Submitted: 2025-04-11

Updated: 2026-09-21

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

Terminology

• Regret — 62× in this paper · explained in 3 episodes
• Sample Complexity — 24× in this paper · explained in 15 episodes
• Generalization — 15× in this paper · explained in 13 episodes
• Stackelberg Littlestone — 5× in this paper
• Adaptation — 3× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Meta-learning — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Agnostic Learning — 1× in this paper · explained in 1 episode
• In International Conference on Learning Representations — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes
• Stackelberg Standard Optimal Algorithm — 1× in this paper
• VC Dimension — 1× in this paper · explained in 2 episodes

Sources

• Learning to Play Multi-Follower Bayesian Stackelberg Games
• Chasing Moving Targets with Online Self-Play Reinforcement Learning for Safer Language Models
• Reward Shaping for (Inference-Time) Alignment: A Stackelberg Game Perspective
• Optimal Budgeted Adaptation of Large Language Models

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