To mitigate climate change, the share of renewable needs to be increased. Renewable energies introduce new challenges to power grids due to decentralization, reduced inertia and volatility in production. The operation of sustainable power grids with a high penetration of renewable energies requires new methods to analyze the dynamic stability. We provide new datasets of dynamic stability of synthetic power grids and find that graph neural networks (GNNs) are surprisingly effective at predicting the highly non-linear target from topological information only. To illustrate the potential to scale to real-sized power grids, we demonstrate the successful prediction on a Texan power grid model.

在过去的几年中，神经网络（NN）从实验室环境中发展为许多现实世界中的最新问题。结果表明，NN模型（即它们的重量和偏见）在训练过程中的重量空间中的独特轨迹上演变。随后，这种神经网络模型（称为模型动物园）的人群将在体重空间中形成结构。我们认为，这些结构的几何形状，曲率和平滑度包含有关训练状态的信息，并且可以揭示单个模型的潜在特性。使用这种模型动物园，可以研究（i）模型分析的新方法，（ii）发现未知的学习动力学，（iii）学习此类人群的丰富表示形式，或（iv）利用模型动物园来用于NN权重和NN权重的生成模型偏见。不幸的是，缺乏标准化模型动物园和可用的基准可以显着增加摩擦，以进一步研究NNS人群。通过这项工作，我们发布了一个新颖的模型动物园数据集，其中包含系统生成和多样化的NN模型种群，以进行进一步研究。总共提出的模型动物园数据集基于八个图像数据集，由27个模型动物园组成，该模型动物园训练有不同的超参数组合，包括50'360唯一的NN型号以及其稀疏双胞胎，导致超过3'844'360收集的型号。 。此外，对于模型动物园数据，我们提供了对动物园的深入分析，并为多个下游任务提供了基准。该数据集可在www.modelzoos.cc上找到。

给定模型动物园的神经网络权重的学习表示是一个新兴而具有挑战性的领域，从模型检查到神经体系结构搜索或知识蒸馏，具有许多潜在的应用。最近，在模型动物园进行训练的自动编码器能够学习一个超代理，该代表体捕获了动物园中模型的内在和外在特性。在这项工作中，我们扩展了超代表，以供生成使用以采样新的模型权重。我们提出的是层损失归一化，我们证明，这是基于超代表拓扑生成高性能模型和几种采样方法的关键。使用我们的方法生成的模型是多种多样的，性能的，并且能够超过强大的基准，从而在下游任务上进行了评估：初始化，合奏采样和传递学习。我们的结果表明，通过超代理通过过度代理，知识聚集从模型动物园到新模型的潜力，从而为新的研究方向铺平了途径。

给定模型动物园的神经网络权重的学习表示是一个新兴而具有挑战性的领域，从模型检查到神经体系结构搜索或知识蒸馏，具有许多潜在的应用。最近，在模型动物园进行训练的自动编码器能够学习一个超代理，该代表体捕获了动物园中模型的内在和外在特性。在这项工作中，我们扩展了超代表性的生成用途，以品尝新的模型权重作为预训练。我们提出的是层损失归一化，我们证明，这是生成高性能模型和基于超代表经验密度的采样方法的关键。使用我们的方法生成的模型是多种多样的，性能的，并且能够超过传统基线的转移学习。我们的结果表明，通过超代理通过过度代理，知识聚集从模型动物园到新模型的潜力，从而为新的研究方向铺平了途径。

能源过渡向可再生能源的成功的主要挑战之一是分析电网的动态稳定性。但是，动态解决方案非常棘手，对于大网格而言非常昂贵。图形神经网络（GNN）是一种有前途的方法，可以减少预测功率网格动态稳定性的计算工作，但是尚不存在适当的复杂性和大小的数据集。我们介绍了两个合成生成电网的新数据集。对于每个网格，使用蒙特卡洛模拟估算了动态稳定性。数据集的网格比以前发布的网格高10倍。为了评估现实世界应用的潜力，我们证明了在德克萨斯电力电网模型上的成功预测。通过在更多数据上训练更复杂的模型，可以将性能提高到令人惊讶的高水平。此外，调查的网格具有不同的尺寸，从而使分发评估的应用并从小域中转移到大型域。我们邀请社区改善我们的基准模型，从而通过更好的工具来帮助能源过渡。

