Machine Learning From Weak Supervision

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Machine Learning from Weak Supervision

Author : Masashi Sugiyama
Publisher : MIT Press
ISBN 13 : 0262047071
Total Pages : 315 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Machine Learning from Weak Supervision by : Masashi Sugiyama

Download or read book Machine Learning from Weak Supervision written by Masashi Sugiyama and published by MIT Press. This book was released on 2022-08-23 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization. Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. In this book Masashi Sugiyama, Han Bao, Takashi Ishida, Nan Lu, Tomoya Sakai and Gang Niu present theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom. The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classification. It then explores problems of binary weakly supervised classification, including positive-unlabeled (PU) classification, positive-negative-unlabeled (PNU) classification, and unlabeled-unlabeled (UU) classification. It then turns to multiclass classification, discussing complementary-label (CL) classification and partial-label (PL) classification. Finally, the book addresses more advanced issues, including a family of correction methods to improve the generalization performance of weakly supervised learning and the problem of class-prior estimation.

Practical Weak Supervision

Author : Wee Hyong Tok
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492077038
Total Pages : 193 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Practical Weak Supervision by : Wee Hyong Tok

Download or read book Practical Weak Supervision written by Wee Hyong Tok and published by "O'Reilly Media, Inc.". This book was released on 2021-09-30 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most data scientists and engineers today rely on quality labeled data to train machine learning models. But building a training set manually is time-consuming and expensive, leaving many companies with unfinished ML projects. There's a more practical approach. In this book, Wee Hyong Tok, Amit Bahree, and Senja Filipi show you how to create products using weakly supervised learning models. You'll learn how to build natural language processing and computer vision projects using weakly labeled datasets from Snorkel, a spin-off from the Stanford AI Lab. Because so many companies have pursued ML projects that never go beyond their labs, this book also provides a guide on how to ship the deep learning models you build. Get up to speed on the field of weak supervision, including ways to use it as part of the data science process Use Snorkel AI for weak supervision and data programming Get code examples for using Snorkel to label text and image datasets Use a weakly labeled dataset for text and image classification Learn practical considerations for using Snorkel with large datasets and using Spark clusters to scale labeling

Inspecting the Machine Learning Pipeline

Author : Rafael Poyiadzi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)

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Book Synopsis Inspecting the Machine Learning Pipeline by : Rafael Poyiadzi

Download or read book Inspecting the Machine Learning Pipeline written by Rafael Poyiadzi and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Online Harassment

Author : Jennifer Golbeck
Publisher : Springer
ISBN 13 : 3319785834
Total Pages : 268 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Online Harassment by : Jennifer Golbeck

Download or read book Online Harassment written by Jennifer Golbeck and published by Springer. This book was released on 2018-07-20 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online Harassment is one of the most serious problems in social media. To address it requires understanding the forms harassment takes, how it impacts the targets, who harasses, and how technology that stands between users and social media can stop harassers and protect users. The field of Human-Computer Interaction provides a unique set of tools to address this challenge. This book brings together experts in theory, socio-technical systems, network analysis, text analysis, and machine learning to present a broad set of analyses and applications that improve our understanding of the harassment problem and how to address it. This book tackles the problem of harassment by addressing it in three major domains. First, chapters explore how harassment manifests, including extensive analysis of the Gamer Gate incident, stylistic features of different types of harassment, how gender differences affect misogynistic harassment. Then, we look at the results of harassment, including how it drives people offline and the impacts it has on targets. Finally, we address techniques for mitigating harassment, both through automated detection and filtering and interface options that users control. Together, many branches of HCI come together to provide a comprehensive look at the phenomenon of online harassment and to advance the field toward effective human-oriented solutions.

