Domain Adaptation for Time Series Under Feature and Label Shifts

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across domains. This leads to feature shifts in the time and frequency representations. Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain. Effectively transferring complex time series models remains a formidable problem. We present Raincoat, the first model for both closed-set and universal domain adaptation on complex time series. Raincoat addresses feature and label shifts by considering both temporal and frequency features, aligning them across domains, and correcting for misalignments to facilitate the detection of private labels. Additionally, Raincoat improves transferability by identifying label shifts in target domains. Our experiments with 5 datasets and 13 state-of-the-art UDA methods demonstrate that Raincoat can improve transfer learning performance by up to 16.33% and can handle both closed-set and universal domain adaptation.Comment: Accepted by ICML 2023; 29 pages (14 pages main paper + 15 pages supplementary materials). Code: see https://github.com/mims-harvard/Raincoa

Generalizable deep learning based medical image segmentation

Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications. To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques. In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain. For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios. In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation. In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method. Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces

Bayesian nonparametric models for data exploration

Mención Internacional en el título de doctorMaking sense out of data is one of the biggest challenges of our time. With the emergence of technologies such as the Internet, sensor networks or deep genome sequencing, a true data explosion has been unleashed that affects all fields of science and our everyday life. Recent breakthroughs, such as self-driven cars or champion-level Go player programs, have demonstrated the potential benefits from exploiting data, mostly in well-defined supervised tasks. However, we have barely started to actually explore and truly understand data. In fact, data holds valuable information for answering most important questions for humanity: How does aging impact our physical capabilities? What are the underlying mechanisms of cancer? Which factors make countries wealthier than others? Most of these questions cannot be stated as well-defined supervised problems, and might benefit enormously from multidisciplinary research efforts involving easy-to-interpret models and rigorous data exploratory analyses. Efficient data exploration might lead to life-changing scientific discoveries, which can later be turned into a more impactful exploitation phase, to put forward more informed policy recommendations, decision-making systems, medical protocols or improved models for highly accurate predictions. This thesis proposes tailored Bayesian nonparametric (BNP) models to solve specific data exploratory tasks across different scientific areas including sport sciences, cancer research, and economics. We resort to BNP approaches to facilitate the discovery of unexpected hidden patterns within data. BNP models place a prior distribution over an infinite-dimensional parameter space, which makes them particularly useful in probabilistic models where the number of hidden parameters is unknown a priori. Under this prior distribution, the posterior distribution of the hidden parameters given the data will assign high probability mass to those configurations that best explain the observations. Hence, inference over the hidden variables can be performed using standard Bayesian inference techniques, therefore avoiding expensive model selection steps. This thesis is application-focused and highly multidisciplinary. More precisely, we propose an automatic grading system for sportive competitions to compare athletic performance regardless of age, gender and environmental aspects; we develop BNP models to perform genetic association and biomarker discovery in cancer research, either using genetic information and Electronic Health Records or clinical trial data; finally, we present a flexible infinite latent factor model of international trade data to understand the underlying economic structure of countries and their evolution over time.Uno de los principales desafíos de nuestro tiempo es encontrar sentido dentro de los datos. Con la aparición de tecnologías como Internet, redes de sensores, o métodos de secuenciación profunda del genoma, una verdadera explosión digital se ha visto desencadenada, afectando todos los campos científicos, así como nuestra vida diaria. Logros recientes como pueden ser los coches auto-dirigidos o programas que ganan a los seres humanos al milenario juego del Go, han demostrado con creces los posibles beneficios que podemos obtener de la explotación de datos, mayoritariamente en tareas supervisadas bien definidas. No obstante, apenas hemos empezado con la exploración de datos y su verdadero entendimiento. En verdad, los datos encierran información muy valiosa para responder a muchas de las preguntas más importantes para la humanidad: ¿Cómo afecta el envejecimiento a nuestras aptitudes físicas? ¿Cuáles son los mecanismos subyacentes del cáncer? ¿Qué factores explican la riqueza de ciertos países frente a otros? Si bien la mayoría de estas preguntas no pueden formularse como problemas supervisados bien definidos, éstas pueden ser abordadas mediante esfuerzos de investigación multidisciplinar que involucren modelos fáciles de interpretar y análisis exploratorios rigurosos. Explorar los datos de manera eficiente abre potencialmente la puerta a un sinnúmero de descubrimientos científicos en diversas áreas con impacto real en nuestras vidas, descubrimientos que a su vez pueden llevarnos a una mejor explotación de los datos, resultando en recomendaciones políticas adecuadas, sistemas precisos de toma de decisión, protocolos médicos optimizados o modelos con mejores capacidades predictivas. Esta tesis propone modelos Bayesianos no-paramétricos (BNP) adecuados para la resolución específica de tareas explorativas de los datos en diversos ámbitos científicos incluyendo ciencias del deporte, investigación contra el cáncer, o economía. Recurrimos a un planteamiento BNP para facilitar el descubrimiento de patrones ocultos inesperados subyacentes en los datos. Los modelos BNP definen una distribución a priori sobre un espacio de parámetros de dimensión infinita, lo cual los hace especialmente atractivos para enfoques probabilísticos donde el número de parámetros latentes es en principio desconocido. Bajo dicha distribución a priori, la distribución a posteriori de los parámetros ocultos dados los datos asignará mayor probabilidad a aquellas configuraciones que mejor explican las observaciones. De esta manera, la inferencia sobre el espacio de variables ocultas puede realizarse mediante técnicas estándar de inferencia Bayesiana, evitando el proceso de selección de modelos. Esta tesis se centra en el ámbito de las aplicaciones, y es de naturaleza multidisciplinar. En concreto, proponemos un sistema de gradación automática para comparar el rendimiento deportivo de atletas independientemente de su edad o género, así como de otros factores del entorno. Desarrollamos modelos BNP para descubrir asociaciones genéticas y biomarcadores dentro de la investigación contra el cáncer, ya sea contrastando información genética con la historia clínica electrónica de los pacientes, o utilizando datos de ensayos clínicos; finalmente, presentamos un modelo flexible de factores latentes infinito para datos de comercio internacional, con el objetivo de entender la estructura económica de los distintos países y su correspondiente evolución a lo largo del tiempo.Programa Oficial de Doctorado en Multimedia y ComunicacionesPresidente: Joaquín Míguez Arenas.- Secretario: Daniel Hernández Lobato.- Vocal: Cédric Archambea

