We propose GANStrument, a generative adversarial model for instrument sound synthesis. Given a one-shot sound as input, it is able to generate pitched instrument sounds that reflect the timbre of the input within an interactive time. By exploiting instance conditioning, GANStrument achieves better fidelity and diversity of synthesized sounds and generalization ability to various inputs. In addition, we introduce an adversarial training scheme for a pitch-invariant feature extractor that significantly improves the pitch accuracy and timbre consistency. Experimental results show that GANStrument outperforms strong baselines that do not use instance conditioning in terms of generation quality and input editability. Qualitative examples are available online.

Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy. We extend its applicability by developing an OPE method for a class of both full support and deficient support logging policies in contextual-bandit settings. This class includes deterministic bandit (such as Upper Confidence Bound) as well as deterministic decision-making based on supervised and unsupervised learning. We prove that our method's prediction converges in probability to the true performance of a counterfactual policy as the sample size increases. We validate our method with experiments on partly and entirely deterministic logging policies. Finally, we apply it to evaluate coupon targeting policies by a major online platform and show how to improve the existing policy.

本文介绍了Hitachi团队的建议自动采样系统，为自动采样的第一个共享任务（Automin-2021）。我们利用可参考方法（即，不使用培训分钟）进行自动采样（任务A），首先将转录成块分成块，随后将这些块与精细调整的预先训练的BART模型总结一下论聊天对话的概述语料库。此外，我们将参数挖掘技术应用于生成的分钟，以一种结构良好和连贯的方式重新组织它们。我们利用多个相关性分数来确定在给出的转录物或另一分钟时是否从相同的会议中衍生出一分钟（任务B和C）。在这些分数之上，我们培养传统的机器学习模型来绑定它们并进行最终决策。因此，我们的任务方法是在语法正确和流畅性方面，在所有提交的所有提交和最佳系统中实现最佳充分性评分。对于任务B和C，所提出的模型成功地表现了大多数投票基线。

算法在政策和业务中产生越来越多的决策和建议。这种算法决策是自然实验（可条件准随机分配的仪器），因为该算法仅基于可观察输入变量的决定。我们使用该观察来为一类随机和确定性决策算法开发治疗效果估算器。我们的估算器被证明对于明确的因果效应，它们是一致的和渐近正常的。我们估算器的一个关键特例是多维回归不连续性设计。我们应用估算员以评估冠状病毒援助，救济和经济安全（关心）法案的效果，其中数十亿美元的资金通过算法规则分配给医院。我们的估计表明，救济资金对Covid-19相关的医院活动水平影响不大。天真的OLS和IV估计表现出实质性的选择偏差。