在时间序列预测的背景下，常用做法是评估多种方法，并选择其中一种方法或用于产生最佳预测的合奏。然而，在多种方法中选择不同的集合仍然是当方法的数量增加时，仍然是经历组合爆炸的具有挑战性的任务。在需求预测或收入预测的背景下，这一挑战在大量时间序列以及由于不断变化的业务环境而获得的有限的历史数据点，进一步加剧。虽然深入学习预测方法旨在同时预测大量时间序列，但由于有限的历史可用，可能不会产生理想的结果，它们变得挑战。我们提出了一种通过在使用交叉验证的潜在时间序列上组合低级时间矩阵分解和最佳模型选择来预测短高维时间序列数据的框架。我们展示预测潜在因子与直接应用于时间序列的不同UNI变化模型相比，潜在因子导致显着的性能提升。在M4月数据集的截断版本上验证了性能，其中包含来自来自多个域的时间序列数据，显示该方法的一般适用性。此外，由于在将预测方法直接应用于高维数据集时通常是不切实际的潜在因子而言，可以将未来的分析师视图纳入未来的分析师观。

Text classifiers have promising applications in high-stake tasks such as resume screening and content moderation. These classifiers must be fair and avoid discriminatory decisions by being invariant to perturbations of sensitive attributes such as gender or ethnicity. However, there is a gap between human intuition about these perturbations and the formal similarity specifications capturing them. While existing research has started to address this gap, current methods are based on hardcoded word replacements, resulting in specifications with limited expressivity or ones that fail to fully align with human intuition (e.g., in cases of asymmetric counterfactuals). This work proposes novel methods for bridging this gap by discovering expressive and intuitive individual fairness specifications. We show how to leverage unsupervised style transfer and GPT-3's zero-shot capabilities to automatically generate expressive candidate pairs of semantically similar sentences that differ along sensitive attributes. We then validate the generated pairs via an extensive crowdsourcing study, which confirms that a lot of these pairs align with human intuition about fairness in the context of toxicity classification. Finally, we show how limited amounts of human feedback can be leveraged to learn a similarity specification that can be used to train downstream fairness-aware models.

We study the sample complexity of reducing reinforcement learning to a sequence of empirical risk minimization problems over the policy space. Such reductions-based algorithms exhibit local convergence in the function space, as opposed to the parameter space for policy gradient algorithms, and thus are unaffected by the possibly non-linear or discontinuous parameterization of the policy class. We propose a variance-reduced variant of Conservative Policy Iteration that improves the sample complexity of producing a $\varepsilon$-functional local optimum from $O(\varepsilon^{-4})$ to $O(\varepsilon^{-3})$. Under state-coverage and policy-completeness assumptions, the algorithm enjoys $\varepsilon$-global optimality after sampling $O(\varepsilon^{-2})$ times, improving upon the previously established $O(\varepsilon^{-3})$ sample requirement.

We introduce Action-GPT, a plug and play framework for incorporating Large Language Models (LLMs) into text-based action generation models. Action phrases in current motion capture datasets contain minimal and to-the-point information. By carefully crafting prompts for LLMs, we generate richer and fine-grained descriptions of the action. We show that utilizing these detailed descriptions instead of the original action phrases leads to better alignment of text and motion spaces. Our experiments show qualitative and quantitative improvement in the quality of synthesized motions produced by recent text-to-motion models. Code, pretrained models and sample videos will be made available at https://actiongpt.github.io

A real-world application or setting involves interaction between different modalities (e.g., video, speech, text). In order to process the multimodal information automatically and use it for an end application, Multimodal Representation Learning (MRL) has emerged as an active area of research in recent times. MRL involves learning reliable and robust representations of information from heterogeneous sources and fusing them. However, in practice, the data acquired from different sources are typically noisy. In some extreme cases, a noise of large magnitude can completely alter the semantics of the data leading to inconsistencies in the parallel multimodal data. In this paper, we propose a novel method for multimodal representation learning in a noisy environment via the generalized product of experts technique. In the proposed method, we train a separate network for each modality to assess the credibility of information coming from that modality, and subsequently, the contribution from each modality is dynamically varied while estimating the joint distribution. We evaluate our method on two challenging benchmarks from two diverse domains: multimodal 3D hand-pose estimation and multimodal surgical video segmentation. We attain state-of-the-art performance on both benchmarks. Our extensive quantitative and qualitative evaluations show the advantages of our method compared to previous approaches.

