我们研究了顺序预测和在线minimax遗憾的问题，并在一般损失函数下具有随机生成的特征。我们介绍了一个预期的最坏情况下的概念minimax遗憾，它概括并涵盖了先前已知的minimax遗憾。对于这种极匹马的遗憾，我们通过随机全局顺序覆盖的新颖概念建立了紧密的上限。我们表明，对于VC-Dimension $ \ Mathsf {Vc} $和$ I.I.D. $生成的长度$ t $的假设类别，随机全局顺序覆盖的基数可以在上限上限制高概率（WHP） e^{o（\ mathsf {vc} \ cdot \ log^2 t）} $。然后，我们通过引入一种称为Star-Littlestone维度的新复杂度度量来改善这种束缚，并显示与Star-Littlestone dimension $ \ Mathsf {Slsf {sl} $类别的类别允许订单的随机全局顺序覆盖$ e^{o（\ Mathsf） {sl} \ cdot \ log t）} $。我们进一步建立了具有有限脂肪的数字的真实有价值类的上限。最后，通过应用固定设计的Minimax遗憾的信息理论工具，我们为预期的最坏情况下的Minimax遗憾提供了下限。我们通过在预期的最坏情况下对对数损失和一般可混合损失的遗憾建立紧密的界限来证明我们的方法的有效性。

虽然注释大量的数据以满足复杂的学习模型，但对于许多现实世界中的应用程序可能会过于良好。主动学习（AL）和半监督学习（SSL）是两个有效但经常被隔离的方法，可以减轻渴望数据的问题。最近的一些研究探索了将AL和SSL相结合以更好地探测未标记数据的潜力。但是，几乎所有这些当代的SSL-AL作品都采用了简单的组合策略，忽略了SSL和AL的固有关系。此外，在处理大规模，高维数据集时，其他方法则遭受高计算成本。通过标记数据的行业实践的激励，我们提出了一种基于创新的基于不一致的虚拟对抗性积极学习（理想）算法，以进一步研究SSL-AL的潜在优势，并实现Al和SSL的相互增强，即SSL，即SSL宣传标签信息，以使标签信息无标记的样本信息并为Al提供平滑的嵌入，而AL排除了具有不一致的预测和相当不确定性的样品。我们通过不同粒度的增强策略（包括细粒度的连续扰动探索和粗粒数据转换）来估计未标记的样品的不一致。在文本和图像域中，广泛的实验验证了所提出的算法的有效性，并将其与最先进的基线进行了比较。两项实际案例研究可视化应用和部署所提出的数据采样算法的实际工业价值。

对话摘要已被广泛研究和应用，其中，先前的作品主要集中在探索卓越的模型结构方面，以对准输入对话和输出摘要。然而，对于专业对话（例如，法律辩论和医学诊断），语义/统计对齐可能几乎不会填补输入对话话语话语和外部知识的摘要输出之间的逻辑/事实差距。在本文中，我们主要研究了非预介绍和预用环境下对话检验摘要（DIS）的事实不一致问题。创新的端到端对话摘要生成框架是有两个辅助任务：预期事实方面正规化（EFAR）和缺少事实实体歧视（MFED）。综合实验表明，该模型可以以准确的事实方面的覆盖率来产生更可读的总结，以及通知用户从输入对话中检测到的潜在缺失事实以获得进一步的人为干预。

Global power systems are increasingly reliant on wind energy as a mitigation strategy for climate change. However, the variability of wind energy causes system reliability to erode, resulting in the wind being curtailed and, ultimately, leading to substantial economic losses for wind farm owners. Wind curtailment can be reduced using battery energy storage systems (BESS) that serve as onsite backup sources. Yet, this auxiliary role may significantly hamper the BESS's capacity to generate revenues from the electricity market, particularly in conducting energy arbitrage in the Spot market and providing frequency control ancillary services (FCAS) in the FCAS markets. Ideal BESS scheduling should effectively balance the BESS's role in absorbing onsite wind curtailment and trading in the electricity market, but it is difficult in practice because of the underlying coordination complexity and the stochastic nature of energy prices and wind generation. In this study, we investigate the bidding strategy of a wind-battery system co-located and participating simultaneously in both the Spot and Regulation FCAS markets. We propose a deep reinforcement learning (DRL)-based approach that decouples the market participation of the wind-battery system into two related Markov decision processes for each facility, enabling the BESS to absorb onsite wind curtailment while simultaneously bidding in the wholesale Spot and FCAS markets to maximize overall operational revenues. Using realistic wind farm data, we validated the coordinated bidding strategy for the wind-battery system and find that our strategy generates significantly higher revenue and responds better to wind curtailment compared to an optimization-based benchmark. Our results show that joint-market bidding can significantly improve the financial performance of wind-battery systems compared to individual market participation.

