Hawkes processes have recently risen to the forefront of tools when it comes to modeling and generating sequential events data. Multidimensional Hawkes processes model both the self and cross-excitation between different types of events and have been applied successfully in various domain such as finance, epidemiology and personalized recommendations, among others. In this work we present an adaptation of the Frank-Wolfe algorithm for learning multidimensional Hawkes processes. Experimental results show that our approach has better or on par accuracy in terms of parameter estimation than other first order methods, while enjoying a significantly faster runtime.

在过去的几年中，霍克斯流程的在线学习受到了越来越多的关注，尤其是用于建模演员网络。但是，这些作品通常会模拟事件或参与者的潜在群集之间的丰富相互作用，或者是参与者之间的网络结构。我们建议对参与者网络的潜在结构进行建模，以及在现实世界中的医疗和财务应用环境中进行的丰富互动。合成和现实世界数据的实验结果展示了我们方法的功效。

Hawkes流程最近从机器学习社区中引起了人们对建模事件序列数据的多功能性的越来越多的关注。尽管它们具有丰富的历史可以追溯到几十年前，但其某些属性（例如用于学习参数的样本复杂性和释放差异化私有版本的样本复杂性）尚未得到彻底的分析。在这项工作中，我们研究了具有背景强度$ \ mu $和激发功能$ \ alpha e^{ - \ beta t} $的标准霍克斯进程。我们提供$ \ mu $和$ \ alpha $的非私人和差异私人估计器，并在两种设置中获得样本复杂性结果以量化隐私成本。我们的分析利用了霍克斯过程的强大混合特性和经典的中央限制定理的结果，结果较弱的随机变量。我们在合成数据集和真实数据集上验证了我们的理论发现。

多代理市场仿真通常用于为下游机器学习或加强学习任务创建环境，例如在部署它们以实时交易之前培训或测试交易策略。在电子交易市场中，只有多个市场参与者的互动导致的价格或体积时间序列通常是直接可观察到的。因此，需要校准多代理市场环境，以使模拟代理的相互作用与历史相互作用导致的时间序列 - 这使得解决高度复杂的大规模优化问题。在本文中，我们提出了一种简单而有效的框架，可以从历史时间序列观测校准多代理市场模拟器参数。首先，我们考虑一个新颖的资格概念，以绕过潜在的不可识别性问题。其次，我们通过Bonferroni校正概括了两个样本的Kolmogorov-Smirnov（K-S）测试，以测试两个高维时间序列分布之间的相似性，这在时间序列样本集之间提供了一个简单但有效的距离度量。第三，我们建议使用贝叶斯优化（BO）和信任区域BO（Turbo）来最小化上述距离度量。最后，我们展示了使用数值实验的框架的效率。

在这项工作中，我们将时间系列预测解决为计算机视觉任务。我们将输入数据捕获为图像并培训模型以产生后续图像。这种方法导致预测分布而不是点的值。为了评估我们方法的稳健性和质量，我们检查各种数据集和多个评估指标。我们的实验表明，我们的预测工具对循环数据有效，但对于股票价格的不规则数据有点少。重要的是，在使用基于图像的评估指标时，我们发现我们的方法以优于各种基线，包括Arima，以及我们的深度学习方法的数值变化。

时间序列预测对于许多领域的决策是必不可少的。在这项工作中，我们解决了在多个可能互动的金融资产中预测价格进化的挑战。对此问题的解决方案对各国政府，银行和投资者来说具有明显的重要性。统计方法如自动回归综合移动平均（Arima）被广泛应用于这些问题。在本文中，我们建议通过视频预测以新颖的方式接近多种金融资产的经济时序预测。鉴于经过多次潜在的互动金融资产价格，我们的目标是预测未来的价格进化。我们在每次作为向量中处理每次的价格的快照，而是在2D中将这些价格空间布局作为图像，使得我们可以利用CNNS学习这些金融资产的潜在代表的力量。因此，这些价格的历史成为一系列图像，我们的目标成为预测未来的图像。我们建立在最先进的视频预测方法中，用于预测未来图像。我们的实验涉及在美国股市交易的九个金融资产价格演变的预测任务。所提出的方法优于基准的基线，包括ARIMA，先知和所提出的方法的变化，展示了利用CNNS在经济时序预测问题中利用CNN的力量的好处。