Out-of-Distribution Generalization of Gigapixel Image Representation

This thesis addresses the significant challenge of improving the generalization capabilities of artificial deep neural networks in the classification of whole slide images (WSIs) in histopathology across different and unseen hospitals. It is a critical issue in AI applications for vision-based healthcare tasks, given that current standard methodologies struggle with out-of-distribution (OOD) data from varying hospital sources. In histopathology, distribution shifts can arise due to image acquisition variances across different scanner vendors, differences in laboratory routines and staining procedures, and diversity in patient demographics. This work investigates two critical forms of generalization within histopathology: magnification generalization and OOD generalization towards different hospitals. One chapter of this thesis is dedicated to the exploration of magnification generalization, acknowledging the variability in histopathological images due to distinct magnification levels and seeking to enhance the model's robustness by learning invariant features across these levels. However, the major part of this work focuses on OOD generalization, specifically unseen hospital data. The objective is to leverage knowledge encapsulated in pre-existing models to help new models adapt to diverse data scenarios and ensure their efficient operation in different hospital environments. Additionally, the concept of Hospital-Agnostic (HA) learning regimes is introduced, focusing on invariant characteristics across hospitals and aiming to establish a learning model that sustains stable performance in varied hospital settings. The culmination of this research introduces a comprehensive method, termed ALFA (Exploiting All Levels of Feature Abstraction), that not only considers invariant features across hospitals but also extracts a broader set of features from input images, thus maximizing the model's generalization potential. The findings of this research are expected to have significant implications for the deployment of medical image classification systems using deep models in clinical settings. The proposed methods allow for more accurate and reliable diagnostic support across various hospital environments, thereby improving diagnostic accuracy and reliability, and paving the way for enhanced generalization in histopathology diagnostics using deep learning techniques. Future research directions may build on expanding these investigations to further improve generalization in histopathology

