In recent years, significant attention has been directed towards learning
average-reward Markov Decision Processes (MDPs). However, existing algorithms
either suffer from sub-optimal regret guarantees or computational
inefficiencies. In this paper, we present the first tractable algorithm with
minimax optimal regret of $\widetilde{\mathrm{O}}(\sqrt{\mathrm{sp}(h^*) S A
T})$, where $\mathrm{sp}(h^*)$ is the span of the optimal bias function $h^*$,
$S \times A$ is the size of the state-action space and $T$ the number of
learning steps. Remarkably, our algorithm does not require prior information on
$\mathrm{sp}(h^*)$. Our algorithm relies on a novel subroutine, Projected
Mitigated Extended Value Iteration (PMEVI), to compute bias-constrained optimal
policies efficiently. This subroutine can be applied to various previous
algorithms to improve regret bounds.

这篇论文介绍了一种具有最小最大后悔度的可行算法，该算法通过使用一种新颖的子程序，即 Projected Mitigated Extended Value Iteration（PMEVI），来高效地计算偏差受限最优策略。同时，该算法不需要先前关于偏差函数的信息。

在平均回报 MDPs 中实现可行的最小最优后悔

Achieving Tractable Minimax Optimal Regret in Average Reward MDPs

In this age where data is abundant, the ability to distill meaningful
insights from the sea of information is essential. Our research addresses the
computational and resource inefficiencies that current Sequential Recommender
Systems (SRSs) suffer from. especially those employing attention-based models
like SASRec, These systems are designed for next-item recommendations in
various applications, from e-commerce to social networks. However, such systems
suffer from substantial computational costs and resource consumption during the
inference stage. To tackle these issues, our research proposes a novel method
that combines automatic pruning techniques with advanced model architectures.
We also explore the potential of resource-constrained Neural Architecture
Search (NAS), a technique prevalent in the realm of recommendation systems, to
fine-tune models for reduced FLOPs, latency, and energy usage while retaining
or even enhancing accuracy. The main contribution of our work is developing the
Elastic Architecture Search for Efficient Long-term Sequential Recommender
Systems (EASRec). This approach aims to find optimal compact architectures for
attention-based SRSs, ensuring accuracy retention. EASRec introduces data-aware
gates that leverage historical information from input data batch to improve the
performance of the recommendation network. Additionally, it utilizes a dynamic
resource constraint approach, which standardizes the search process and results
in more appropriate architectures. The effectiveness of our methodology is
validated through exhaustive experiments on three benchmark datasets, which
demonstrates EASRec's superiority in SRSs. Our research set a new standard for
future exploration into efficient and accurate recommender systems, signifying
a substantial advancement within this swiftly advancing field.

我们的研究针对当前基于注意力模型的顺序推荐系统（SRSs）在计算和资源效率方面存在的困难进行了探讨，提出了结合自动修剪技术和先进模型架构的新方法。我们还探索了资源受限的神经架构搜索技术在减少计算成本、延迟和能源消耗方面的潜力，同时保持或提高准确性。我们的研究通过在三个基准数据集上进行详尽的实验验证了我们方法的有效性，证明了 EASRec 在 SRSs 中的优越性。这标志着在这个快速发展的领域内，我们为未来对高效准确的推荐系统的探索设定了新的标准。

EASRec：用于高效长期顺序推荐系统的弹性架构搜索

EASRec: Elastic Architecture Search for Efficient Long-term Sequential  Recommender Systems

Vision Transformers (ViTs) have become increasingly popular in large-scale
Vision and Language Pre-training (VLP) models. Although previous VLP research
has demonstrated the efficacy of ViTs, these efforts still struggle with
computational inefficiencies caused by lengthy visual sequences. To address
this challenge, we introduce an efficient VLP approach called TRIPS, which
stands for Text-Relevant Image Patch Selection. TRIPS progressively reduces the
visual sequence using a text-guided patch-selection layer in the visual
backbone, thereby accelerating both training and inference processes. This
patch-selection layer dynamically computes text-dependent visual attention,
enabling it to identify attentive image tokens with text guidance and fuse
inattentive ones in an end-to-end fashion. Importantly, TRIPS does not add any
extra parameters and generalizes to most ViT-based VLP models. We incorporate
TRIPS into three representative VLP models covering single-stream, dual-stream,
and generative paradigms, and conduct extensive experiments on five widely-used
multi-modal benchmark datasets. Our experimental results reveal that TRIPS
delivers a 40% speedup, while maintaining competitive or superior performance
on downstream tasks.

TRIPS 是一种高效的 VLP 方法，通过在视觉主干中引入一个文本导向的补丁选择层，以渐进的方式减少视觉序列，从而加速训练和推理过程，并通过动态计算文本相关的视觉注意力来识别注意力图像令牌，以无缝融合不注意的令牌。TRIPS 不添加额外参数且适用于大多数基于 ViT 的 VLP 模型。将 TRIPS 整合到三种代表性的 VLP 模型中，并在五个广泛使用的多模式基准数据集上进行了详尽的实验研究。实验结果表明，TRIPS 能够提供 40% 的加速效果，同时在下游任务上保持有竞争力或更好的性能。