Weak Supervision from High-level Abstractions

Author : Braden Jay Hancock
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (111 download)

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Book Synopsis Weak Supervision from High-level Abstractions by : Braden Jay Hancock

Download or read book Weak Supervision from High-level Abstractions written by Braden Jay Hancock and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The interfaces for interacting with machine learning models are changing. Consider, for example, that while computers run on 1s and 0s, that is no longer the level of abstraction we use to program most computers. Instead, we use higher-level abstractions such as assembly language, high-level languages, or declarative languages to more efficiently convert our objectives into code. Similarly, most machine learning models are trained with "1s and 0s" (individually labeled examples), but we need not limit ourselves to interacting with them at this low level. Instead, we can use higher-level abstractions to more efficiently convert our domain knowledge into the inputs our models require. In this work, we show that weak supervision from high-level abstractions can be used to train high-performance machine learning models. At three different levels of abstraction, we describe the system we built to enable such interaction. We begin with Snorkel, which elevates label generation from a manual process to a programmatic one. With this system, domain experts encode their knowledge in potentially noisy and correlated black-box functions called labeling functions. These functions can then be automatically denoised and applied to unlabeled data to create large training sets quickly. Next, with Fonduer we enable an abstraction one step higher where advanced primitives defined over multiple modalities (visual, textual, structural, and tabular) allow users to programmatically supervise over richly formatted data (e.g., PDFs with tables and formatting). Finally, in BabbleLabble we show that we can even utilize supervision given in the form of natural language explanations, maintaining the benefits of programmatic supervision while removing the burden of writing code. For all of these systems, we demonstrate their effectiveness with empirical results and present real-world use cases where they have enabled rapid development of machine learning applications, including in bio-medicine, commerce, and defense.

Strengthening Weak Supervision for Information Retrieval

Author : Dany M. Haddad
Publisher :
ISBN 13 :
Total Pages : 104 pages
Book Rating : 4.:/5 (112 download)

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Book Synopsis Strengthening Weak Supervision for Information Retrieval by : Dany M. Haddad

Download or read book Strengthening Weak Supervision for Information Retrieval written by Dany M. Haddad and published by . This book was released on 2019 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: The limited availability of ground truth relevance labels has been a major impediment to the application of supervised machine learning techniques to ad-hoc document retrieval and ranking. As a result, unsupervised scoring methods, such as BM25 and TF-IDF, remain strong competitors to deep learning approaches whose counterparts have brought on dramatic improvements in other domains, such as computer vision and natural language processing. However, recent works have shown that it is possible to take advantage of the performance of unsupervised methods to generate training data necessary for learning-to-rank models. Surprisingly, machine learning models trained on this generated data can outperform the original unsupervised method. The key limitation to this line of work is the size of the training set required to surpass the performance of the original unsupervised method, which can be as large as 1013 training examples. Building on these insights, this work proposes two methods to reduce the amount of training data required. The first method takes inspiration from crowdsourcing, and leverages multiple unsupervised rankers to generate soft, or noise-aware, training labels. The second identifies harmful, or mislabeled, training examples and removes them from the training set. We show that our methods allow us to surpass the performance of the unsupervised baseline with far fewer training examples than previous works

Weakly Supervised Learning Via Statistical Sufficiency

Author : Giorgio Patrini
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (144 download)

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Book Synopsis Weakly Supervised Learning Via Statistical Sufficiency by : Giorgio Patrini

Download or read book Weakly Supervised Learning Via Statistical Sufficiency written by Giorgio Patrini and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Thesis introduces a novel algorithmic framework for weakly supervised learning, namely, for any any problem in between supervised and unsupervised learning, from the labels standpoint. Weak supervision is the reality in many applications of machine learning where training is performed with partially missing, aggregated- level and/or noisy labels. The approach is grounded on the concept of statistical suf- ficiency and its transposition to loss functions. Our solution is problem-agnostic yet constructive as it boils down to a simple two-steps procedure. First, estimate a sufficient statistic for the labels from weak supervision. Second, plug the estimate into a (newly defined) linear-odd loss function and learn the model by any gradient-based solver, with a simple adaptation. We apply the same approach to several challenging learning problems: (i) learning from label proportions, (ii) learning with noisy labels for both linear classifiers and deep neural networks, and (iii) learning from feature-wise distributed datasets where the entity matching function is unknown.

