We introduce a new setting, optimize-and-estimate structured bandits. Here, a policy must select a batch of arms, each characterized by its own context, that would allow it to both maximize reward and maintain an accurate (ideally unbiased) population estimate of the reward. This setting is inherent to many public and private sector applications and often requires handling delayed feedback, small data, and distribution shifts. We demonstrate its importance on real data from the United States Internal Revenue Service (IRS). The IRS performs yearly audits of the tax base. Two of its most important objectives are to identify suspected misreporting and to estimate the "tax gap" -- the global difference between the amount paid and true amount owed. Based on a unique collaboration with the IRS, we cast these two processes as a unified optimize-and-estimate structured bandit. We analyze optimize-and-estimate approaches to the IRS problem and propose a novel mechanism for unbiased population estimation that achieves rewards comparable to baseline approaches. This approach has the potential to improve audit efficacy, while maintaining policy-relevant estimates of the tax gap. This has important social consequences given that the current tax gap is estimated at nearly half a trillion dollars. We suggest that this problem setting is fertile ground for further research and we highlight its interesting challenges. The results of this and related research are currently being incorporated into the continual improvement of the IRS audit selection methods.

We investigate the effects on authorship identification tasks of a fundamental shift in how to conceive the vectorial representations of documents that are given as input to a supervised learner. In ``classic'' authorship analysis a feature vector represents a document, the value of a feature represents (an increasing function of) the relative frequency of the feature in the document, and the class label represents the author of the document. We instead investigate the situation in which a feature vector represents an unordered pair of documents, the value of a feature represents the absolute difference in the relative frequencies (or increasing functions thereof) of the feature in the two documents, and the class label indicates whether the two documents are from the same author or not. This latter (learner-independent) type of representation has been occasionally used before, but has never been studied systematically. We argue that it is advantageous, and that in some cases (e.g., authorship verification) it provides a much larger quantity of information to the training process than the standard representation. The experiments that we carry out on several publicly available datasets (among which one that we here make available for the first time) show that feature vectors representing pairs of documents (that we here call Diff-Vectors) bring about systematic improvements in the effectiveness of authorship identification tasks, and especially so when training data are scarce (as it is often the case in real-life authorship identification scenarios). Our experiments tackle same-author verification, authorship verification, and closed-set authorship attribution; while DVs are naturally geared for solving the 1st, we also provide two novel methods for solving the 2nd and 3rd that use a solver for the 1st as a building block.

These superior performances are mostly achieved The goal of coreset selection in supervised learning via learning from huge amounts of data. However, this datadriven is to produce a weighted subset of data, so that paradigm also poses several new challenges: 1) the training only on the subset achieves similar performance cumbersome dataset becomes harder to store and transfer; as training on the entire dataset. Existing 2) for some real applications, such as continual learning, methods achieved promising results in resourceconstrained one can only access a small number of training data at each scenarios such as continual learning stage of training; 3) in some more extreme scenarios, where and streaming. However, most of the existing algorithms the training data is incorrectly labelled, or they are collected are limited to traditional machine learning from different domains, more training data may even hurt models. A few algorithms that can handle the model's performance. To address these issues, a natural large models adopt greedy search approaches due idea is to select a small subset (i.e., coreset) comprised of to the difficulty in solving the discrete subset selection most informative training samples, such that training on this problem, which is computationally costly subset can achieve comparable or even better performance when coreset becomes larger and often produces with that on the full dataset, which is verified in Appendix suboptimal results. In this work, for the first time D. Therefore, how to construct a good coreset for DNNs we propose a continuous probabilistic bilevel formulation now becomes a crucial problem. of coreset selection by learning a probablistic weight for each training sample. The overall We notice that, coreset selection has been investigated for objective is posed as a bilevel optimization the traditional machine learning models, e.g., SVM (Tsang problem, where 1) the inner loop samples coresets et al., 2005), logistic regression (Huggins et al., 2016) and and train the model to convergence and 2) Gaussian mixture model (Lucic et al., 2017), for a long the outer loop updates the sample probability progressively time to accelerate the training process and lots of effective according to the model's performance.

