我们提出了一种基于数据驱动的优化的预补偿方法，以通过修改参考轨迹而不修改任何内置的低级控制器来改善精度运动阶段的轮廓跟踪性能。精确运动阶段的位置是通过数据驱动模型预测的，线性低保真模型用于通过更改路径速度和加速度轮廓来优化遍历时间，然后使用非线性高效率模型来完善该模型先前找到了时间最佳解决方案。我们通过实验证明，所提出的方法能够同时提高高精度运动阶段的生产率和准确性。鉴于模型的基于数据的性质，提出的方法很容易适应广泛的精确运动系统。

我们提出了一个基于贝叶斯优化的昂贵评估高级制造方法的配置和操作的框架。该框架统一了量身定制的采集功能，并行的获取过程以及为优化过程提供上下文的过程信息的集成。\ cmtb {在最新的基准测试问题上展示，分析和比较了新颖的采集函数。我们将优化方法应用于大气等离子体喷涂和融合沉积建模。}我们的结果表明，所提出的框架可以有效地找到产生预期结果的输入参数并最大程度地减少过程成本。

我们考虑大规模的马尔可夫决策过程（MDP），具有未知的成本函数，采用随机凸优化工具，以解决模仿学习的问题，包括从有限一套专家演示学习政策。我们采用学徒学习形式主义，这承担了假设真正的成本函数可以表示为一些已知功能的线性组合。现有的逆钢筋学习算法具有强烈的理论保证，但是计算得昂贵，因为它们使用强化学习或计划算法作为子程序。另一方面，最先进的政策梯度基于基于梯度的算法（如IM-Conifforce，IM-TRPO和Gail），在具有挑战性的基准任务中实现了重大的经验成功，但在理论方面并不顺利。强调绩效的非渐近保证，我们提出了一种方法，即通过将问题作为占用措施的单个凸优化问题的问题绕过学习成本函数的中间步骤，提出了一种从专家演示中绕过策略的方法。我们开发了一种计算高效的算法，并在提取的策略的质量上导出了高信心遗憾，利用随机凸优化的结果以及近似线性编程的近似有效，用于解决前向MDP。

我们考虑了具有未知成本函数的大规模马尔可夫决策过程，并解决了从有限一套专家演示学习政策的问题。我们假设学习者不允许与专家互动，并且无法访问任何类型的加固信号。现有的逆钢筋学习方法具有强大的理论保证，但在计算上是昂贵的，而最先进的政策优化算法实现了重大的经验成功，但受到有限的理论理解受到阻碍。为了弥合理论与实践之间的差距，我们使用拉格朗日二元介绍了一种新的Bilinear鞍点框架。所提出的原始双视点允许我们通过随机凸优化的镜头开发出无模型可释放的算法。该方法享有实现，低内存要求和独立于州数量的计算和采样复杂性的优点。我们进一步提出了同等的无悔在线学习解释。

We demonstrate a proof-of-concept of a large language model conducting corporate lobbying related activities. We use an autoregressive large language model (OpenAI's text-davinci-003) to determine if proposed U.S. Congressional bills are relevant to specific public companies and provide explanations and confidence levels. For the bills the model deems as relevant, the model drafts a letter to the sponsor of the bill in an attempt to persuade the congressperson to make changes to the proposed legislation. We use hundreds of ground-truth labels of the relevance of a bill to a company to benchmark the performance of the model, which outperforms the baseline of predicting the most common outcome of irrelevance. However, we test the ability to determine the relevance of a bill with the previous OpenAI GPT-3 model (text-davinci-002), which was state-of-the-art on many language tasks until text-davinci-003 was released on November 28, 2022. The performance of text-davinci-002 is worse than simply always predicting that a bill is irrelevant to a company. These results suggest that, as large language models continue to improve core natural language understanding capabilities, performance on corporate lobbying related tasks will continue to improve. We then discuss why this could be problematic for societal-AI alignment.

Variational autoencoders model high-dimensional data by positing low-dimensional latent variables that are mapped through a flexible distribution parametrized by a neural network. Unfortunately, variational autoencoders often suffer from posterior collapse: the posterior of the latent variables is equal to its prior, rendering the variational autoencoder useless as a means to produce meaningful representations. Existing approaches to posterior collapse often attribute it to the use of neural networks or optimization issues due to variational approximation. In this paper, we consider posterior collapse as a problem of latent variable non-identifiability. We prove that the posterior collapses if and only if the latent variables are non-identifiable in the generative model. This fact implies that posterior collapse is not a phenomenon specific to the use of flexible distributions or approximate inference. Rather, it can occur in classical probabilistic models even with exact inference, which we also demonstrate. Based on these results, we propose a class of latent-identifiable variational autoencoders, deep generative models which enforce identifiability without sacrificing flexibility. This model class resolves the problem of latent variable non-identifiability by leveraging bijective Brenier maps and parameterizing them with input convex neural networks, without special variational inference objectives or optimization tricks. Across synthetic and real datasets, latent-identifiable variational autoencoders outperform existing methods in mitigating posterior collapse and providing meaningful representations of the data.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.

In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This class of systems is widely used in control engineering and econometrics, in particular, they represent a special case of recurrent neural networks. In this paper we 1) formalize the learning problem for stochastic LTI systems with inputs, 2) derive a PAC-Bayesian-Like error bound for such systems, 3) discuss various consequences of this error bound.

We demonstrate how efficient autonomous drone swarms can be in detecting and tracking occluded targets in densely forested areas, such as lost people during search and rescue missions. Exploration and optimization of local viewing conditions, such as occlusion density and target view obliqueness, provide much faster and much more reliable results than previous, blind sampling strategies that are based on pre-defined waypoints. An adapted real-time particle swarm optimization and a new objective function are presented that are able to deal with dynamic and highly random through-foliage conditions. Synthetic aperture sensing is our fundamental sampling principle, and drone swarms are employed to approximate the optical signals of extremely wide and adaptable airborne lenses.

Generative AI has matured to a point where large-scale models can generate text that seems indistinguishable from human-written text and remarkably photorealistic images. Automatically measuring how close the distribution of generated data is to the target real data distribution is a key step in diagnosing existing models and developing better models. We present MAUVE, a family of comparison measures between pairs of distributions such as those encountered in the generative modeling of text or images. These scores are statistical summaries of divergence frontiers capturing two types of errors in generative modeling. We explore four approaches to statistically estimate these scores: vector quantization, non-parametric estimation, classifier-based estimation, and parametric Gaussian approximations. We provide statistical bounds for the vector quantization approach. Empirically, we find that the proposed scores paired with a range of $f$-divergences and statistical estimation methods can quantify the gaps between the distributions of human-written text and those of modern neural language models by correlating with human judgments and identifying known properties of the generated texts. We conclude the paper by demonstrating its applications to other AI domains and discussing practical recommendations.