高维模型通常具有较大的内存足迹，必须在训练后进行量化，然后将其部署在资源受限的边缘设备上以进行推理任务。在这项工作中，我们开发了一个信息理论框架，用于量化从训练数据$（\ mathbf {x}，\ mathbf {y}）$的线性回归剂的问题，用于某些基本统计关系$ \ mathbf {y} = \ Mathbf {X} \ BoldSymbol {\ Theta} + \ Mathbf {V} $。博学的模型是对潜在参数$ \ boldsymbol {\ theta} \ in \ mathbb {r}^d $的估计值，仅使用$ bd $ bits来代表，其中$ b \ in（0，in 0，0，in（0） \ infty）$是预先指定的预算，$ d $是维度。在此设置下，我们为Minimax风险提供了信息理论的下限，并建议使用基于嵌入的算法进行匹配的上限，该算法紧密到恒定因素。上限和上限共同表征了达到与未量化设置相当的性能风险所需的最小阈值位预算。我们还提出了在计算上有效且最佳的随机hadamard嵌入到下限的轻度对数因子。我们的模型量化策略可以概括，我们通过将方法和上限扩展到两层relu神经网络以进行非线性回归来显示其功效。数值模拟表明，我们提出的方案的性能得到改善，以及其与下限的亲密关系。

当人类彼此合作时，他们经常通过观察他人来做出决定，并考虑到他们的行为可能在整个团队中的后果，而不是贪婪地做到最好的事情。我们希望我们的AI代理商通过捕获其合作伙伴的模型来有效地以类似的方式协作。在这项工作中，我们提出并分析了分散的多武装强盗（MAB）问题，耦合奖励作为更一般的多代理协作的抽象。我们展示了当申请分散的强盗团队时单代理最佳MAB算法的NA \“IVE扩展失败。相反，我们提出了一个合作伙伴感知策略，用于联合连续决策，这些策略扩展了众所周知的单王子的上置信度算法。我们分析表明，我们的拟议战略达到了对数遗憾，并提供了涉及人类AI和人机协作的广泛实验，以验证我们的理论发现。我们的结果表明，拟议的合作伙伴感知策略优于其他已知方法，以及我们的人类主题研究表明人类宁愿与实施我们合作伙伴感知战略的AI代理商合作。

我们在限制下研究了一阶优化算法，即使用每个维度的$ r $ bits预算进行量化下降方向，其中$ r \ in（0，\ infty）$。我们提出了具有收敛速率的计算有效优化算法，与信息理论性能匹配：（i）：（i）具有访问精确梯度甲骨文的平稳且强烈的符合目标，以及（ii）一般凸面和非平滑目标访问嘈杂的亚级别甲骨文。这些算法的关键是一种多项式复杂源编码方案，它在量化它之前将矢量嵌入随机子空间中。这些嵌入使得具有很高的概率，它们沿着转换空间的任何规范方向的投影很小。结果，量化这些嵌入，然后对原始空间进行逆变换产生一种源编码方法，具有最佳的覆盖效率，同时仅利用每个维度的$ r $ bits。我们的算法保证了位预算$ r $的任意值的最佳性，其中包括次线性预算制度（$ r <1 $），以及高预算制度（$ r \ geq 1 $），虽然需要$ o \ left（n^2 \右）$乘法，其中$ n $是尺寸。我们还提出了使用Hadamard子空间对这种编码方案的有效放松扩展以显着提高梯度稀疏方案的性能。数值模拟验证我们的理论主张。我们的实现可在https://github.com/rajarshisaha95/distoptconstrocncomm上获得。

The usage of technologically advanced devices has seen a boom in many domains, including education, automation, and healthcare; with most of the services requiring Internet connectivity. To secure a network, device identification plays key role. In this paper, a device fingerprinting (DFP) model, which is able to distinguish between Internet of Things (IoT) and non-IoT devices, as well as uniquely identify individual devices, has been proposed. Four statistical features have been extracted from the consecutive five device-originated packets, to generate individual device fingerprints. The method has been evaluated using the Random Forest (RF) classifier and different datasets. Experimental results have shown that the proposed method achieves up to 99.8% accuracy in distinguishing between IoT and non-IoT devices and over 97.6% in classifying individual devices. These signify that the proposed method is useful in assisting operators in making their networks more secure and robust to security breaches and unauthorized access.

We consider the problem of constructing minimax rate-optimal estimators for a doubly robust nonparametric functional that has witnessed applications across the causal inference and conditional independence testing literature. Minimax rate-optimal estimators for such functionals are typically constructed through higher-order bias corrections of plug-in and one-step type estimators and, in turn, depend on estimators of nuisance functions. In this paper, we consider a parallel question of interest regarding the optimality and/or sub-optimality of plug-in and one-step bias-corrected estimators for the specific doubly robust functional of interest. Specifically, we verify that by using undersmoothing and sample splitting techniques when constructing nuisance function estimators, one can achieve minimax rates of convergence in all H\"older smoothness classes of the nuisance functions (i.e. the propensity score and outcome regression) provided that the marginal density of the covariates is sufficiently regular. Additionally, by demonstrating suitable lower bounds on these classes of estimators, we demonstrate the necessity to undersmooth the nuisance function estimators to obtain minimax optimal rates of convergence.

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.

Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs' strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation methods on PopQA, our new open-domain QA dataset with 14k questions. We find that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those findings, we devise a simple, yet effective, method for powerful and efficient retrieval-augmented LMs, which retrieves non-parametric memories only when necessary. Experimental results show that this significantly improves models' performance while reducing the inference costs.

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.