我们研究$ k $ used的上下文决斗强盗问题，一个顺序决策制定设置，其中学习者使用上下文信息来制作两个决定，但只观察到\ emph {基于优先级的反馈}建议一个决定比另一个决定更好。我们专注于可实现的遗憾最小化问题，其中反馈由一个由给定函数类$ \ mathcal f $规定的成对偏好矩阵生成。我们提供了一种新的算法，实现了最佳反应遗憾的新概念的最佳遗憾，这是一个严格更强烈的性能测量，而不是先前作品所考虑的绩效衡量标准。该算法还在计算上有效，在多项式时间中运行，假设访问在线丢失回归超过$ \ mathcal f $。这可以解决dud \'ik等人的开放问题。[2015]关于Oracle高效，后悔 - 用于上下文决斗匪徒的最佳算法。

我们考虑基于偏好的加强学习（PBRL）的问题，在那里，与传统的增强学习不同，代理仅根据轨迹对的1位（0/1）偏好而不是对它们的绝对奖励来接收反馈。传统的RL框架的成功至关重要，依赖于潜在的代理 - 奖励模型，但是，这取决于系统设计者可以表达适当的奖励功能以及通常是非微不足道的任务。我们框架的主要新颖性是能够从基于偏好的轨迹反馈中学习，这消除了手工艺数字奖励模型的需要。本文为非马车奖励提供了一个正式的框架，其中轨道偏好是由尺寸为$ d $的广义线性模型编码。假设过渡模型是已知的，我们提出了一种算法，几乎最佳的$ \ tilde {\ mathcal {o}} \ left（sh d \ log（t / \ delta）\ sqrt {t} \右）$ 。进一步，将上述算法扩展到未知的转换动态的情况，并提供近最优遗憾的算法保证$ \ widetilde {\ mathcal {o}}（（\ sqrt {d} + h ^ 2 + | \ mathcal { s} |）\ sqrt {dt} + \ sqrt {| \ mathcal {s} || \ mathcal {a} | th}）$。据我们所知，我们的作品是第一个遗憾的遗忘遗嘱的首选RL问题之一，轨迹偏好。

我们在非稳定性或时间变化偏好下，在$ k $的武器{动态遗憾最小化}中研究了\ mpph {动态遗憾最小化}。这是一个在线学习设置，其中代理在每个轮中选择一对项目，并仅观察该对的相对二进制`的次数“反馈，从该圆的底层偏好矩阵中采样。我们首先研究对抗性偏好序列的静态后悔最小化问题，并使用$ O（\ SQRT {kt}）为高概率遗憾设计了高效的算法。我们接下来使用类似的算法思想，提出一种在非实践中的两种概念下的动态遗为最小化的高效且可透明的最佳算法。特别是，我们建立$ \ to（\ sqrt {skt}）$和$ \ to（{v_t ^ {1/3} k ^ {1/3} t ^ {2/3}}）$动态后悔保证，$ S $是基础偏好关系中的“有效交换机”的总数，以及$ V_T $的衡量标准的“连续变化”非公平性。尽管现实世界系统中的非静止环境实用性，但在这项工作之前尚未研究这些问题的复杂性。我们通过证明在上述非实践概念下的符合下限保证匹配的匹配的算法来证明我们的算法的最优性。最后，我们通过广泛的模拟来证实我们的结果，并比较我们算法在最先进的基线上的功效。

Transformer-based language models have been shown to be highly effective for several NLP tasks. In this paper, we consider three transformer models, BERT, RoBERTa, and XLNet, in both small and large version, and investigate how faithful their representations are with respect to the semantic content of texts. We formalize a notion of semantic faithfulness, in which the semantic content of a text should causally figure in a model's inferences in question answering. We then test this notion by observing a model's behavior on answering questions about a story after performing two novel semantic interventions -- deletion intervention and negation intervention. While transformer models achieve high performance on standard question answering tasks, we show that they fail to be semantically faithful once we perform these interventions for a significant number of cases (~50% for deletion intervention, and ~20% drop in accuracy for negation intervention). We then propose an intervention-based training regime that can mitigate the undesirable effects for deletion intervention by a significant margin (from ~50% to ~6%). We analyze the inner-workings of the models to better understand the effectiveness of intervention-based training for deletion intervention. But we show that this training does not attenuate other aspects of semantic unfaithfulness such as the models' inability to deal with negation intervention or to capture the predicate-argument structure of texts. We also test InstructGPT, via prompting, for its ability to handle the two interventions and to capture predicate-argument structure. While InstructGPT models do achieve very high performance on predicate-argument structure task, they fail to respond adequately to our deletion and negation interventions.

Prompting large language models has enabled significant recent progress in multi-step reasoning over text. However, when applied to text generation from semi-structured data (e.g., graphs or tables), these methods typically suffer from low semantic coverage, hallucination, and logical inconsistency. We propose MURMUR, a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning. MURMUR is a best-first search method that generates reasoning paths using: (1) neural and symbolic modules with specific linguistic and logical skills, (2) a grammar whose production rules define valid compositions of modules, and (3) value functions that assess the quality of each reasoning step. We conduct experiments on two diverse data-to-text generation tasks like WebNLG and LogicNLG. These tasks differ in their data representations (graphs and tables) and span multiple linguistic and logical skills. MURMUR obtains significant improvements over recent few-shot baselines like direct prompting and chain-of-thought prompting, while also achieving comparable performance to fine-tuned GPT-2 on out-of-domain data. Moreover, human evaluation shows that MURMUR generates highly faithful and correct reasoning paths that lead to 26% more logically consistent summaries on LogicNLG, compared to direct prompting.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Flooding is one of the most disastrous natural hazards, responsible for substantial economic losses. A predictive model for flood-induced financial damages is useful for many applications such as climate change adaptation planning and insurance underwriting. This research assesses the predictive capability of regressors constructed on the National Flood Insurance Program (NFIP) dataset using neural networks (Conditional Generative Adversarial Networks), decision trees (Extreme Gradient Boosting), and kernel-based regressors (Gaussian Process). The assessment highlights the most informative predictors for regression. The distribution for claims amount inference is modeled with a Burr distribution permitting the introduction of a bias correction scheme and increasing the regressor's predictive capability. Aiming to study the interaction with physical variables, we incorporate Daymet rainfall estimation to NFIP as an additional predictor. A study on the coastal counties in the eight US South-West states resulted in an $R^2=0.807$. Further analysis of 11 counties with a significant number of claims in the NFIP dataset reveals that Extreme Gradient Boosting provides the best results, that bias correction significantly improves the similarity with the reference distribution, and that the rainfall predictor strengthens the regressor performance.

Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. This article provides a broad overview of research on the explainability and interpretability of natural language processing and information retrieval methods. More specifically, we survey approaches that have been applied to explain word embeddings, sequence modeling, attention modules, transformers, BERT, and document ranking. The concluding section suggests some possible directions for future research on this topic.

The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for medical studies. As there is so much data accessible to doctors and patients alike, summarizing it has become increasingly necessary - this has been supported through the introduction of deep learning and transformer-based networks, which have boosted the sector significantly in recent years. This paper gives a comprehensive survey of the current techniques and trends in medical summarization

Modeling the risk of extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Although the available low-resolution climate models capture different scenarios, accurate risk assessment for mitigation and adaption often demands detail that they typically cannot resolve. Here, we develop a dynamic data-driven downscaling (super-resolution) method that incorporates physics and statistics in a generative framework to learn the fine-scale spatial details of rainfall. Our method transforms coarse-resolution ($0.25^{\circ} \times 0.25^{\circ}$) climate model outputs into high-resolution ($0.01^{\circ} \times 0.01^{\circ}$) rainfall fields while efficaciously quantifying uncertainty. Results indicate that the downscaled rainfall fields closely match observed spatial fields and their risk distributions.