We explore a scheme that enables the training of a deep neural network in a Federated Learning configuration over an additive white Gaussian noise channel. The goal is to create a low complexity, linear compression strategy, called PolarAir, that reduces the size of the gradient at the user side to lower the number of channel uses needed to transmit it. The suggested approach belongs to the family of compressed sensing techniques, yet it constructs the sensing matrix and the recovery procedure using multiple access techniques. Simulations show that it can reduce the number of channel uses by ~30% when compared to conveying the gradient without compression. The main advantage of the proposed scheme over other schemes in the literature is its low time complexity. We also investigate the behavior of gradient updates and the performance of PolarAir throughout the training process to obtain insight on how best to construct this compression scheme based on compressed sensing.

Federated learning (FL) is a novel learning paradigm that addresses the privacy leakage challenge of centralized learning. However, in FL, users with non-independent and identically distributed (non-IID) characteristics can deteriorate the performance of the global model. Specifically, the global model suffers from the weight divergence challenge owing to non-IID data. To address the aforementioned challenge, we propose a novel diffusion strategy of the machine learning (ML) model (FedDif) to maximize the FL performance with non-IID data. In FedDif, users spread local models to neighboring users over D2D communications. FedDif enables the local model to experience different distributions before parameter aggregation. Furthermore, we theoretically demonstrate that FedDif can circumvent the weight divergence challenge. On the theoretical basis, we propose the communication-efficient diffusion strategy of the ML model, which can determine the trade-off between the learning performance and communication cost based on auction theory. The performance evaluation results show that FedDif improves the test accuracy of the global model by 10.37% compared to the baseline FL with non-IID settings. Moreover, FedDif improves the number of consumed sub-frames by 1.28 to 2.85 folds to the latest methods except for the model compression scheme. FedDif also improves the number of transmitted models by 1.43 to 2.67 folds to the latest methods.

The volume of scientific output is creating an urgent need for automated tools to help scientists keep up with developments in their field. Semantic Scholar (S2) is an open data platform and website aimed at accelerating science by helping scholars discover and understand scientific literature. We combine public and proprietary data sources using state-of-the-art techniques for scholarly PDF content extraction and automatic knowledge graph construction to build the Semantic Scholar Academic Graph, the largest open scientific literature graph to-date, with 200M+ papers, 80M+ authors, 550M+ paper-authorship edges, and 2.4B+ citation edges. The graph includes advanced semantic features such as structurally parsed text, natural language summaries, and vector embeddings. In this paper, we describe the components of the S2 data processing pipeline and the associated APIs offered by the platform. We will update this living document to reflect changes as we add new data offerings and improve existing services.

Machine learning is the dominant approach to artificial intelligence, through which computers learn from data and experience. In the framework of supervised learning, a necessity for a computer to learn from data accurately and efficiently is to be provided with auxiliary information about the data distribution and target function through the learning model. This notion of auxiliary information relates to the concept of regularization in statistical learning theory. A common feature among real-world datasets is that data domains are multiscale and target functions are well-behaved and smooth. This paper proposes an entropy-based learning model that exploits this data structure and discusses its statistical and computational benefits. The hierarchical learning model is inspired by human beings' logical and progressive easy-to-hard learning mechanism and has interpretable levels. The model apportions computational resources according to the complexity of data instances and target functions. This property can have multiple benefits, including higher inference speed and computational savings in training a model for many users or when training is interrupted. We provide a statistical analysis of the learning mechanism using multiscale entropies and show that it can yield significantly stronger guarantees than uniform convergence bounds.

The mathematics of least squares would have been so trivial for Gauss that even had he come upon the method he might have passed it over as but one of many, not noticing its true significance until Legendre's book prodded his memory and produced a post facto priority claim. There have been many extraordinary equations that changed the world (whether they were discovered or invented depends on whether you subscribe to mathematical Platonism--I do) but among the 17 equations that changed the world, the legendary Ordinary Least Squares (OLS) wasn't listed among them (though it is heavily related to both the Normal Distribution and Information Theory). It's a shame because the article and tweets referencing the "17 Equations" have been floating around for nearly ten years. So I will tell you about the magic of OLS, a little about its history, some of its extensions, and its applications (yes, to Fintech too). Subscribe for free to receive new posts and support my work.

Statistical Learning with Math and R: The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building R programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further into the following main chapters. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter.

Additionally, the chance is you won't be working with a dataset, so merging data is also a common operation you'll use. Extracting meaningful information from data becomes easier if you visualize it. In Python, there are many libraries you can use to visualize your data. You should use this stage to detect the outliers and correlated predictors. If undetected, they will decrease your machine-learning model performance.