Use of Machine Learning and Weak Supervision to Predict Stocks from Unlabeled Press Releases

Author : Joel Miller
Publisher : Independent Author
ISBN 13 : 9781805254294
Total Pages : 0 pages
Book Rating : 4.2/5 (542 download)

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Book Synopsis Use of Machine Learning and Weak Supervision to Predict Stocks from Unlabeled Press Releases by : Joel Miller

Download or read book Use of Machine Learning and Weak Supervision to Predict Stocks from Unlabeled Press Releases written by Joel Miller and published by Independent Author. This book was released on 2023-04-04 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis examines the eﬀect of press releases on the Nordic stock market. A weak supervision approach is utilized to estimate the short-term eﬀect on stock re-turns given press releases of diﬀerent categories. By utilizing the data programming framework as implemented in the Snorkel library, approximately 24% of all press releases are categorized into a set of 10 distinct categories. Further, a collection of machine learning models for stock price prediction is developed, where simulation is conducted to determine how press releases may be used to forecast stock price movement. Stock price prediction is performed for large stock price movements and for stock price direction, where the result shows that the best performing model achieves a 53% F1-score and 54% accuracy respectively for the tasks. Finally, it appears that the labeled press releases can be used to increase the predictability of stock movements in the Nordic stock market.

Machine Learning and Data Science Blueprints for Finance

Author : Hariom Tatsat
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492073008
Total Pages : 432 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Machine Learning and Data Science Blueprints for Finance by : Hariom Tatsat

Download or read book Machine Learning and Data Science Blueprints for Finance written by Hariom Tatsat and published by "O'Reilly Media, Inc.". This book was released on 2020-10-01 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Introduction to Semi-Supervised Learning

Author : Xiaojin Geffner
Publisher : Springer Nature
ISBN 13 : 3031015487
Total Pages : 116 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Introduction to Semi-Supervised Learning by : Xiaojin Geffner

Download or read book Introduction to Semi-Supervised Learning written by Xiaojin Geffner and published by Springer Nature. This book was released on 2022-05-31 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical Machine Learning / Overview of Semi-Supervised Learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised Learning / Semi-Supervised Support Vector Machines / Human Semi-Supervised Learning / Theory and Outlook

Machine Learning for Data Streams

Author : Albert Bifet
Publisher : MIT Press
ISBN 13 : 0262346052
Total Pages : 255 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Machine Learning for Data Streams by : Albert Bifet

Download or read book Machine Learning for Data Streams written by Albert Bifet and published by MIT Press. This book was released on 2018-03-16 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework. Today many information sources—including sensor networks, financial markets, social networks, and healthcare monitoring—are so-called data streams, arriving sequentially and at high speed. Analysis must take place in real time, with partial data and without the capacity to store the entire data set. This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations. The book first offers a brief introduction to the topic, covering big data mining, basic methodologies for mining data streams, and a simple example of MOA. More detailed discussions follow, with chapters on sketching techniques, change, classification, ensemble methods, regression, clustering, and frequent pattern mining. Most of these chapters include exercises, an MOA-based lab session, or both. Finally, the book discusses the MOA software, covering the MOA graphical user interface, the command line, use of its API, and the development of new methods within MOA. The book will be an essential reference for readers who want to use data stream mining as a tool, researchers in innovation or data stream mining, and programmers who want to create new algorithms for MOA.