This paper addresses the problem of reliably and efficiently solving broad classes of long-horizon stochastic path planning problems. Starting with a vanilla RL formulation with a stochastic dynamics simulator and an occupancy matrix of the environment, our approach computes useful options with policies as well as high-level paths that compose the discovered options. Our main contributions are (1) data-driven methods for creating abstract states that serve as endpoints for helpful options, (2) methods for computing option policies using auto-generated option guides in the form of dense pseudo-reward functions, and (3) an overarching algorithm for composing the computed options. We show that this approach yields strong guarantees of executability and solvability: under fairly general conditions, the computed option guides lead to composable option policies and consequently ensure downward refinability. Empirical evaluation on a range of robots, environments, and tasks shows that this approach effectively transfers knowledge across related tasks and that it outperforms existing approaches by a significant margin.

室内运动计划的重点是解决通过混乱环境导航代理的问题。迄今为止，在该领域已经完成了很多工作，但是这些方法通常无法找到计算廉价的在线路径计划和路径最佳之间的最佳平衡。除此之外，这些作品通常证明是单一启动单目标世界的最佳性。为了应对这些挑战，我们为在未知室内环境中进行导航的多个路径路径计划者和控制器堆栈，在该环境中，路点将目标与机器人必须在达到目标之前必须穿越的中介点一起。我们的方法利用全球规划师（在任何瞬间找到下一个最佳航路点），本地规划师（计划通往特定航路点的路径）以及自适应模型预测性控制策略（用于强大的系统控制和更快的操作） 。我们在一组随机生成的障碍图，中间航路点和起始目标对上评估了算法，结果表明计算成本显着降低，具有高度准确性和可靠的控制。

通过脑电图信号的情绪分类取得了许多进步。但是，诸如缺乏数据和学习重要特征和模式之类的问题始终是具有在计算和预测准确性方面改进的领域。这项工作分析了基线机器学习分类器在DEAP数据集上的性能以及一种表格学习方法，该方法提供了最新的可比结果，从而利用了性能提升，这是由于其深度学习架构而无需部署重型神经网络。

深尾学习旨在培训有用的深层网络，以实用现实世界中的不平衡分布，其中大多数尾巴类别的标签都与一些样本相关联。有大量的工作来训练判别模型，以进行长尾分布的视觉识别。相比之下，我们旨在训练有条件的生成对抗网络，这是一类长尾分布的图像生成模型。我们发现，类似于识别图像产生的最新方法类似，也遭受了尾部类别的性能降解。性能降解主要是由于尾部类别的类别模式塌陷，我们观察到与调节参数矩阵的光谱爆炸相关。我们提出了一种新型的组光谱正规剂（GSR），以防止光谱爆炸减轻模式崩溃，从而导致尾巴类别的形象产生多样化和合理的图像产生。我们发现GSR有效地与现有的增强和正则化技术结合在一起，从而导致长尾数据上的最新图像生成性能。广泛的实验证明了我们的常规器在不同程度不平衡的长尾数据集上的功效。

该报告涵盖了我们对Chaplot等人的“使用变压器的可区分空间计划”的复制工作。。在本文中，考虑了以可不同方式进行空间路径计划的问题。他们表明，他们提出的使用空间规划变压器的方法优于先前数据驱动的模型，并利用可不同的结构来学习映射而无需同时地面真相图。我们通过重现其实验并在新数据上测试其方法来验证这些主张。我们还通过地图提高了障碍物复杂性，研究了计划准确性的稳定性。努力调查和验证映射模块的学习的努力是由于缺乏计算资源和无法到达的作者而导致的失败。