认知偏见是人类在处理信息和环境中使用的精神捷径，这会导致偏见的行动和行为（或行动），对自己不知所措。偏见采取了多种形式，认知偏见占据了核心作用，造成公平，问责制，透明，道德，法律，医学和歧视。偏见的检测被认为是朝着缓解措施的必要步骤。在此，我们专注于两个认知偏见 - 锚定和新近度。计算机科学中认知偏见的识别在很大程度上是在信息检索的领域中，并且在注释数据的帮助下在总级别上确定了偏差。提出了不同的偏见检测方向，我们提供了一种原则性的方法，以及机器学习以从用户操作的Web日志中检测这两个认知偏见。我们的个人用户级别检测使其真正个性化，并且不依赖注释的数据。取而代之的是，我们从认知心理学中建立的两个基本原理开始，使用注意力网络的修改培训，并根据这些原则以新颖的方式解释注意力权重，以推断和区分这两种偏见。个性化方法允许对特定用户进行检测，这些用户在执行任务时容易受到这些偏见的影响，并且可以帮助他们之间建立意识以进行偏见缓解。

Off-policy evaluation methods are important in recommendation systems and search engines, where data collected under an existing logging policy is used to estimate the performance of a new proposed policy. A common approach to this problem is weighting, where data is weighted by a density ratio between the probability of actions given contexts in the target and logged policies. In practice, two issues often arise. First, many problems have very large action spaces and we may not observe rewards for most actions, and so in finite samples we may encounter a positivity violation. Second, many recommendation systems are not probabilistic and so having access to logging and target policy densities may not be feasible. To address these issues, we introduce the featurized embedded permutation weighting estimator. The estimator computes the density ratio in an action embedding space, which reduces the possibility of positivity violations. The density ratio is computed leveraging recent advances in normalizing flows and density ratio estimation as a classification problem, in order to obtain estimates which are feasible in practice.

在线强化学习（RL）算法通常难以在复杂的人体面对应用中部署，因为它们可能会缓慢学习并且早期性能差。为了解决这个问题，我们介绍了一种结合人类洞察速度学习的实用算法。我们的算法，约束采样增强学习（CSRL）将现有域知识包含为RL策略的约束/限制。它需要多种潜在的政策限制，以保持稳健性，以便在利用有用的时击败个体限制，以便快速学习。鉴于基础RL学习算法（例如UCRL，DQN，Rainbow），我们提出了对消除方案的上下置信度，该方案利用了限制与其观察性能之间的关系，以便自适应地切换它们。我们将我们的算法用DQN型算法和UCRL作为基础算法，并在四种环境中评估我们的算法，包括基于实际数据的三个模拟器：建议，教育活动排序和HIV处理测序。在所有情况下，CSRL比基线更快地学习良好的政策。

大多数用于边缘计算的强化学习（RL）推荐系统必须在推荐选择期间同步，或者依赖于算法的未经警告拼凑集合。在这项工作中，我们构建了异步凝固策略梯度算法\ citep {kostas2020aSynchronchronous}，为此问题提出了一个原则的解决方案。我们提出的算法类可以通过Internet分发，并实时地运行。当给定边缘无法响应具有足够速度的数据请求时，这不是问题;该算法旨在在边缘设置中函数和学习，网络问题是此设置的一部分。结果是一个原则性的理论地接地的RL算法，旨在分布在该异步环境中并学习。在这项工作中，我们详细描述了这种算法和建议的架构类，并且证明它们在异步设置中的实践中运行良好，即使网络质量降低。

