用预训练表格模型+自举采样，实现高效在线决策。
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
- 基于自举采样和上下文学习构建随机策略
- 在早期回合中显著降低累积损失（后悔值）
- 适合冷启动、数据稀疏的个性化推荐场景
上下文老虎机为高效个性化提供了自然框架，但在稀疏、有偏的交互数据、不可靠的不确定性估计和严重冷启动条件下，实际部署仍具挑战。本文研究是否可将具备上下文学习能力的预训练表格基础模型转化为在线决策的随机策略。提出BC-ICL方法：每轮从历史交互中抽取自举样本，将其作为上下文条件输入冻结的预训练ICL模型，对所有动作打分并选择最高分动作。进一步引入动作-上下文联合建模结构，增强动作间统计共享，避免孤立动作自举失败问题。实验表明，该策略在标准上下文老虎机基准上表现出色，尤其在早期回合中累积后悔值更低，在严格在线协议下优于现有基线。
原文摘要 · Abstract (English)
Contextual bandits offer a natural framework for sample-efficient personalization, but practical deployment remains difficult under sparse, biased interaction data, unreliable uncertainty estimates, and severe cold starts. We study whether pre-trained tabular foundation models with in-context learning can be turned into randomized policies for online decision making. We propose BC-ICL (Bootstrap-conditioned action selection using ICL), which at each round draws a bootstrap resample of the interaction history, conditions a frozen pre-trained ICL model on that resample, scores all actions, and selects the action with the highest sampled score. We further introduce an arm-context conditioning architecture that promotes shared statistical strength across actions and helps avoid common bootstrap failure modes of isolated-arm bandits. Empirically, this policy delivers strong early-round regret and regret performance on standard contextual bandit suites, outperforming established baselines under a strict online protocol.
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