标准深度强化学习（DRL）旨在考虑收集的经验在制定政策方面的经验，旨在最大程度地提高预期奖励。这与人类决策不同，在人类的决策中，收益和损失的重视程度有所不同，而外围的结果被越来越多。它也无法利用通过合并分配环境来提高安全性和/或绩效的机会。已经研究了几种分配DRL的方法，其中一种流行的策略是评估预计的可能行动收益分配。我们提出了一种更直接的方法，通过优化了根据全剧集奖励的分布累积分布函数（CDF）指定的风险敏感目标。这种方法允许根据相对质量权衡结果，可用于连续和离散的动作空间，并且自然可以在约束和不受约束的设置中应用。我们展示了如何通过抽样来计算广泛的风险敏感目标的政策梯度的渐近一致估计，随后纳入了降低方差和正则化措施，以促进有效的实质性学习。然后，我们证明使用中等“悲观”的风险概况，强调了代理商表现不佳的场景，从而导致了增强的探索，并不断地专注于解决缺陷。我们在六个OpenAI安全健身房环境中使用不同的风险概况测试了该方法，与最先进的政策方法相比。没有成本限制，我们发现悲观的风险概况可用于降低成本，同时改善总奖励积累。借助成本限制，他们可以以规定的允许成本提供比风险中立的方法更高的积极奖励。

In robust Markov decision processes (MDPs), the uncertainty in the transition kernel is addressed by finding a policy that optimizes the worst-case performance over an uncertainty set of MDPs. While much of the literature has focused on discounted MDPs, robust average-reward MDPs remain largely unexplored. In this paper, we focus on robust average-reward MDPs, where the goal is to find a policy that optimizes the worst-case average reward over an uncertainty set. We first take an approach that approximates average-reward MDPs using discounted MDPs. We prove that the robust discounted value function converges to the robust average-reward as the discount factor $\gamma$ goes to $1$, and moreover, when $\gamma$ is large, any optimal policy of the robust discounted MDP is also an optimal policy of the robust average-reward. We further design a robust dynamic programming approach, and theoretically characterize its convergence to the optimum. Then, we investigate robust average-reward MDPs directly without using discounted MDPs as an intermediate step. We derive the robust Bellman equation for robust average-reward MDPs, prove that the optimal policy can be derived from its solution, and further design a robust relative value iteration algorithm that provably finds its solution, or equivalently, the optimal robust policy.

Creating compelling captions for data visualizations has been a longstanding challenge. Visualization researchers are typically untrained in journalistic reporting and hence the captions that are placed below data visualizations tend to be not overly engaging and rather just stick to basic observations about the data. In this work we explore the opportunities offered by the newly emerging crop of large language models (LLM) which use sophisticated deep learning technology to produce human-like prose. We ask, can these powerful software devices be purposed to produce engaging captions for generic data visualizations like a scatterplot. It turns out that the key challenge lies in designing the most effective prompt for the LLM, a task called prompt engineering. We report on first experiments using the popular LLM GPT-3 and deliver some promising results.

Universal cross-lingual sentence embeddings map semantically similar cross-lingual sentences into a shared embedding space. Aligning cross-lingual sentence embeddings usually requires supervised cross-lingual parallel sentences. In this work, we propose mSimCSE, which extends SimCSE to multilingual settings and reveal that contrastive learning on English data can surprisingly learn high-quality universal cross-lingual sentence embeddings without any parallel data. In unsupervised and weakly supervised settings, mSimCSE significantly improves previous sentence embedding methods on cross-lingual retrieval and multilingual STS tasks. The performance of unsupervised mSimCSE is comparable to fully supervised methods in retrieving low-resource languages and multilingual STS. The performance can be further enhanced when cross-lingual NLI data is available. Our code is publicly available at https://github.com/yaushian/mSimCSE.

Upcoming astronomical surveys will observe billions of galaxies across cosmic time, providing a unique opportunity to map the many pathways of galaxy assembly to an incredibly high resolution. However, the huge amount of data also poses an immediate computational challenge: current tools for inferring parameters from the light of galaxies take $\gtrsim 10$ hours per fit. This is prohibitively expensive. Simulation-based Inference (SBI) is a promising solution. However, it requires simulated data with identical characteristics to the observed data, whereas real astronomical surveys are often highly heterogeneous, with missing observations and variable uncertainties determined by sky and telescope conditions. Here we present a Monte Carlo technique for treating out-of-distribution measurement errors and missing data using standard SBI tools. We show that out-of-distribution measurement errors can be approximated by using standard SBI evaluations, and that missing data can be marginalized over using SBI evaluations over nearby data realizations in the training set. While these techniques slow the inference process from $\sim 1$ sec to $\sim 1.5$ min per object, this is still significantly faster than standard approaches while also dramatically expanding the applicability of SBI. This expanded regime has broad implications for future applications to astronomical surveys.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

增量任务学习（ITL）是一个持续学习的类别，试图培训单个网络以进行多个任务（一个接一个），其中每个任务的培训数据仅在培训该任务期间可用。当神经网络接受较新的任务培训时，往往会忘记旧任务。该特性通常被称为灾难性遗忘。为了解决此问题，ITL方法使用情节内存，参数正则化，掩盖和修剪或可扩展的网络结构。在本文中，我们提出了一个基于低级别分解的新的增量任务学习框架。特别是，我们表示每一层的网络权重作为几个等级1矩阵的线性组合。为了更新新任务的网络，我们学习一个排名1（或低级别）矩阵，并将其添加到每一层的权重。我们还引入了一个其他选择器向量，该向量将不同的权重分配给对先前任务的低级矩阵。我们表明，就准确性和遗忘而言，我们的方法的表现比当前的最新方法更好。与基于情节的内存和基于面具的方法相比，我们的方法还提供了更好的内存效率。我们的代码将在https://github.com/csiplab/task-increment-rank-update.git上找到。

我们提出了Tacobot，这是为首届Alexa Prive Taskbot Challenge构建的面向任务的对话系统，该系统可帮助用户完成多步骤烹饪和家庭装修任务。Tacobot的设计采用以用户为中心的原则，并渴望提供协作且易于访问的对话体验。为此，它具有准确的语言理解，灵活的对话管理和引人入胜的响应生成。此外，Tacobot还以强大的搜索引擎和自动化的端到端测试套件为支持。在引导Tacobot的开发中，我们探索了一系列数据增强策略，以训练先进的神经语言处理模型，并通过收集的真实对话不断改善对话经验。在半决赛结束时，Tacobot的平均评分为3.55/5.0。

Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stack blocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens. In this report we describe the model and the data, and document the current capabilities of Gato.

人类表演的法律文件中句子的注释是许多基于机器学习的系统支持法律任务的重要先决条件。通常，注释是按顺序完成的，句子句子通常是耗时的，因此昂贵。在本文中，我们介绍了一个概念验证系统，用于横向注释句子。该方法基于观察到含义类似的句子通常在特定类型系统方面具有相同的标签。我们在允许注释器中使用此观察来快速查看和注释在整个文档语料库中使用与给定句子的语义类似的句子。在这里，我们介绍了系统的界面并经验评估方法。实验表明，横向注释有可能使注释过程更快，更加一致。