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

Modern speech recognition systems exhibits rapid performance degradation under domain shift. This issue is especially prevalent in data-scarce settings, such as low-resource languages, where diversity of training data is limited. In this work we propose M2DS2, a simple and sample-efficient finetuning strategy for large pretrained speech models, based on mixed source and target domain self-supervision. We find that including source domain self-supervision stabilizes training and avoids mode collapse of the latent representations. For evaluation, we collect HParl, a $120$ hour speech corpus for Greek, consisting of plenary sessions in the Greek Parliament. We merge HParl with two popular Greek corpora to create GREC-MD, a test-bed for multi-domain evaluation of Greek ASR systems. In our experiments we find that, while other Unsupervised Domain Adaptation baselines fail in this resource-constrained environment, M2DS2 yields significant improvements for cross-domain adaptation, even when a only a few hours of in-domain audio are available. When we relax the problem in a weakly supervised setting, we find that independent adaptation for audio using M2DS2 and language using simple LM augmentation techniques is particularly effective, yielding word error rates comparable to the fully supervised baselines.

In this work, we propose a novel framework for estimating the dimension of the data manifold using a trained diffusion model. A trained diffusion model approximates the gradient of the log density of a noise-corrupted version of the target distribution for varying levels of corruption. If the data concentrates around a manifold embedded in the high-dimensional ambient space, then as the level of corruption decreases, the score function points towards the manifold, as this direction becomes the direction of maximum likelihood increase. Therefore, for small levels of corruption, the diffusion model provides us with access to an approximation of the normal bundle of the data manifold. This allows us to estimate the dimension of the tangent space, thus, the intrinsic dimension of the data manifold. Our method outperforms linear methods for dimensionality detection such as PPCA in controlled experiments.

This project leverages advances in multi-agent reinforcement learning (MARL) to improve the efficiency and flexibility of order-picking systems for commercial warehouses. We envision a warehouse of the future in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to maximise performance (e.g. order throughput) under given resource constraints. Established industry methods using heuristic approaches require large engineering efforts to optimise for innately variable warehouse configurations. In contrast, the MARL framework can be flexibly applied to any warehouse configuration (e.g. size, layout, number/types of workers, item replenishment frequency) and the agents learn via a process of trial-and-error how to optimally cooperate with one another. This paper details the current status of the R&D effort initiated by Dematic and the University of Edinburgh towards a general-purpose and scalable MARL solution for the order-picking problem in realistic warehouses.

With the success of Vision Transformers (ViTs) in computer vision tasks, recent arts try to optimize the performance and complexity of ViTs to enable efficient deployment on mobile devices. Multiple approaches are proposed to accelerate attention mechanism, improve inefficient designs, or incorporate mobile-friendly lightweight convolutions to form hybrid architectures. However, ViT and its variants still have higher latency or considerably more parameters than lightweight CNNs, even true for the years-old MobileNet. In practice, latency and size are both crucial for efficient deployment on resource-constraint hardware. In this work, we investigate a central question, can transformer models run as fast as MobileNet and maintain a similar size? We revisit the design choices of ViTs and propose an improved supernet with low latency and high parameter efficiency. We further introduce a fine-grained joint search strategy that can find efficient architectures by optimizing latency and number of parameters simultaneously. The proposed models, EfficientFormerV2, achieve about $4\%$ higher top-1 accuracy than MobileNetV2 and MobileNetV2$\times1.4$ on ImageNet-1K with similar latency and parameters. We demonstrate that properly designed and optimized vision transformers can achieve high performance with MobileNet-level size and speed.

Multimodal learning pipelines have benefited from the success of pretrained language models. However, this comes at the cost of increased model parameters. In this work, we propose Adapted Multimodal BERT (AMB), a BERT-based architecture for multimodal tasks that uses a combination of adapter modules and intermediate fusion layers. The adapter adjusts the pretrained language model for the task at hand, while the fusion layers perform task-specific, layer-wise fusion of audio-visual information with textual BERT representations. During the adaptation process the pre-trained language model parameters remain frozen, allowing for fast, parameter-efficient training. In our ablations we see that this approach leads to efficient models, that can outperform their fine-tuned counterparts and are robust to input noise. Our experiments on sentiment analysis with CMU-MOSEI show that AMB outperforms the current state-of-the-art across metrics, with 3.4% relative reduction in the resulting error and 2.1% relative improvement in 7-class classification accuracy.