1,720,972 research outputs found
Refining the sample complexity of comparative learning
No full textThe PAC (Probably Approximately Correct) framework is a well-established theoretical framework for analyzing the statistical (and sometimes computational) complexity of machine learning tasks. Comparative learning is a recently introduced variation of the PAC framework that interpolates between the two standard extreme settings of realizable and agnostic PAC learning. In comparative learning the labeling is assumed to be from one hypothesis class (the source) while the learner's performance is to be measured against another hypothesis class (the benchmark). This setup allows for incorporating more specific prior knowledge into PAC-type learning bounds, which are known to be otherwise overly pessimistic. In this work we study the sample complexity of a variation of this setting we call proper comparative learning where we require the learning algorithm to output a hypothesis from the benchmark class. This setting represents model distillation tasks, where a predictor with specific requirements (e.g., interpretability) is trained on the labels from another model
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Novel Examination of Interpretable Surrogates and Adversarial Robustness in Machine Learning
Get PDFThe lack of transparent output behavior is a significant source of mistrust in many of the currently most successful machine learning tools. Concern arises particularly in situations where the data generation changes, for example under marginal shift or under adversarial manipulations. We analyze the use of decision trees (a human interpretable model) for indicating marginal shift. We then investigate the role of the data generation for the validity of the interpretable surrogate and its implementation as both local and global interpretation methods.
We often observed that the decision boundaries of the blackbox model was mostly sitting close to the original data manifold. This makes those regions vulnerable to imperceptible perturbations. Hence, we carefully argue that adversarial robustness should be defined as a locally adaptive measure complying with the underlying distribution. We then suggest a definition for an adaptive robust loss, an empirical version of it and a resulting data-augmentation framework
Precision Recall Cover: A Method to Assess Generative Models
No full textGenerative modelling has seen enormous practical advances over the past few years from LLMs like ChatGPT to image generation. However, evaluating the quality of a generative system is often still based on subjective human inspection. To overcome this, very recently, the research community has turned to exploring formal evaluation metrics and methods. In this work, we propose a novel evaluation method based on a two-way nearest neighbor test. We define a new measure of mutual coverage for two probability distributions. From this, we derive an empirical analogue and show analytically that it exhibits favorable theoretical properties while it is also straightforward to compute. We show that, while algorithmically simple, our derived method is also statistically sound. We complement our analysis with a systematic experimental evaluation and comparison to other recently proposed measures. Using a wide array of experiments, we demonstrate our algorithm’s strengths over other existing methods and confirm our results from the theoretical analysis
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Investigating Calibrated Classification Scores through the Lens of Interpretability
No full textCalibration is a frequently invoked concept when useful label probability estimates are required on top of classification accuracy. A calibrated model is a scoring function whose scores correctly reflect underlying label probabilities. Calibration in itself however does not imply classification accuracy, nor human interpretable estimates, nor is it straightforward to verify calibration from finite data. There is a plethora of evaluation metrics (and loss functions) that each assesses a specific aspect of a calibration model. In this work, we initiate an axiomatic study of the notion of calibration and evaluation measures for calibration. We catalogue desirable properties of calibration models as well as evaluation metrics and analyze their feasibility and correspondences. We complement this analysis with an empirical evaluation, comparing two metrics and comparing common calibration methods to employing a simple, interpretable decision tree
An Axiomatic Perspective on Anomaly Detection
No full textA major challenge for both theoretical treatment and practical application of unsupervised learning tasks, such as clustering, anomaly detection or generative modeling, is the inherent lack of quantifiable objectives. Choosing methods and evaluating outcomes is then often a matter of ad-hoc heuristics or personal taste. Anomaly detection is often employed as a preprocessing step to other learning tasks, and unsound decisions for this task may thus have far-reaching consequences. In this work, we propose an axiomatic framework for analyzing behaviours of anomaly detection methods. We propose a basic set of desirable properties (or axioms) for distance-based anomaly detection methods and identify dependencies and (in-)consistencies between subsets of these. In addition, we include empirical results, which demonstrate the benefits of this axiomatic perspective on behaviours of anomaly detection methods. Our experiments illustrate how some commonly employed algorithms violate, perhaps unexpectedly, a basic desirable property. Namely, we highlight a material problem with a commonly used method called Isolation Forest, related to infinite bands of space likely to be labelled as inliers that extend infinitely far away from the training data. Additionally, we experimentally demonstrate that another common method, Local Outlier Factor, is vulnerable to adversarial data poisoning. To conduct these experimental evaluations, a tool for dataset generation, experimentation and visualization was built, which is an additional contribution of this work
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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