在这项工作中，我们将时间系列预测解决为计算机视觉任务。我们将输入数据捕获为图像并培训模型以产生后续图像。这种方法导致预测分布而不是点的值。为了评估我们方法的稳健性和质量，我们检查各种数据集和多个评估指标。我们的实验表明，我们的预测工具对循环数据有效，但对于股票价格的不规则数据有点少。重要的是，在使用基于图像的评估指标时，我们发现我们的方法以优于各种基线，包括Arima，以及我们的深度学习方法的数值变化。

Imitation learning (IL) is a simple and powerful way to use high-quality human driving data, which can be collected at scale, to identify driving preferences and produce human-like behavior. However, policies based on imitation learning alone often fail to sufficiently account for safety and reliability concerns. In this paper, we show how imitation learning combined with reinforcement learning using simple rewards can substantially improve the safety and reliability of driving policies over those learned from imitation alone. In particular, we use a combination of imitation and reinforcement learning to train a policy on over 100k miles of urban driving data, and measure its effectiveness in test scenarios grouped by different levels of collision risk. To our knowledge, this is the first application of a combined imitation and reinforcement learning approach in autonomous driving that utilizes large amounts of real-world human driving data.

Reliably planning fingertip grasps for multi-fingered hands lies as a key challenge for many tasks including tool use, insertion, and dexterous in-hand manipulation. This task becomes even more difficult when the robot lacks an accurate model of the object to be grasped. Tactile sensing offers a promising approach to account for uncertainties in object shape. However, current robotic hands tend to lack full tactile coverage. As such, a problem arises of how to plan and execute grasps for multi-fingered hands such that contact is made with the area covered by the tactile sensors. To address this issue, we propose an approach to grasp planning that explicitly reasons about where the fingertips should contact the estimated object surface while maximizing the probability of grasp success. Key to our method's success is the use of visual surface estimation for initial planning to encode the contact constraint. The robot then executes this plan using a tactile-feedback controller that enables the robot to adapt to online estimates of the object's surface to correct for errors in the initial plan. Importantly, the robot never explicitly integrates object pose or surface estimates between visual and tactile sensing, instead it uses the two modalities in complementary ways. Vision guides the robots motion prior to contact; touch updates the plan when contact occurs differently than predicted from vision. We show that our method successfully synthesises and executes precision grasps for previously unseen objects using surface estimates from a single camera view. Further, our approach outperforms a state of the art multi-fingered grasp planner, while also beating several baselines we propose.

Importance: Social determinants of health (SDOH) are known to be associated with increased risk of suicidal behaviors, but few studies utilized SDOH from unstructured electronic health record (EHR) notes. Objective: To investigate associations between suicide and recent SDOH, identified using structured and unstructured data. Design: Nested case-control study. Setting: EHR data from the US Veterans Health Administration (VHA). Participants: 6,122,785 Veterans who received care in the US VHA between October 1, 2010, and September 30, 2015. Exposures: Occurrence of SDOH over a maximum span of two years compared with no occurrence of SDOH. Main Outcomes and Measures: Cases of suicide deaths were matched with 4 controls on birth year, cohort entry date, sex, and duration of follow-up. We developed an NLP system to extract SDOH from unstructured notes. Structured data, NLP on unstructured data, and combining them yielded seven, eight and nine SDOH respectively. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using conditional logistic regression. Results: In our cohort, 8,821 Veterans committed suicide during 23,725,382 person-years of follow-up (incidence rate 37.18 /100,000 person-years). Our cohort was mostly male (92.23%) and white (76.99%). Across the six common SDOH as covariates, NLP-extracted SDOH, on average, covered 84.38% of all SDOH occurrences. All SDOH, measured by structured data and NLP, were significantly associated with increased risk of suicide. The SDOH with the largest effects was legal problems (aOR=2.67, 95% CI=2.46-2.89), followed by violence (aOR=2.26, 95% CI=2.11-2.43). NLP-extracted and structured SDOH were also associated with suicide. Conclusions and Relevance: NLP-extracted SDOH were always significantly associated with increased risk of suicide among Veterans, suggesting the potential of NLP in public health studies.