It is inexpensive and needs only one GPU week of training.

Section 2 provides the implementation details of Rubik's cube convolution within the image restora-4 Section 3 provides the evaluation of our proposed Rubik's cube convolution on the classification task. Section 4 provides more quantitative and qualitative results. Specifically, the input feature is separated into five groups, where the last four are shifted into four direction and the first is unchanged. Rubik's cube convolution operation into the baseline will achieve the performance improvement, "Acc-1" and "Acc-5" indicate the top-1 and As illustrated in Figure 3 and 4, integrating our Rubik's cube In contrast, the baseline combined with our Rubik's cube convolution operator achieves details

Open problem: "Can we extend [fully dynamic] results (algorithm or hardness) to non-monotone

It also enables potential positive knowledge transfer across tasks/domains, leading to improved accuracy and data-efficient training.

Deep Neural Networks and Reinforcement Learning methods have empirically shown great promise in tackling challenging combinatorial problems.

Moreover, query results (e.g., detected search and rescue activity in a particular region) are highly informative about the locations of target objects in other regions, for example, due to spatial

GFlowNet policies can efficiently find high-quality solutions.

Three strands of prior work motivate this study: minimax-quantile bounds restricted to non-interactive estimation; unified interactive analyses that focus on expected risk rather than risk level specific quantile bounds; and high-probability bandit bounds that still lack a quantile-specific toolkit for general interactive protocols. To close this gap, within the interactive statistical decision making framework, we develop high-probability Fano and Le Cam tools and derive risk level explicit minimax-quantile bounds, including a quantile-to-expectation conversion and a tight link between strict and lower minimax quan-tiles. Instantiating these results for the two-armed Gaussian bandit immediately recovers optimal-rate bounds. Index T erms-- information theory, learning theory 1. INTRODUCTION The concept of minimax risk has become a staple in learning theory and statistics [1-3]. In interactive or online learning settings, the minimax risk is often replaced by its counterpart, the minimax regret.