迄今为止，游戏中的学习研究主要集中在正常形式游戏上。相比之下，我们以广泛的形式游戏（EFG），尤其是在许多代理商远远落后的EFG中对学习的理解，尽管它们与许多现实世界的应用更加接近。我们考虑了网络零和广泛表单游戏的天然类别，该游戏结合了代理收益的全球零和属性，图形游戏的有效表示以及EFG的表达能力。我们检查了这些游戏中乐观梯度上升（OGA）的收敛属性。我们证明，这种在线学习动力学的时间平均值表现出$ O（1/t）$ rate contergence convergence contergence contergence。此外，我们表明，对于某些与游戏有关的常数$ c> 0 $，日常行为也与速率$ o（c^{ - t}）$收敛到nash。

在本文中，我们解决了普通游戏中无遗憾的学习问题。具体而言，我们提供了一种简单实用的算法，实现了固定的阶梯大小的恒定遗憾。随着阶梯大小的增加，我们的算法的累积遗憾可被线性降低。我们的调查结果离开了现行范式，即消失的阶梯尺寸是迄今为止所有最先进的方法的低遗憾的先决条件。通过定义我们称之为Clairvoyant乘法权重更新（CMWU）的小说算法，从此范例转移到此范式。 CMWU是配备有精神模型的乘法权重更新（MWU），其在下一个时期中的系统状态有关系统的状态。每个代理记录其混合策略，即它对在下一期间在下一期间播放的信念，在此共享心理模型中，在此共享心理模型中使用MWU内部更新而没有对实际行为的任何改变，直到它平衡，从而标记其与第二天的真实结果一致。然后，只有那个代理在现实世界中采取行动，有效地在第二天的系统状态的“全面知识”，即它们是克莱师。CMWU有效充当MWU一天展望，实现有界遗憾。在技术水平，我们建立了任何选择的阶梯大小的自我一致的心理模型，并在其唯一性和线性时间计算的阶梯大小上提供界限收缩映射参数。我们的论点超越正常的游戏，几乎没有努力。

在博弈论中的精髓结果是von Neumann的Minmax定理，这些定理使得零和游戏承认基本上独特的均衡解决方案。古典学习结果对本定理构建，以表明在线无后悔动态会聚到零和游戏中的时间平均意义上的均衡。在过去几年中，一个关键的研究方向专注于表征这种动态的日常行为。一般结果在这个方向上表明，广泛的在线学习动态是循环的，并且在零和游戏中正式的Poincar {e}复发。在具有时间不变均衡的定期零和游戏的情况下，我们分析了这些在线学习行为的稳健性。该模型概括了通常的重复游戏制定，同时也是参与者之间反复竞争的现实和自然模型，这取决于外源性环境变化，如日期效果，周到一周的趋势和季节性。有趣的是，即使在最简单的这种情况下，也可能失败的时间平均收敛性，尽管有均衡是固定的。相比之下，使用新颖的分析方法，我们表明Poincar \'{E}尽管这些动态系统的复杂性，非自主性质，但是普及的复发概括。

For small training set sizes $P$, the generalization error of wide neural networks is well-approximated by the error of an infinite width neural network (NN), either in the kernel or mean-field/feature-learning regime. However, after a critical sample size $P^*$, we empirically find the finite-width network generalization becomes worse than that of the infinite width network. In this work, we empirically study the transition from infinite-width behavior to this variance limited regime as a function of sample size $P$ and network width $N$. We find that finite-size effects can become relevant for very small dataset sizes on the order of $P^* \sim \sqrt{N}$ for polynomial regression with ReLU networks. We discuss the source of these effects using an argument based on the variance of the NN's final neural tangent kernel (NTK). This transition can be pushed to larger $P$ by enhancing feature learning or by ensemble averaging the networks. We find that the learning curve for regression with the final NTK is an accurate approximation of the NN learning curve. Using this, we provide a toy model which also exhibits $P^* \sim \sqrt{N}$ scaling and has $P$-dependent benefits from feature learning.

Uniform-precision neural network quantization has gained popularity since it simplifies densely packed arithmetic unit for high computing capability. However, it ignores heterogeneous sensitivity to the impact of quantization errors across the layers, resulting in sub-optimal inference accuracy. This work proposes a novel neural architecture search called neural channel expansion that adjusts the network structure to alleviate accuracy degradation from ultra-low uniform-precision quantization. The proposed method selectively expands channels for the quantization sensitive layers while satisfying hardware constraints (e.g., FLOPs, PARAMs). Based on in-depth analysis and experiments, we demonstrate that the proposed method can adapt several popular networks channels to achieve superior 2-bit quantization accuracy on CIFAR10 and ImageNet. In particular, we achieve the best-to-date Top-1/Top-5 accuracy for 2-bit ResNet50 with smaller FLOPs and the parameter size.

