中文学习是指模型在及时序列中条件条件的能力，该序列由内部下文示例（输入输出对，与某些任务相对应）以及新的查询输入，并生成相应的输出。至关重要的是，内在学习仅在推理时间发生，而没有任何参数更新模型。尽管大型语言模型（例如GPT-3）具有某种能力来执行中文学习的能力，但尚不清楚任务成功的任务之间的关系以及培训数据中存在的内容。为了取得进步朝着理解文本学习的进步，我们考虑了训练模型的明确定义的问题，以学习函数类（例如，线性函数）：也就是说，给定的数据从类中的某些功能衍生而成，可以我们训练一个模型以在此课程中学习“大多数”功能？我们从经验上表明，可以从头开始训练标准变压器，以执行线性函数的文本学习 - 也就是说，训练有素的模型能够从具有与最佳最小二乘估计器相当的性能的示例中学习看不见的线性函数。实际上，即使在两种形式的分配变化下，也可能进行中文学习：（i）模型的训练数据和推理时间提示之间，以及（ii）在推理过程中的内在示例和查询输入之间。我们还表明，我们可以训练变形金刚在文本中学习更多复杂的功能类，即稀疏线性功能，两层神经网络和决策树 - 具有匹配或超过特定于任务特定的学习算法的性能。我们的代码和模型可在https://github.com/dtsip/in-context-learning上找到。

鉴于$ n $ i.i.d.从未知的分发$ P $绘制的样本，何时可以生成更大的$ n + m $ samples，这些标题不能与$ n + m $ i.i.d区别区别。从$ p $绘制的样品？（AXELROD等人2019）将该问题正式化为样本放大问题，并为离散分布和高斯位置模型提供了最佳放大程序。然而，这些程序和相关的下限定制到特定分布类，对样本扩增的一般统计理解仍然很大程度上。在这项工作中，我们通过推出通常适用的放大程序，下限技术和与现有统计概念的联系来放置对公司统计基础的样本放大问题。我们的技术适用于一大类分布，包括指数家庭，并在样本放大和分配学习之间建立严格的联系。

Softmax政策的政策梯度（PG）估计与子最佳饱和初始化无效，当密度集中在次良动作时发生。从策略初始化或策略已经收敛后发生的环境的突然变化可能会出现次优策略饱和度，并且SoftMax PG估计器需要大量更新以恢复有效的策略。这种严重问题导致高样本低效率和对新情况的适应性差。为缓解此问题，我们提出了一种新的政策梯度估计，用于软MAX策略，该估计在批评中利用批评中的偏差和奖励信号中存在的噪声来逃避策略参数空间的饱和区域。我们对匪徒和古典MDP基准测试任务进行了分析和实验，表明我们的估算变得更加坚固，以便对政策饱和度更加强大。

ML的梯度下降的成功尤其是学习神经网络是显着的和稳健的。在大脑如何学习的背景下，似乎在生物学上难以实现（如果不是难以判断）的梯度下降的一个方面是，其更新依赖于通过相同的连接到更早层的反馈。这种双向链路在脑网络中相对较少，即使存在互易连接时，它们也可能不等级。随机反馈对准（LillicRap等，2016），后向后重量是随机的和固定的，已经提出作为生物合理的替代品，并发现凭经验有效。我们调查如何以及当反馈对齐（FA）工作的方式，重点关注分层结构的最基本问题之一 - 低秩矩阵分解。在这个问题中，给定矩阵$ y_ {n \ times m} $，目标是找到低秩分解$ z_ {n \ times r} w_ {r \ times m} $，从而最小化错误$ \ | zw - 我\ | _f $。梯度血压最佳地解决了这个问题。我们显示FA收敛于当$ r \ ge \ mbox {rank}（y）$时收敛到最佳解决方案。我们还阐明了Fa工作的方式。经验上观察到前进权重矩阵和（随机）反馈矩阵在FA更新期间更接近。我们的分析严格地源地源于这种现象，并展示了如何促进FA的收敛。我们还表明，当$ r <\ mbox {rank}（y）$时，FA可能远非最佳。这是梯度下降和FA之间的第一个可提供的分离结果。此外，即使当它们的错误$ \ | zw-y \ | _f $大致相等时，梯度下降和fa发现的表示也可能是几乎正交的。