已显示自我监督学习（SSL）学习有用和信息保存的表示。神经网络（NNS）被广泛应用，但它们的重量空间仍然不完全理解。因此，我们建议使用SSL来学习NNS群体重量的神经表示。为此，我们介绍域特定的数据增强和适应的关注架构。我们的实证评估表明，该领域的自我监督的代表学习能够恢复不同的NN模型特征。此外，我们表明所提出的学习表示始终是预测超参数，测试准确性和泛化差距以及转移到分发外设置的工作。

This paper addresses the kinodynamic motion planning for non-holonomic robots in dynamic environments with both static and dynamic obstacles -- a challenging problem that lacks a universal solution yet. One of the promising approaches to solve it is decomposing the problem into the smaller sub problems and combining the local solutions into the global one. The crux of any planning method for non-holonomic robots is the generation of motion primitives that generates solutions to local planning sub-problems. In this work we introduce a novel learnable steering function (policy), which takes into account kinodynamic constraints of the robot and both static and dynamic obstacles. This policy is efficiently trained via the policy optimization. Empirically, we show that our steering function generalizes well to unseen problems. We then plug in the trained policy into the sampling-based and lattice-based planners, and evaluate the resultant POLAMP algorithm (Policy Optimization that Learns Adaptive Motion Primitives) in a range of challenging setups that involve a car-like robot operating in the obstacle-rich parking-lot environments. We show that POLAMP is able to plan collision-free kinodynamic trajectories with success rates higher than 92%, when 50 simultaneously moving obstacles populate the environment showing better performance than the state-of-the-art competitors.

Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions. In practice, there exist noise and outliers in clustering task, which, however, may significantly degrade the performance. To address this issue, we propose a novel algorithm -- robust consensus clustering that can find common ground truth among experts' opinions, which tends to be minimally affected by the bias caused by the outliers. In particular, we formalize the robust consensus clustering problem as a constraint optimization problem, and then derive an effective algorithm upon alternating direction method of multipliers (ADMM) with rigorous convergence guarantee. Our method outperforms the baselines on benchmarks. We apply the proposed method to the real-world advertising campaign segmentation and forecasting tasks using the proposed consensus clustering results based on the similarity computed via Kolmogorov-Smirnov Statistics. The accurate clustering result is helpful for building the advertiser profiles so as to perform the forecasting.

In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint. This paper presents a bid recommendation scenario that discovers the concavity changes in click prediction curves. The recommended bid is derived based on the turning point from significant increase (i.e. concave downward) to slow increase (convex upward). Parametric learning based method is applied by solving the corresponding constraint optimization problem. Empirical studies on real-world advertising scenarios clearly demonstrate the performance gains for business metrics (including revenue increase, click increase and advertiser ROI increase).

In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions. Moreover, the bidding on advertising inventory has few connections with propensity one that can reach to target cost-per-acquisition (tCPA) goals. To address this problem, this paper presents a bid optimization scenario to achieve the desired tCPA goals for advertisers. In particular, we build the optimization engine to make a decision by solving the rigorously formalized constrained optimization problem, which leverages the bid landscape model learned from rich historical auction data using non-parametric learning. The proposed model can naturally recommend the bid that meets the advertisers' expectations by making inference over advertisers' historical auction behaviors, which essentially deals with the data challenges commonly faced by bid landscape modeling: incomplete logs in auctions, and uncertainty due to the variation and fluctuations in advertising bidding behaviors. The bid optimization model outperforms the baseline methods on real-world campaigns, and has been applied into a wide range of scenarios for performance improvement and revenue liftup.