We propose a sparse end-to-end multi-person pose regression framework, termed QueryPose, which can directly predict multi-person keypoint sequences from the input image. The existing end-to-end methods rely on dense representations to preserve the spatial detail and structure for precise keypoint localization. However, the dense paradigm introduces complex and redundant post-processes during inference. In our framework, each human instance is encoded by several learnable spatial-aware part-level queries associated with an instance-level query. First, we propose the Spatial Part Embedding Generation Module (SPEGM) that considers the local spatial attention mechanism to generate several spatial-sensitive part embeddings, which contain spatial details and structural information for enhancing the part-level queries. Second, we introduce the Selective Iteration Module (SIM) to adaptively update the sparse part-level queries via the generated spatial-sensitive part embeddings stage-by-stage. Based on the two proposed modules, the part-level queries are able to fully encode the spatial details and structural information for precise keypoint regression. With the bipartite matching, QueryPose avoids the hand-designed post-processes and surpasses the existing dense end-to-end methods with 73.6 AP on MS COCO mini-val set and 72.7 AP on CrowdPose test set. Code is available at https://github.com/buptxyb666/QueryPose.

The Shapley value (SV) is adopted in various scenarios in machine learning (ML), including data valuation, agent valuation, and feature attribution, as it satisfies their fairness requirements. However, as exact SVs are infeasible to compute in practice, SV estimates are approximated instead. This approximation step raises an important question: do the SV estimates preserve the fairness guarantees of exact SVs? We observe that the fairness guarantees of exact SVs are too restrictive for SV estimates. Thus, we generalise Shapley fairness to probably approximate Shapley fairness and propose fidelity score, a metric to measure the variation of SV estimates, that determines how probable the fairness guarantees hold. Our last theoretical contribution is a novel greedy active estimation (GAE) algorithm that will maximise the lowest fidelity score and achieve a better fairness guarantee than the de facto Monte-Carlo estimation. We empirically verify GAE outperforms several existing methods in guaranteeing fairness while remaining competitive in estimation accuracy in various ML scenarios using real-world datasets.

Process mining is a methodology for the derivation and analysis of process models based on the event log. When process mining is employed to analyze business processes, the process discovery step, the conformance checking step, and the enhancements step are repeated. If a user wants to analyze a process from multiple perspectives (such as activity perspectives, originator perspectives, and time perspectives), the above procedure, inconveniently, has to be repeated over and over again. Although past studies involving process mining have applied detailed stepwise methodologies, no attempt has been made to incorporate and optimize multi-perspective process mining procedures. This paper contributes to developing a solution approach to this problem. First, we propose an automatic discovery framework of a multi-perspective process model based on deep Q-Learning. Our Dual Experience Replay with Experience Distribution (DERED) approach can automatically perform process model discovery steps, conformance check steps, and enhancements steps. Second, we propose a new method that further optimizes the experience replay (ER) method, one of the key algorithms of deep Q-learning, to improve the learning performance of reinforcement learning agents. Finally, we validate our approach using six real-world event datasets collected in port logistics, steel manufacturing, finance, IT, and government administration. We show that our DERED approach can provide users with multi-perspective, high-quality process models that can be employed more conveniently for multi-perspective process mining.

The time-series forecasting (TSF) problem is a traditional problem in the field of artificial intelligence. Models such as Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and GRU (Gate Recurrent Units) have contributed to improving the predictive accuracy of TSF. Furthermore, model structures have been proposed to combine time-series decomposition methods, such as seasonal-trend decomposition using Loess (STL) to ensure improved predictive accuracy. However, because this approach is learned in an independent model for each component, it cannot learn the relationships between time-series components. In this study, we propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time series decomposition within a neural cell and learn correlations (autocorrelation and correlation) between each decomposition component. The proposed neural architecture was evaluated through comparative experiments with previous studies using five univariate time-series datasets and four multivariate time-series data. The results showed that long- and short-term predictive performance was improved by more than 10%. The experimental results show that the proposed CRU is an excellent method for TSF problems compared to other neural architectures.

Embodied Instruction Following (EIF) studies how mobile manipulator robots should be controlled to accomplish long-horizon tasks specified by natural language instructions. While most research on EIF are conducted in simulators, the ultimate goal of the field is to deploy the agents in real life. As such, it is important to minimize the data cost required for training an agent, to help the transition from sim to real. However, many studies only focus on the performance and overlook the data cost -- modules that require separate training on extra data are often introduced without a consideration on deployability. In this work, we propose FILM++ which extends the existing work FILM with modifications that do not require extra data. While all data-driven modules are kept constant, FILM++ more than doubles FILM's performance. Furthermore, we propose Prompter, which replaces FILM++'s semantic search module with language model prompting. Unlike FILM++'s implementation that requires training on extra sets of data, no training is needed for our prompting based implementation while achieving better or at least comparable performance. Prompter achieves 42.64% and 45.72% on the ALFRED benchmark with high-level instructions only and with step-by-step instructions, respectively, outperforming the previous state of the art by 6.57% and 10.31%.