scikit-learn is an increasingly popular machine learning library. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant interface shared by all learning and processing units in the library and then discuss its advantages in terms of composition and reusability. The paper also comments on implementation details specific to the Python ecosystem and analyzes obstacles faced by users and developers of the library.

Despite a sea of interpretability methods that can produce plausible explanations, the field has also empirically seen many failure cases of such methods. In light of these results, it remains unclear for practitioners how to use these methods and choose between them in a principled way. In this paper, we show that for even moderately rich model classes (easily satisfied by neural networks), any feature attribution method that is complete and linear--for example, Integrated Gradients and SHAP--can provably fail to improve on random guessing for inferring model behaviour. Our results apply to common end-tasks such as identifying local model behaviour, spurious feature identification, and algorithmic recourse. One takeaway from our work is the importance of concretely defining end-tasks. In particular, we show that once such an end-task is defined, a simple and direct approach of repeated model evaluations can outperform many other complex feature attribution methods.

Microprocessor architects are increasingly resorting to domain-specific customization in the quest for high-performance and energy-efficiency. As the systems grow in complexity, fine-tuning architectural parameters across multiple sub-systems (e.g., datapath, memory blocks in different hierarchies, interconnects, compiler optimization, etc.) quickly results in a combinatorial explosion of design space. This makes domain-specific customization an extremely challenging task. Prior work explores using reinforcement learning (RL) and other optimization methods to automatically explore the large design space. However, these methods have traditionally relied on single-agent RL/ML formulations. It is unclear how scalable single-agent formulations are as we increase the complexity of the design space (e.g., full stack System-on-Chip design). Therefore, we propose an alternative formulation that leverages Multi-Agent RL (MARL) to tackle this problem. The key idea behind using MARL is an observation that parameters across different sub-systems are more or less independent, thus allowing a decentralized role assigned to each agent. We test this hypothesis by designing domain-specific DRAM memory controller for several workload traces. Our evaluation shows that the MARL formulation consistently outperforms single-agent RL baselines such as Proximal Policy Optimization and Soft Actor-Critic over different target objectives such as low power and latency. To this end, this work opens the pathway for new and promising research in MARL solutions for hardware architecture search.

本文解决了逆增强学习（IRL）的问题 - 从观察其行为中推断出代理的奖励功能。 IRL可以为学徒学习提供可概括和紧凑的代表，并能够准确推断人的偏好以帮助他们。 ％并提供更准确的预测。但是，有效的IRL具有挑战性，因为许多奖励功能可以与观察到的行为兼容。我们专注于如何利用先前的强化学习（RL）经验，以使学习这些偏好更快，更高效。我们提出了IRL算法基础（通过样本中的连续功能意图推断行为获取行为），该算法利用多任务RL预培训和后继功能，使代理商可以为跨越可能的目标建立强大的基础，从而跨越可能的目标。给定的域。当仅接触一些专家演示以优化新颖目标时，代理商会使用其基础快速有效地推断奖励功能。我们的实验表明，我们的方法非常有效地推断和优化显示出奖励功能，从而准确地从少于100个轨迹中推断出奖励功能。

我们研究了在室内路线上捕获的360度图像中的自动生成导航指令。现有的发电机遭受较差的视觉接地，导致它们依赖语言前沿和幻觉对象。我们的Marky-MT5系统通过专注于视觉地标来解决这一点;它包括第一阶段地标检测器和第二级发生器 - 多峰，多语言，多任务编码器 - 解码器。要培训它，我们在房间顶部（RXR）数据集的顶部引导地标注释。使用文本解析器，来自RXR的姿势迹线的弱监督，以及在1.8B图像上培训的多语言图像文本编码器，我们识别1.1M英语，印地语和泰卢语的地标描述并将其接地为Panoramas的特定区域。在房间到室内，人类途径在Marky-MT5的指示之后获得了71％的成功率（SR），只害羞他们的75％SR在人类指令之后 - 以及与其他发电机的SR高于SRS。对RXR更长的评估，不同的路径上的三种语言获得61-64％的SRS。在新颖环境中生成这种高质量的导航指令是迈向对话导航工具的一步，可以促进对指令跟随代理的大规模培训。

无监督的强化学习（RL）研究如何利用环境统计，在没有奖励工程成本的情况下学习有用的行为。然而，无监督的RL中的中央挑战是提取有意义地影响世界的行为，并涵盖可能的结果的范围，而不会被环境中固有的不可预测，无法控制和随机元素分散。为此，我们提出了一种无监督的RL方法，该方法是基于两项政策（我们呼叫探索和控制）之间的对手游戏而设计的高维，随机环境，控制单个身体并在观察熵的数量上竞争代理体验。探索代理寻求最大惊喜控制代理的状态，这反过来旨在最大限度地减少惊喜，从而操纵环境以返回熟悉和可预测的状态。这两项政策之间的竞争驱使他们寻求越来越令人惊讶的环境，同时学习掌握它们。我们正式显示所得算法，最大化块MDP的底层状态的覆盖率，随机观察，提供了对我们假设的理论备份，即该程序避免了无法控制和随机分心。我们的实验进一步表明对抗性惊喜导致复杂和有意义的技能的出现，并且在勘探和零射击转移到下游任务方面优于最先进的无监督的加强学习方法。