We consider the nonstochastic multi-agent multi-armed bandit problem with agents collaborating via a communication network with delays. We show a lower bound for individual regret of all agents. We show that with suitable regularizers and communication protocols, a collaborative multi-agent \emph{follow-the-regularized-leader} (FTRL) algorithm has an individual regret upper bound that matches the lower bound up to a constant factor when the number of arms is large enough relative to degrees of agents in the communication graph. We also show that an FTRL algorithm with a suitable regularizer is regret optimal with respect to the scaling with the edge-delay parameter. We present numerical experiments validating our theoretical results and demonstrate cases when our algorithms outperform previously proposed algorithms.

本文为旋转组开发了旋转不变的阵阵卷积，因此（3）可以提炼球形信号的多尺度信息。球形的阵头变换从$ \ mathbb {s}^2 $推广到SO（3）组，该组通过一组紧密的Framelet操作员将球形信号分解为近似和详细的光谱系数。分解和重建过程中的球形信号实现了旋转不变性。基于阵型变换，我们形成了一个带有多个SO（3）一面卷积层的NEDLET近似均值球形CNN（NES）。该网络建立了一个强大的工具，可以提取球形信号的几何不变特征。该模型允许具有多分辨率表示的足够网络可伸缩性。通过小波收缩激活函数学习了强大的信号嵌入，该函数会过滤冗余高通表示，同时保持近似旋转不变性。 NES实现了量子化学回归和宇宙微波背景（CMB）的最新性能，删除重建，这显示了通过高分辨率和多尺度球形信号表示解决科学挑战的巨大潜力。

How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem. A key to solving this problem is to learn low-dimensional state representations from observations, from which an effective policy can be learned. In order to boost the learning of state encoding, recent works are focused on capturing behavioral similarities between state representations or applying data augmentation on visual observations. In this paper, we propose a novel meta-learner-based framework for representation learning regarding behavioral similarities for reinforcement learning. Specifically, our framework encodes the high-dimensional observations into two decomposed embeddings regarding reward and dynamics in a Markov Decision Process (MDP). A pair of meta-learners are developed, one of which quantifies the reward similarity and the other quantifies dynamics similarity over the correspondingly decomposed embeddings. The meta-learners are self-learned to update the state embeddings by approximating two disjoint terms in on-policy bisimulation metric. To incorporate the reward and dynamics terms, we further develop a strategy to adaptively balance their impacts based on different tasks or environments. We empirically demonstrate that our proposed framework outperforms state-of-the-art baselines on several benchmarks, including conventional DM Control Suite, Distracting DM Control Suite and a self-driving task CARLA.

Detecting personal health mentions on social media is essential to complement existing health surveillance systems. However, annotating data for detecting health mentions at a large scale is a challenging task. This research employs a multitask learning framework to leverage available annotated data from a related task to improve the performance on the main task to detect personal health experiences mentioned in social media texts. Specifically, we focus on incorporating emotional information into our target task by using emotion detection as an auxiliary task. Our approach significantly improves a wide range of personal health mention detection tasks compared to a strong state-of-the-art baseline.

The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a non-trivial task. Here we build on recent studies suggesting that using emotional information may improve upon this task. Our study results in a framework for health mention classification that incorporates affective features. We present two methods, an intermediate task fine-tuning approach (implicit) and a multi-feature fusion approach (explicit) to incorporate emotions into our target task of HMC. We evaluated our approach on 5 HMC-related datasets from different social media platforms including three from Twitter, one from Reddit and another from a combination of social media sources. Extensive experiments demonstrate that our approach results in statistically significant performance gains on HMC tasks. By using the multi-feature fusion approach, we achieve at least a 3% improvement in F1 score over BERT baselines across all datasets. We also show that considering only negative emotions does not significantly affect performance on the HMC task. Additionally, our results indicate that HMC models infused with emotional knowledge are an effective alternative, especially when other HMC datasets are unavailable for domain-specific fine-tuning. The source code for our models is freely available at https://github.com/tahirlanre/Emotion_PHM.

In this paper we revisit endless online level generation with the recently proposed experience-driven procedural content generation via reinforcement learning (EDRL) framework, from an observation that EDRL tends to generate recurrent patterns. Inspired by this phenomenon, we formulate a notion of state space closure, which means that any state that may appear in an infinite-horizon online generation process can be found in a finite horizon. Through theoretical analysis we find that though state space closure arises a concern about diversity, it makes the EDRL trained on a finite-horizon generalised to the infinite-horizon scenario without deterioration of content quality. Moreover, we verify the quality and diversity of contents generated by EDRL via empirical studies on the widely used Super Mario Bros. benchmark. Experimental results reveal that the current EDRL approach's ability of generating diverse game levels is limited due to the state space closure, whereas it does not suffer from reward deterioration given a horizon longer than the one of training. Concluding our findings and analysis, we argue that future works in generating online diverse and high-quality contents via EDRL should address the issue of diversity on the premise of state space closure which ensures the quality.

