Weproveanewlower bound onthe competitive ratio of any online algorithm in the setting where the costs aremstrongly convex and the movement costs are the squared`2 norm. This lower bound shows that no algorithm can achieveacompetitiveratio that iso(m 1/2) asmtendstozero.

Theorem 1.1 relies on two

We consider the non-stochastic Multi-Armed Bandit problem in a setting where there is a fixed and known metric on the action space that determines a cost for switching between any pair of actions.

Abstract-- Periodic 3D reconstruction is essential for crop monitoring, but costly when each cycle restarts from scratch, wasting resources and ignoring information from previous captures. We propose temporal-prior-guided view planning for periodic plant reconstruction, in which a previously reconstructed model of the same plant is non-rigidly aligned to a new partial observation to form an approximation of the current geometry. T o accommodate plant growth, we inflate this approximation and solve a set covering optimization problem to compute a minimal set of views. We integrated this method into a complete pipeline that acquires one additional next-best view before registration for robustness and then plans a globally shortest path to connect the planned set of views and outputs the best view sequence. Experiments on maize and tomato under hemisphere and sphere view spaces show that our system maintains or improves surface coverage while requiring fewer views and comparable movement cost compared to state-of-the-art baselines.

We study online convex optimization in a setting where the learner seeks to minimize the sum of a per-round hitting cost and a movement cost which is incurred when changing decisions between rounds.

In this setting, the learner receives context (e.g., weather conditions) at each round, and has to choose an action (e.g., turbine parameters).

Occlusion is a longstanding difficulty that challenges the UAV-based object detection. Many works address this problem by adapting the detection model. However, few of them exploit that the UAV could fundamentally improve detection performance by changing its viewpoint. Active Object Detection (AOD) offers an effective way to achieve this purpose. Through Deep Reinforcement Learning (DRL), AOD endows the UAV with the ability of autonomous path planning to search for the observation that is more conducive to target identification. Unfortunately, there exists no available dataset for developing the UAV AOD method. To fill this gap, we released a UAV's eye view active vision dataset named UEVAVD and hope it can facilitate research on the UAV AOD problem. Additionally, we improve the existing DRL-based AOD method by incorporating the inductive bias when learning the state representation. First, due to the partial observability, we use the gated recurrent unit to extract state representations from the observation sequence instead of the single-view observation. Second, we pre-decompose the scene with the Segment Anything Model (SAM) and filter out the irrelevant information with the derived masks. With these practices, the agent could learn an active viewing policy with better generalization capability. The effectiveness of our innovations is validated by the experiments on the UEVAVD dataset. Our dataset will soon be available at https://github.com/Leo000ooo/UEVAVD_dataset.

This paper introduces a framework for Bayesian Optimization (BO) with metric movement costs, addressing a critical challenge in practical applications where input alterations incur varying costs. Our approach is a convenient plug-in that seamlessly integrates with the existing literature on batched algorithms, where designs within batches are observed following the solution of a Traveling Salesman Problem. The proposed method provides a theoretical guarantee of convergence in terms of movement costs for BO. Empirically, our method effectively reduces average movement costs over time while maintaining comparable regret performance to conventional BO methods. This framework also shows promise for broader applications in various bandit settings with movement costs.

We consider the non-stochastic Multi-Armed Bandit problem in a setting where there is a fixed and known metric on the action space that determines a cost for switching between any pair of actions. The loss of the online learner has two components: the first is the usual loss of the selected actions, and the second is an additional loss due to switching between actions. Our main contribution gives a tight characterization of the expected minimax regret in this setting, in terms of a complexity measure C of the underlying metric which depends on its covering numbers.