Universal Sequential Decisions in Unknown Environments

Learning to Play Sequential Games versus Unknown Opponents

We consider a repeated sequential game between a learner, who plays first, and an opponent who responds to the chosen action. We seek to design strategies for the learner to successfully interact with the opponent. While most previous approaches consider known opponent models, we focus on the setting in which the opponent's model is unknown. To this end, we use kernel-based regularity assumptions to capture and exploit the structure in the opponent's response. We propose a novel algorithm for the learner when playing against an adversarial sequence of opponents. The algorithm combines ideas fr…

具有未知延迟的在线序贯决策

Online Sequential Decision-Making with Unknown Delays

本文针对在线凸优化中反馈具有未知延迟的问题，提出三族适用于通用范数的延迟算法，并在一般凸性与相对强凸性下给出遗憾界，验证了其与现有最优界的一致性。

Towards a Universal Theory of Artificial Intelligence based on Algorithmic Probability and Sequential Decision Theory

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that the resulting universal AIXI model behaves optimal in any computable environment. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI^tl, which is still superior to any other time t and space l bounded ag…

SNAP：未知图下目标因果效应估计的序贯非祖先剪枝

SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph

针对多变量因果发现计算需求高的问题，提出SNAP框架序贯剪枝目标的确定非祖先，可作为预处理或独立算法，大幅减少计算量与检验次数，且不影响因果效应估计质量。

Meta-Learning Hypothesis Spaces for Sequential Decision-making

Obtaining reliable, adaptive confidence sets for prediction functions (hypotheses) is a central challenge in sequential decision-making tasks, such as bandits and model-based reinforcement learning. These confidence sets typically rely on prior assumptions on the hypothesis space, e.g., the known kernel of a Reproducing Kernel Hilbert Space (RKHS). Hand-designing such kernels is error prone, and misspecification may lead to poor or unsafe performance. In this work, we propose to meta-learn a kernel from offline data (Meta-KeL). For the case where the unknown kernel is a combination of known ba…

通过在线序贯检验识别智能过程

Identifying Intelligent Processes via Online Sequential Testing

本文提出将识别未知大语言模型的问题形式化为外部探针设计与内部序贯识别两层，外部问题等价于加权集合覆盖，内部问题给出评估次数上界。