Driven by the global decarbonization effort, the rapid integration of renewable energy into the conventional electricity grid presents new challenges and opportunities for the battery energy storage system (BESS) participating in the energy market. Energy arbitrage can be a significant source of revenue for the BESS due to the increasing price volatility in the spot market caused by the mismatch between renewable generation and electricity demand. In addition, the Frequency Control Ancillary Services (FCAS) markets established to stabilize the grid can offer higher returns for the BESS due to their capability to respond within milliseconds. Therefore, it is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions. This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit. Then, Proximal Policy Optimization, a model-free deep reinforcement learning algorithm, is employed to learn the optimal bidding strategy from the dynamic environment of the energy market under a continuous bidding scale. The proposed model is trained and validated using real-world historical data of the Australian National Electricity Market. The results demonstrate that our developed joint bidding strategy in both markets is significantly profitable compared to individual markets.

Script event prediction aims to predict the subsequent event given the context. This requires the capability to infer the correlations between events. Recent works have attempted to improve event correlation reasoning by using pretrained language models and incorporating external knowledge~(e.g., discourse relations). Though promising results have been achieved, some challenges still remain. First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well. Second, modeling correlations between events with discourse relations is limited because it can only capture explicit correlations between events with discourse markers, and cannot capture many implicit correlations. To this end, we propose a novel generative approach for this task, in which a pretrained language model is fine-tuned with an event-centric pretraining objective and predicts the next event within a generative paradigm. Specifically, we first introduce a novel event-level blank infilling strategy as the learning objective to inject event-level knowledge into the pretrained language model, and then design a likelihood-based contrastive loss for fine-tuning the generative model. Instead of using an additional prediction layer, we perform prediction by using sequence likelihoods generated by the generative model. Our approach models correlations between events in a soft way without any external knowledge. The likelihood-based prediction eliminates the need to use additional networks to make predictions and is somewhat interpretable since it scores each word in the event. Experimental results on the multi-choice narrative cloze~(MCNC) task demonstrate that our approach achieves better results than other state-of-the-art baselines. Our code will be available at \url{https://github.com/zhufq00/mcnc}.

神经文本排名模型已经见证了显着的进步，并越来越多地在实践中部署。不幸的是，它们还继承了一般神经模型的对抗性脆弱性，这些神经模型已被检测到，但仍未被先前的研究所忽视。此外，Blackhat SEO可能会利用继承的对抗性漏洞来击败受保护的搜索引擎。在这项研究中，我们提出了对黑盒神经通道排名模型的模仿对抗攻击。我们首先表明，可以通过列举关键查询/候选者，然后训练排名模仿模型来透明和模仿目标段落排名模型。利用排名模仿模型，我们可以精心操纵排名结果并将操纵攻击转移到目标排名模型。为此，我们提出了一种由成对目标函数授权的基于创新的基于梯度的攻击方法，以产生对抗性触发器，该触发器会导致有预谋的混乱，而具有很少的令牌。为了配备触发器的伪装，我们将下一个句子预测损失和语言模型流利度限制添加到目标函数中。对通过排名的实验结果证明了对各种SOTA神经排名模型的排名模仿攻击模型和对抗触发器的有效性。此外，各种缓解分析和人类评估表明，在面对潜在的缓解方法时，伪装的有效性。为了激励其他学者进一步研究这一新颖和重要的问题，我们将实验数据和代码公开可用。

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.