有效的决策涉及将过去的经验和相关上下文信息与新型情况联系起来。在深入的强化学习中，主导范式是代理商摊销信息，通过训练损失的梯度下降来帮助决策进入其网络权重。在这里，我们采用了一种替代方法，其中代理可以利用大规模上下文敏感的数据库查找来支持其参数计算。这使代理商可以直接以端到端的方式学习，以利用相关信息来告知其输出。此外，代理可以通过简单地扩大检索数据集来了解新信息，而无需再进行重新培训。我们在GO中研究这种方法，这是一款具有挑战性的游戏，庞大的组合状态空间特权对与过去的体验进行了直接匹配。我们利用快速，大约最近的邻居技术来从数千万的专家示范状态中检索相关数据。参与此信息为简单地将这些示范作为训练轨迹而言，可以显着提高预测准确性和游戏性能，从而使大规模检索在加强学习剂中的价值提供了令人信服的演示。

强化学习中的信用作业是衡量行动对未来奖励的影响的问题。特别是，这需要从运气中分离技能，即解除外部因素和随后的行动对奖励行动的影响。为实现这一目标，我们将来自因果关系的反事件的概念调整为无模型RL设置。关键思想是通过学习从轨迹中提取相关信息来应对未来事件的价值函数。我们制定了一系列政策梯度算法，这些算法使用这些未来条件的价值函数作为基准或批评，并表明它们是可怕的差异。为避免对未来信息的调理潜在偏见，我们将后视信息限制为不包含有关代理程序行为的信息。我们展示了我们对许多说明性和具有挑战性问题的算法的功效和有效性。

Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics governing the environment are often complex and unknown. In this work we present the MuZero algorithm which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics. MuZero learns a model that, when applied iteratively, predicts the quantities most directly relevant to planning: the reward, the action-selection policy, and the value function. When evaluated on 57 different Atari games -the canonical video game environment for testing AI techniques, in which model-based planning approaches have historically struggled -our new algorithm achieved a new state of the art. When evaluated on Go, chess and shogi, without any knowledge of the game rules, MuZero matched the superhuman performance of the AlphaZero algorithm that was supplied with the game rules.

We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When this subgroup confounds all observed data, neither covariate shift nor label shift assumptions apply. We show that the optimal target predictor can be non-parametrically identified with the help of concept and proxy variables available only in the source domain, and unlabeled data from the target. The identification results are constructive, immediately suggesting an algorithm for estimating the optimal predictor in the target. For continuous observations, when this algorithm becomes impractical, we propose a latent variable model specific to the data generation process at hand. We show how the approach degrades as the size of the shift changes, and verify that it outperforms both covariate and label shift adjustment.

We introduce the Conditional Independence Regression CovariancE (CIRCE), a measure of conditional independence for multivariate continuous-valued variables. CIRCE applies as a regularizer in settings where we wish to learn neural features $\varphi(X)$ of data $X$ to estimate a target $Y$, while being conditionally independent of a distractor $Z$ given $Y$. Both $Z$ and $Y$ are assumed to be continuous-valued but relatively low dimensional, whereas $X$ and its features may be complex and high dimensional. Relevant settings include domain-invariant learning, fairness, and causal learning. The procedure requires just a single ridge regression from $Y$ to kernelized features of $Z$, which can be done in advance. It is then only necessary to enforce independence of $\varphi(X)$ from residuals of this regression, which is possible with attractive estimation properties and consistency guarantees. By contrast, earlier measures of conditional feature dependence require multiple regressions for each step of feature learning, resulting in more severe bias and variance, and greater computational cost. When sufficiently rich features are used, we establish that CIRCE is zero if and only if $\varphi(X) \perp \!\!\! \perp Z \mid Y$. In experiments, we show superior performance to previous methods on challenging benchmarks, including learning conditionally invariant image features.

The detection and prevention of illegal fishing is critical to maintaining a healthy and functional ecosystem. Recent research on ship detection in satellite imagery has focused exclusively on performance improvements, disregarding detection efficiency. However, the speed and compute cost of vessel detection are essential for a timely intervention to prevent illegal fishing. Therefore, we investigated optimization methods that lower detection time and cost with minimal performance loss. We trained an object detection model based on a convolutional neural network (CNN) using a dataset of satellite images. Then, we designed two efficiency optimizations that can be applied to the base CNN or any other base model. The optimizations consist of a fast, cheap classification model and a statistical algorithm. The integration of the optimizations with the object detection model leads to a trade-off between speed and performance. We studied the trade-off using metrics that give different weight to execution time and performance. We show that by using a classification model the average precision of the detection model can be approximated to 99.5% in 44% of the time or to 92.7% in 25% of the time.

Quantifying the deviation of a probability distribution is challenging when the target distribution is defined by a density with an intractable normalizing constant. The kernel Stein discrepancy (KSD) was proposed to address this problem and has been applied to various tasks including diagnosing approximate MCMC samplers and goodness-of-fit testing for unnormalized statistical models. This article investigates a convergence control property of the diffusion kernel Stein discrepancy (DKSD), an instance of the KSD proposed by Barp et al. (2019). We extend the result of Gorham and Mackey (2017), which showed that the KSD controls the bounded-Lipschitz metric, to functions of polynomial growth. Specifically, we prove that the DKSD controls the integral probability metric defined by a class of pseudo-Lipschitz functions, a polynomial generalization of Lipschitz functions. We also provide practical sufficient conditions on the reproducing kernel for the stated property to hold. In particular, we show that the DKSD detects non-convergence in moments with an appropriate kernel.

Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can simply project tokens in a continuous space of embeddings, as is standard in language modeling. We propose Self-conditioned Embedding Diffusion, a continuous diffusion mechanism that operates on token embeddings and allows to learn flexible and scalable diffusion models for both conditional and unconditional text generation. Through qualitative and quantitative evaluation, we show that our text diffusion models generate samples comparable with those produced by standard autoregressive language models - while being in theory more efficient on accelerator hardware at inference time. Our work paves the way for scaling up diffusion models for text, similarly to autoregressive models, and for improving performance with recent refinements to continuous diffusion.

当歌曲创作或演奏时，歌手/词曲作者通常会出现通过它表达感受或情感的意图。对于人类而言，将音乐作品或表演中的情感与观众的主观感知相匹配可能会非常具有挑战性。幸运的是，此问题的机器学习方法更简单。通常，它需要一个数据集，从该数据集中提取音频功能以将此信息呈现给数据驱动的模型，从而又将训练以预测给定歌曲与目标情绪匹配的概率是什么。在本文中，我们研究了最近出版物中最常见的功能和模型来解决此问题，揭示了哪些最适合在无伴奏歌曲中识别情感。

机器人的感知目前处于在有效的潜在空间中运行的现代方法与数学建立的经典方法之间的跨道路，并提供了可解释的，可信赖的结果。在本文中，我们引入了卷积的贝叶斯内核推理（Convbki）层，该层在可分离的卷积层中明确执行贝叶斯推断，以同时提高效率，同时保持可靠性。我们将层应用于3D语义映射的任务，在该任务中，我们可以实时学习激光雷达传感器信息的语义几何概率分布。我们根据KITTI数据集的最新语义映射算法评估我们的网络，并通过类似的语义结果证明了延迟的提高。