We introduce the problem of Bayesian optimization with cost-varying variable subsets (BOCVS) where in each iteration, the learner chooses a subset of query variables and specifies their values while the rest are randomly sampled. Each chosen subset has an associated cost. This presents the learner with the novel challenge of balancing between choosing more informative subsets for more directed learning versus leaving some variables to be randomly sampled to reduce incurred costs. This paper presents a novel Gaussian process upper confidence bound-based algorithm for solving the BOCVS problem that is provably no-regret. We analyze how the availability of cheaper control sets helps in exploration and reduces overall regret. We empirically show that our proposed algorithm can find significantly better solutions than comparable baselines with the same budget.

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PCWorld reports that Intel's CPU supply chain is stabilizing as demand surges for agentic AI applications requiring enhanced processing power.

PCWorld reports Microsoft 365 Copilot has evolved from offering passive suggestions to actively making live changes in Excel, PowerPoint, and Word documents. The upgraded agentic capabilities allow Copilot to create presentations and documents from scratch, though with some limitations like missing graphics. These enhanced features are available across Microsoft 365 Copilot, Premium, Personal, and Family subscriptions, representing a significant productivity upgrade. Although Microsoft's Copilot reportedly remains far behind competing AI Large Language Models (LLMs) in terms of usage, the Copilot built into its Microsoft 365 applications remains a potent assistant.

Existing risk-aware multi-armed bandit models typically focus on risk measures of individual options such as variance. As a result, they cannot be directly applied to important real-world online decision making problems with correlated options. In this paper, we propose a novel Continuous Mean-Covariance Bandit (CMCB) model to explicitly take into account option correlation. Specifically, in CMCB, there is a learner who sequentially chooses weight vectors on given options and observes random feedback according to the decisions. The agent's objective is to achieve the best trade-off between reward and risk, measured with option covariance.