常见的策略梯度方法依赖于代理函数序列的最大化。近年来，已经提出了许多这样的代理功能，大多数没有强烈的理论担保，导致TRPO，PPO或MPO等算法。我们而不是设计另一个代理函数，而是根据功能镜中的函数提出一般框架（FMA-PG），这导致了整个代理功能。我们构建了使策略改进保证能够担保的代理功能，这是由最现有的代理职能共享的属性。至关重要，无论政策参数化的选择如何，这些保证都会持有。此外，FMA-PG的特定实例化恢复了重要的实施启发式（例如，使用前向VS反向KL发散），导致TRPO的变体具有额外的理想性质。通过对简单强盗问题的实验，我们评估FMA-PG实例化的算法。拟议的框架还提出了一种改进的PPO变体，其鲁棒性和效率我们在Mujoco套件上证明。

Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications. This is often because off-policy RL algorithms suffer from distributional shift, due to mismatch between dataset and the target policy, leading to high variance and over-estimation of value functions. In this work, we propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms. We show that the regularizer leads to a lower bound to the offline policy optimization objective, which can help avoid over-estimation errors, and explains the benefits of our approach across a range of continuous control domains when compared to existing state-of-the-art algorithms.

Recent advances in deep learning have enabled us to address the curse of dimensionality (COD) by solving problems in higher dimensions. A subset of such approaches of addressing the COD has led us to solving high-dimensional PDEs. This has resulted in opening doors to solving a variety of real-world problems ranging from mathematical finance to stochastic control for industrial applications. Although feasible, these deep learning methods are still constrained by training time and memory. Tackling these shortcomings, Tensor Neural Networks (TNN) demonstrate that they can provide significant parameter savings while attaining the same accuracy as compared to the classical Dense Neural Network (DNN). In addition, we also show how TNN can be trained faster than DNN for the same accuracy. Besides TNN, we also introduce Tensor Network Initializer (TNN Init), a weight initialization scheme that leads to faster convergence with smaller variance for an equivalent parameter count as compared to a DNN. We benchmark TNN and TNN Init by applying them to solve the parabolic PDE associated with the Heston model, which is widely used in financial pricing theory.

In the process of materials discovery, chemists currently need to perform many laborious, time-consuming, and often dangerous lab experiments. To accelerate this process, we propose a framework for robots to assist chemists by performing lab experiments autonomously. The solution allows a general-purpose robot to perform diverse chemistry experiments and efficiently make use of available lab tools. Our system can load high-level descriptions of chemistry experiments, perceive a dynamic workspace, and autonomously plan the required actions and motions to perform the given chemistry experiments with common tools found in the existing lab environment. Our architecture uses a modified PDDLStream solver for integrated task and constrained motion planning, which generates plans and motions that are guaranteed to be safe by preventing collisions and spillage. We present a modular framework that can scale to many different experiments, actions, and lab tools. In this work, we demonstrate the utility of our framework on three pouring skills and two foundational chemical experiments for materials synthesis: solubility and recrystallization. More experiments and updated evaluations can be found at https://ac-rad.github.io/arc-icra2023.

This paper proposes an easy-to-compute upper bound for the overlap index between two probability distributions without requiring any knowledge of the distribution models. The computation of our bound is time-efficient and memory-efficient and only requires finite samples. The proposed bound shows its value in one-class classification and domain shift analysis. Specifically, in one-class classification, we build a novel one-class classifier by converting the bound into a confidence score function. Unlike most one-class classifiers, the training process is not needed for our classifier. Additionally, the experimental results show that our classifier \textcolor{\colorname}{can be accurate with} only a small number of in-class samples and outperforms many state-of-the-art methods on various datasets in different one-class classification scenarios. In domain shift analysis, we propose a theorem based on our bound. The theorem is useful in detecting the existence of domain shift and inferring data information. The detection and inference processes are both computation-efficient and memory-efficient. Our work shows significant promise toward broadening the applications of overlap-based metrics.

We propose a framework in which multiple entities collaborate to build a machine learning model while preserving privacy of their data. The approach utilizes feature embeddings from shared/per-entity feature extractors transforming data into a feature space for cooperation between entities. We propose two specific methods and compare them with a baseline method. In Shared Feature Extractor (SFE) Learning, the entities use a shared feature extractor to compute feature embeddings of samples. In Locally Trained Feature Extractor (LTFE) Learning, each entity uses a separate feature extractor and models are trained using concatenated features from all entities. As a baseline, in Cooperatively Trained Feature Extractor (CTFE) Learning, the entities train models by sharing raw data. Secure multi-party algorithms are utilized to train models without revealing data or features in plain text. We investigate the trade-offs among SFE, LTFE, and CTFE in regard to performance, privacy leakage (using an off-the-shelf membership inference attack), and computational cost. LTFE provides the most privacy, followed by SFE, and then CTFE. Computational cost is lowest for SFE and the relative speed of CTFE and LTFE depends on network architecture. CTFE and LTFE provide the best accuracy. We use MNIST, a synthetic dataset, and a credit card fraud detection dataset for evaluations.