Semi-Supervised Learning

Author : Olivier Chapelle
Publisher : MIT Press
ISBN 13 : 0262514125
Total Pages : 525 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Semi-Supervised Learning by : Olivier Chapelle

Download or read book Semi-Supervised Learning written by Olivier Chapelle and published by MIT Press. This book was released on 2010-01-22 with total page 525 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

From Weakly Supervised Learning to Active Labeling

Author : Vivien Cabannes
Publisher : Independently Published
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.8/5 (314 download)

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Book Synopsis From Weakly Supervised Learning to Active Labeling by : Vivien Cabannes

Download or read book From Weakly Supervised Learning to Active Labeling written by Vivien Cabannes and published by Independently Published. This book was released on 2022-05-24 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied maths and machine computations have raised a lot of hope since the recent success of supervised learning. Many practitioners in industries have been trying to switch from their old paradigms to machine learning. Interestingly, those data scientists spend more time scrapping, annotating and cleaning data than fine-tuning models. This PhD thesis is motivated by the following question: can we derive a more generic framework than the one of supervised learning in order to learn from clutter data? This question is approached through the lens of weakly supervised learning, assuming that the bottleneck of data collection lies in annotation. We model weak supervision as giving, rather than a unique target, a set of target candidates. We argue that one should look for an "optimistic" function that matches most of the observations. This allows us to derive a principle to disambiguate partial labels. We also discuss the advantage to incorporate unsupervised learning techniques into our framework, in particular manifold regularization approached through diffusion techniques, for which we derived a new algorithm that scales better with input dimension then the baseline method. Finally, we switch from passive to active weakly supervised learning, introducing the "active labeling" framework, in which a practitioner can query weak information about chosen data. Among others, we leverage the fact that one does not need full information to access stochastic gradients and perform stochastic gradient descent.

Fundamentals and Methods of Machine and Deep Learning

Author : Pradeep Singh
Publisher : John Wiley & Sons
ISBN 13 : 1119821886
Total Pages : 480 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Fundamentals and Methods of Machine and Deep Learning by : Pradeep Singh

Download or read book Fundamentals and Methods of Machine and Deep Learning written by Pradeep Singh and published by John Wiley & Sons. This book was released on 2022-02-01 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Semantic Systems. The Power of AI and Knowledge Graphs

Author : Maribel Acosta
Publisher : Springer Nature
ISBN 13 : 3030332209
Total Pages : 400 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Semantic Systems. The Power of AI and Knowledge Graphs by : Maribel Acosta

Download or read book Semantic Systems. The Power of AI and Knowledge Graphs written by Maribel Acosta and published by Springer Nature. This book was released on 2019-11-04 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies.

Training Discriminative Computer Vision Models with Weak Supervision

Author : Boris Babenko
Publisher :
ISBN 13 : 9781267250643
Total Pages : 109 pages
Book Rating : 4.2/5 (56 download)

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Book Synopsis Training Discriminative Computer Vision Models with Weak Supervision by : Boris Babenko

Download or read book Training Discriminative Computer Vision Models with Weak Supervision written by Boris Babenko and published by . This book was released on 2012 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical machine learning techniques have transformed computer vision research in the last two decades, and have led to many breakthroughs in object detection, recognition and tracking. Such data-driven methods extrapolate rules from a set of labeled examples, freeing us from designing and tuning a system by hand for a particular application or domain. Discriminative learning methods, which directly learn to differentiate categories of data rather than modeling the data itself, have been shown to be particularly effective. However, the requirement of a large set of labeled examples becomes prohibitively expensive, especially if we consider scaling to a wide range of domains and applications. In this dissertation we explore weakly supervised methods of training discriminative models for a number of computer vision applications. These methods require weaker forms of annotation that are easier and/or cheaper to obtain, and can learn in situations where the ground truth is inherently ambiguous. Many of the algorithms in this dissertation are based on a particular form of weakly supervised learning called Multiple Instance Learning (MIL). Our final contribution is a theoretical analysis of MIL that takes into account the characteristics of applications in computer vision and related areas.

Interpretable and Annotation-Efficient Learning for Medical Image Computing

Author : Jaime Cardoso
Publisher : Springer Nature
ISBN 13 : 3030611663
Total Pages : 292 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Interpretable and Annotation-Efficient Learning for Medical Image Computing by : Jaime Cardoso

Download or read book Interpretable and Annotation-Efficient Learning for Medical Image Computing written by Jaime Cardoso and published by Springer Nature. This book was released on 2020-10-03 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing.