We study the problem of model selection in causal inference, specifically for the case of conditional average treatment effect (CATE) estimation under binary treatments. Unlike model selection in machine learning, we cannot use the technique of cross-validation here as we do not observe the counterfactual potential outcome for any data point. Hence, we need to design model selection techniques that do not explicitly rely on counterfactual data. As an alternative to cross-validation, there have been a variety of proxy metrics proposed in the literature, that depend on auxiliary nuisance models also estimated from the data (propensity score model, outcome regression model). However, the effectiveness of these metrics has only been studied on synthetic datasets as we can observe the counterfactual data for them. We conduct an extensive empirical analysis to judge the performance of these metrics, where we utilize the latest advances in generative modeling to incorporate multiple realistic datasets. We evaluate 9 metrics on 144 datasets for selecting between 415 estimators per dataset, including datasets that closely mimic real-world datasets. Further, we use the latest techniques from AutoML to ensure consistent hyperparameter selection for nuisance models for a fair comparison across metrics.

在计算机视觉中，面对域转移是很常见的：具有相同类但采集条件不同的图像。在域适应性（DA）中，人们希望使用源标记的图像对未标记的目标图像进行分类。不幸的是，在源训练集中训练的深度神经网络在不属于训练领域的目标图像上表现不佳。改善这些性能的一种策略是使用最佳传输（OT）在嵌入式空间中对齐源和目标图像分布。但是，OT会导致负转移，即与不同标签的样品对齐，这导致过度拟合，尤其是在域之间存在标签移动的情况下。在这项工作中，我们通过将其解释为针对目标图像的嘈杂标签分配来减轻负相位。然后，我们通过适当的正则化来减轻其效果。我们建议将混合正则化\ citep {zhang2018mixup}与噪音标签强大的损失，以提高域的适应性性能。我们在一项广泛的消融研究中表明，这两种技术的结合对于提高性能至关重要。最后，我们在几个基准和现实世界DA问题上评估了称为\ textsc {mixunbot}的方法。

专为单药加固学习（RL）设计的算法通常无法在两人零和零和游戏中收敛到平衡。相反，在2P0S游戏中近似NASH和量子响应平衡（QRE）的游戏理论算法通常对RL竞争，并且很难扩展。结果，这两种情况的算法通常是分别开发和评估的。在这项工作中，我们表明，单个算法是一种近端正则化的镜像下降的简单扩展，我们称之为磁性镜下降（MMD） - 尽管它们的基本差异都可以在两种情况下产生强大的结果。从理论的角度来看，我们证明了MMD在广泛的游戏中线性收敛到QRE-这是第一阶求解器首次证明线性收敛。此外，我们通过自我播放作为表格NASH均衡求解器应用，我们从经验上表明，MMD在正常形式和广泛的形式游戏中都具有全反馈（这是标准RL算法首次完成），在正常形式和广泛的形式游戏中产生竞争性竞争因此）以及MMD在黑盒反馈设置中经验收敛。此外，对于单人Deep RL，在一小部分Atari和Mujoco游戏中，我们表明MMD可以与PPO的结果竞争。最后，对于多代理Deep RL，我们显示MMD可以在3x3突然的黑暗中胜过NFSP。

我们调查随机镜面下降（SMD）的趋同相对光滑和平滑凸优化。在相对平滑的凸优化中，我们为SMD提供了新的收敛保证，并持续步骤。对于平滑的凸优化，我们提出了一种新的自适应步骤方案 - 镜子随机Polyak Spectize（MSP）。值得注意的是，我们的收敛导致两个设置都不会使有界渐变假设或有界方差假设，并且我们向邻域显示在插值下消失的邻居的融合。MSP概括了最近提出的随机Polyak Spectize（SPS）（Loizou等，2021）以镜子血液镜子，并且在继承镜子血清的好处的同时，现代机器学习应用仍然是实用和高效的。我们将我们的结果与各种监督的学习任务和SMD的不同实例相结合，展示了MSP的有效性。

用于解决无约束光滑游戏的两个最突出的算法是经典随机梯度下降 - 上升（SGDA）和最近引入的随机共识优化（SCO）[Mescheder等，2017]。已知SGDA可以收敛到特定类别的游戏的静止点，但是当前的收敛分析需要有界方差假设。 SCO用于解决大规模对抗问题，但其收敛保证仅限于其确定性变体。在这项工作中，我们介绍了预期的共同胁迫条件，解释了它的好处，并在这种情况下提供了SGDA和SCO的第一次迭代收敛保证，以解决可能是非单调的一类随机变分不等式问题。我们将两种方法的线性会聚到解决方案的邻域时，当它们使用恒定的步长时，我们提出了富有识别的步骤化切换规则，以保证对确切解决方案的融合。此外，我们的收敛保证在任意抽样范式下担保，因此，我们对迷你匹配的复杂性进行了解。

Deep neural networks excel at learning the training data, but often provide incorrect and confident predictions when evaluated on slightly different test examples. This includes distribution shifts, outliers, and adversarial examples. To address these issues, we propose Manifold Mixup, a simple regularizer that encourages neural networks to predict less confidently on interpolations of hidden representations. Manifold Mixup leverages semantic interpolations as additional training signal, obtaining neural networks with smoother decision boundaries at multiple levels of representation. As a result, neural networks trained with Manifold Mixup learn class-representations with fewer directions of variance. We prove theory on why this flattening happens under ideal conditions, validate it on practical situations, and connect it to previous works on information theory and generalization. In spite of incurring no significant computation and being implemented in a few lines of code, Manifold Mixup improves strong baselines in supervised learning, robustness to single-step adversarial attacks, and test log-likelihood.

Three-dimensional geometric data offer an excellent domain for studying representation learning and generative modeling. In this paper, we look at geometric data represented as point clouds. We introduce a deep AutoEncoder (AE) network with state-of-the-art reconstruction quality and generalization ability. The learned representations outperform existing methods on 3D recognition tasks and enable shape editing via simple algebraic manipulations, such as semantic part editing, shape analogies and shape interpolation, as well as shape completion. We perform a thorough study of different generative models including GANs operating on the raw point clouds, significantly improved GANs trained in the fixed latent space of our AEs, and Gaussian Mixture Models (GMMs). To quantitatively evaluate generative models we introduce measures of sample fidelity and diversity based on matchings between sets of point clouds. Interestingly, our evaluation of generalization, fidelity and diversity reveals that GMMs trained in the latent space of our AEs yield the best results overall.

In recent years distributional reinforcement learning has produced many state of the art results. Increasingly sample efficient Distributional algorithms for the discrete action domain have been developed over time that vary primarily in the way they parameterize their approximations of value distributions, and how they quantify the differences between those distributions. In this work we transfer three of the most well-known and successful of those algorithms (QR-DQN, IQN and FQF) to the continuous action domain by extending two powerful actor-critic algorithms (TD3 and SAC) with distributional critics. We investigate whether the relative performance of the methods for the discrete action space translates to the continuous case. To that end we compare them empirically on the pybullet implementations of a set of continuous control tasks. Our results indicate qualitative invariance regarding the number and placement of distributional atoms in the deterministic, continuous action setting.

Data scarcity is one of the main issues with the end-to-end approach for Speech Translation, as compared to the cascaded one. Although most data resources for Speech Translation are originally document-level, they offer a sentence-level view, which can be directly used during training. But this sentence-level view is single and static, potentially limiting the utility of the data. Our proposed data augmentation method SegAugment challenges this idea and aims to increase data availability by providing multiple alternative sentence-level views of a dataset. Our method heavily relies on an Audio Segmentation system to re-segment the speech of each document, after which we obtain the target text with alignment methods. The Audio Segmentation system can be parameterized with different length constraints, thus giving us access to multiple and diverse sentence-level views for each document. Experiments in MuST-C show consistent gains across 8 language pairs, with an average increase of 2.2 BLEU points, and up to 4.7 BLEU for lower-resource scenarios in mTEDx. Additionally, we find that SegAugment is also applicable to purely sentence-level data, as in CoVoST, and that it enables Speech Translation models to completely close the gap between the gold and automatic segmentation at inference time.

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as ``agnostic''. In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.