Evaluating neural network performance is critical to deep neural network design but a costly procedure. Neural predictors provide an efficient solution by treating architectures as samples and learning to estimate their performance on a given task. However, existing predictors are task-dependent, predominantly estimating neural network performance on image classification benchmarks. They are also search-space dependent; each predictor is designed to make predictions for a specific architecture search space with predefined topologies and set of operations. In this paper, we propose a novel All-in-One Predictor (AIO-P), which aims to pretrain neural predictors on architecture examples from multiple, separate computer vision (CV) task domains and multiple architecture spaces, and then transfer to unseen downstream CV tasks or neural architectures. We describe our proposed techniques for general graph representation, efficient predictor pretraining and knowledge infusion techniques, as well as methods to transfer to downstream tasks/spaces. Extensive experimental results show that AIO-P can achieve Mean Absolute Error (MAE) and Spearman's Rank Correlation (SRCC) below 1% and above 0.5, respectively, on a breadth of target downstream CV tasks with or without fine-tuning, outperforming a number of baselines. Moreover, AIO-P can directly transfer to new architectures not seen during training, accurately rank them and serve as an effective performance estimator when paired with an algorithm designed to preserve performance while reducing FLOPs.

Predicting neural architecture performance is a challenging task and is crucial to neural architecture design and search. Existing approaches either rely on neural performance predictors which are limited to modeling architectures in a predefined design space involving specific sets of operators and connection rules, and cannot generalize to unseen architectures, or resort to zero-cost proxies which are not always accurate. In this paper, we propose GENNAPE, a Generalized Neural Architecture Performance Estimator, which is pretrained on open neural architecture benchmarks, and aims to generalize to completely unseen architectures through combined innovations in network representation, contrastive pretraining, and fuzzy clustering-based predictor ensemble. Specifically, GENNAPE represents a given neural network as a Computation Graph (CG) of atomic operations which can model an arbitrary architecture. It first learns a graph encoder via Contrastive Learning to encourage network separation by topological features, and then trains multiple predictor heads, which are soft-aggregated according to the fuzzy membership of a neural network. Experiments show that GENNAPE pretrained on NAS-Bench-101 can achieve superior transferability to 5 different public neural network benchmarks, including NAS-Bench-201, NAS-Bench-301, MobileNet and ResNet families under no or minimum fine-tuning. We further introduce 3 challenging newly labelled neural network benchmarks: HiAML, Inception and Two-Path, which can concentrate in narrow accuracy ranges. Extensive experiments show that GENNAPE can correctly discern high-performance architectures in these families. Finally, when paired with a search algorithm, GENNAPE can find architectures that improve accuracy while reducing FLOPs on three families.

为了解决培训和测试数据之间的分布变化，域的概括（DG）利用多个源域来学习一个概括地看不见域的模型。但是，现有的DG方法通常遭受过度适应源域的影响，部分原因是特征空间中预期区域的覆盖率有限。在此激励的情况下，我们建议与数据插值和外推进行混合，以涵盖潜在的看不见区域。为了防止不受约束的外推的有害影响，我们仔细设计了一种策略来生成实例权重，名为Flatents-Awarnement-Awarnement-Awarnement-Awarness-Angients-Awments-Altents-Altents-Alignness-Actient-Actient-Actient-Actient-Actient-Actient-natments-Actient-Actient-Actient-natments-naterment-Actient-naterment-naterments-awite渐变的混音（FGMIX）。该政策采用基于梯度的相似性，将更大的权重分配给携带更多不变信息的实例，并了解相似性的功能，以提高最小值以更好地概括。在域基准测试中，我们验证了FGMIX各种设计的功效，并证明了其优于其他DG算法。

学习优化是一个快速增长的领域，旨在使用机器学习（ML）来解决优化问题或改善现有的优化算法。特别是，图形神经网络（GNN）被认为是用于优化问题的合适ML模型，其变量和约束是置换的 - 例如线性程序（LP）。尽管文献报道了令人鼓舞的数值结果，但本文确定了将GNN应用于解决LP的理论基础。给定LPS的任何尺寸限制，我们构造了一个GNN，该GNN将不同的LP映射到不同的输出。我们表明，正确构建的GNN可以可靠地预测广泛类别中每个LP的可行性，界限和最佳解决方案。我们的证明是基于最近发现的Weisfeiler-Lehman同构测试与GNN之间的联系。为了验证我们的结果，我们培训了一个简单的GNN，并提出了将LP映射到其可行性和解决方案中的准确性。