了解强化学习(RL)代理的新兴行为可能很困难,因为这种代理通常使用高度复杂的决策程序在复杂的环境中进行训练。这引起了RL中解释性的多种方法,旨在调和可能在主体行为与观察者预期的行为之间产生的差异。最近的方法取决于域知识,这可能并非总是可用的,分析代理商的策略,或者是对基础环境的特定要素的分析,通常被建模为马尔可夫决策过程(MDP)。我们的主要主张是,即使基本的MDP尚不完全了解(例如,尚未准确地了解过渡概率),也没有由代理商维护(即,在使用无模型方法时),但仍可以利用它为自动生成解释。为此,我们建议使用以前在文献中使用的正式MDP抽象和转换来加快寻找最佳策略的搜索,以自动产生解释。由于这种转换通常基于环境的符号表示,因此它们可能代表了预期和实际代理行为之间差距的有意义的解释。我们正式定义了这个问题,建议一类可用于解释新兴行为的转换,并提出了有效搜索解释的方法。我们演示了一组标准基准测试的方法。
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我们考虑战略设置,其中几个用户在重复的在线互动中聘用,辅助最小化的代理商代表他们反复发挥“游戏”。我们研究了代理人的重复游戏的动态和平均结果,并将其视为诱导用户之间的元游戏。我们的主要焦点是用户可以在此元游戏中从“操纵”他们自己的代理商中可以受益于他们自己的代理商。我们正式定义了普通游戏的这种“用户代理元荟萃游戏”模型,讨论了自动化代理动态的不同概念下的属性,并分析了2x2游戏中用户的均衡,其中动态收敛到a单均衡。
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我们分析了一种方案,其中软件代理作为后悔最小化算法代表他们的用户参与重复拍卖。我们研究了第一个价格和第二次价格拍卖,以及他们的广义版本(例如,作为用于广告拍卖的版本)。利用理论分析和模拟,我们展示了,令人惊讶的是,在二次价格拍卖中,球员的激励措施将他们的真正估值释放到自己的学习代理,而在第一次价格拍卖中,这是所有球员如实的主要战略向他们的代理商报告他们的估值。
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Pretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised pretraining. Until now, massively multitask learning required simultaneous access to all datasets in the mixture and heavy compute resources that are only available to well-resourced teams. In this paper, we propose ColD Fusion, a method that provides the benefits of multitask learning but leverages distributed computation and requires limited communication and no sharing of data. Consequentially, ColD Fusion can create a synergistic loop, where finetuned models can be recycled to continually improve the pretrained model they are based on. We show that ColD Fusion yields comparable benefits to multitask pretraining by producing a model that (a) attains strong performance on all of the datasets it was multitask trained on and (b) is a better starting point for finetuning on unseen datasets. We find ColD Fusion outperforms RoBERTa and even previous multitask models. Specifically, when training and testing on 35 diverse datasets, ColD Fusion-based model outperforms RoBERTa by 2.45 points in average without any changes to the architecture.
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We introduce KiloGram, a resource for studying abstract visual reasoning in humans and machines. Drawing on the history of tangram puzzles as stimuli in cognitive science, we build a richly annotated dataset that, with >1k distinct stimuli, is orders of magnitude larger and more diverse than prior resources. It is both visually and linguistically richer, moving beyond whole shape descriptions to include segmentation maps and part labels. We use this resource to evaluate the abstract visual reasoning capacities of recent multi-modal models. We observe that pre-trained weights demonstrate limited abstract reasoning, which dramatically improves with fine-tuning. We also observe that explicitly describing parts aids abstract reasoning for both humans and models, especially when jointly encoding the linguistic and visual inputs. KiloGram is available at https://lil.nlp.cornell.edu/kilogram .
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Learned classifiers should often possess certain invariance properties meant to encourage fairness, robustness, or out-of-distribution generalization. However, multiple recent works empirically demonstrate that common invariance-inducing regularizers are ineffective in the over-parameterized regime, in which classifiers perfectly fit (i.e. interpolate) the training data. This suggests that the phenomenon of ``benign overfitting," in which models generalize well despite interpolating, might not favorably extend to settings in which robustness or fairness are desirable. In this work we provide a theoretical justification for these observations. We prove that -- even in the simplest of settings -- any interpolating learning rule (with arbitrarily small margin) will not satisfy these invariance properties. We then propose and analyze an algorithm that -- in the same setting -- successfully learns a non-interpolating classifier that is provably invariant. We validate our theoretical observations on simulated data and the Waterbirds dataset.
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We present lilGym, a new benchmark for language-conditioned reinforcement learning in visual environments. lilGym is based on 2,661 highly-compositional human-written natural language statements grounded in an interactive visual environment. We annotate all statements with executable Python programs representing their meaning to enable exact reward computation in every possible world state. Each statement is paired with multiple start states and reward functions to form thousands of distinct Markov Decision Processes of varying difficulty. We experiment with lilGym with different models and learning regimes. Our results and analysis show that while existing methods are able to achieve non-trivial performance, lilGym forms a challenging open problem. lilGym is available at https://lil.nlp.cornell.edu/lilgym/.
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Previous studies observed that finetuned models may be better base models than the vanilla pretrained model. Such a model, finetuned on some source dataset, may provide a better starting point for a new finetuning process on a desired target dataset. Here, we perform a systematic analysis of this intertraining scheme, over a wide range of English classification tasks. Surprisingly, our analysis suggests that the potential intertraining gain can be analyzed independently for the target dataset under consideration, and for a base model being considered as a starting point. This is in contrast to current perception that the alignment between the target dataset and the source dataset used to generate the base model is a major factor in determining intertraining success. We analyze different aspects that contribute to each. Furthermore, we leverage our analysis to propose a practical and efficient approach to determine if and how to select a base model in real-world settings. Last, we release an updating ranking of best models in the HuggingFace hub per architecture https://ibm.github.io/model-recycling/.
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我们介绍了FastCoref,这是一个用于快速,准确且易于使用的英语核心分辨率的Python软件包。该软件包是可以安装的,并且允许两种模式:基于LingMess体系结构的精确模式,提供最新的核心精度,以及基本更快的模型F-Coref,这是本工作的重点。\ Model {}允许在V100 GPU上25秒内处理2.8K Ontonotes文档(相比之下,LingMess模型为6分钟,而流行的AllennLP Coreference模型的12分钟仅适度精度下降。快速速度是通过将紧凑模型从Lingmess模型中蒸馏而成的,以及使用我们称为“剩余批处理”的技术的有效批处理实现。https://github.com/shon-otmazgin/fastcoref
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文本分类在许多真实世界的情况下可能很有用,为最终用户节省了很多时间。但是,构建自定义分类器通常需要编码技能和ML知识,这对许多潜在用户构成了重大障碍。为了提高此障碍,我们介绍了标签侦探,这是一种免费的开源系统,用于标记和创建文本分类器。该系统对于(a)是一个无代码系统是独一无二的分类器在几个小时内,(c)开发用于开发人员进行配置和扩展。通过开放采购标签侦探,我们希望建立一个用户和开发人员社区,以扩大NLP模型的利用率。
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