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Conspiracies and regrets abound in Dune: Part Three trailer
《沙丘3》预告片揭晓阴谋与遗憾，保罗的誓言面临考验
"You promised me that you would never take power in your name."
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Evaluating AI Investment Strategies
从输入输出审计黑箱算法，动态政策遗憾可分解为协方差之和，理论透彻。
arXiv:2606.08791v1 Announce Type: cross Abstract: We study the problem of auditing a black-box algorithmic decision-maker from observable inputs and o…
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Mean-based algorithms: A lower bound and regret
均值算法理论突破，揭示其下界与遗憾的互补关系。
arXiv:2606.04931v1 Announce Type: new Abstract: Mean-based algorithms are a class of online learning algorithms that assign low probability to actions…
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Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach
用遗憾最小化方法优化LLM后训练，让大模型在交互环境中做出更低遗憾的决策，破解基础在线决策难题。
arXiv:2511.04393v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed as "agents" for decision-making (DM) in int…
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A Perturbation Approach to Unconstrained Linear Bandits
提出一种扰动方法，解决无约束线性赌博机的遗憾下界与最优算法设计问题，ICML 2026收录。
arXiv:2603.28201v2 Announce Type: replace Abstract: We revisit the standard perturbation-based approach of Abernethy et al. (2008) in the context of u…
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Calibeating for general proper losses: A Bregman divergence approach
将校准方法从Brier与对数损失推广至通用适当损失函数，基于Bregman散度与遗憾最小化框架创新。
arXiv:2605.17269v1 Announce Type: new Abstract: This work introduces a general framework for calibeating based on regret minimization. As compared to …
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Best-of-Both-Worlds for Heavy-Tailed Markov Decision Processes
提出首个在重尾MDP上同时实现随机与对抗环境最优遗憾的BoBW算法，突破保守局限。
arXiv:2602.01295v3 Announce Type: replace Abstract: We investigate episodic Markov Decision Processes with heavy-tailed losses (HTMDPs). Existing appr…
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A Regret Perspective on Online Multiple Testing
从遗憾视角统一在线多重检验的评估，揭示假阳性与假阴性非对称成本的优化新框架。
arXiv:2605.13916v1 Announce Type: cross Abstract: Online Multiple Testing (OMT), a fundamental pillar of sequential statistical inference, traditional…