Bridging the Gap: Towards an Expanded Toolkit for ML-Supported Decision-Making in the Public Sector

Machine Learning (ML) systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health. While these systems offer great potential benefits to institutional decision-making processes, such as improved efficiency and reliability, they still face the challenge of aligning intricate and nuanced policy objectives with the precise formalization requirements necessitated by ML models. In this paper, we aim to bridge the gap between ML and public sector decision-making by presenting a comprehensive overview of key technical challenges where disjunctions between policy goals and ML models commonly arise. We concentrate on pivotal points of the ML pipeline that connect the model to its operational environment, delving into the significance of representative training data and highlighting the importance of a model setup that facilitates effective decision-making. Additionally, we link these challenges with emerging methodological advancements, encompassing causal ML, domain adaptation, uncertainty quantification, and multi-objective optimization, illustrating the path forward for harmonizing ML and public sector objectives

Prediction of poor health in small ruminants and companion animals with accelerometers and machine learning

Global warming is one of the biggest challenge of our times, and significant efforts are being undertaken by academics, industries and other actors to tackle the problem. In the agricultural field precision farming is part of the solution to environmental sustainability and has been researched increasingly in recent years. Indeed, it has the potential to effectively increase livestock yield and decrease production carbon footprint while maintaining welfare. The thesis begins with a review of developments in automated animal monitoring and then moves on to a case study of a health monitoring system for small-ruminant in South Africa. As a demonstration and validation of the potential use case of the system, the method we propose is then applied to another study which aims to study feline health. Lower and Middle Income countries will be strongly affected by the changing climate and its impacts. We devise our method based on two South African small scale sheep and goat farms where assessment of the health status of individual animals is a key step in the timely and targeted treatment of infections, which is critical in the fight against anthelmintic and antimicrobial resistance. The FAMACHA scoring system has been used successfully to detect anaemia caused by infection with the parasitic nematode Haemonchus contortus in small ruminants and is an effective way to identify individuals in need of treatment. However, assessing FAMACHA is labour-intensive and costly as individuals must be manually examined at frequent intervals. Here, we used accelerometers to measure the individual activity of extensively grazed small ruminants exposed to natural Haemonchus contortus worm infection in southern Africa over long time scales (13+ months). When combined with machine learning for missing data imputation and classification, we find that this activity data can predict poorer health as well as those individuals that respond to treatment, with precision up to 80%. We demonstrate that these classifiers remain robust over time. Interpretation of trained classifiers reveals that poorer health can be predicted mainly by the night-time activity levels in the sheep. Our study reveals behavioural patterns across two small ruminant species, which low-cost biologgers can exploit to detect subtle changes in animal health and enable timely and targeted intervention. This has real potential to improve economic outcomes and animal welfare as well as limit the use of anthelmintic drugs and diminish pressures on anthelmintic resistance in both commercial and resource-poor communal farming. The validation of the proposed techniques with a different study group will be discussed in the latter part of the thesis. We used the accelerometry data of indoor cats equipped with wearable accelerometers in conjunction with their health status to detect signs of degenerative joint disease, and adapted our machine-learning pipeline to analyse bursts of high activity in the cats. We were able to classify high-activity events with precision up to 70% despite the relatively small dataset adding further evidence to the viability of animal health monitoring with accelerometers

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Parenting

Parenting is a lifelong task and parents are always remaining as a parent. Parenting in the present situation faces many challenges which need to be reviewed and understood with profuse equanimity and alacrity. This book tries to consider the process of parenting in different conditions such as during COVID-19 lockdown, in the refugee camps, and in different cultural structures and economic conditions. Situations that families face due to changing conditions such as global changes which cause lifestyles, immigration patterns, social conflicts, and raising children with developmental disabilities have been discussed. Topics such as professional concern with child care and education, gender roles and caregiving, and father’s involvement in parenting, have been covered in different chapters of the book

Tilting at Windmills: Policy Battles Won, Lost or Long Since Forgotten, 2015

http://deepblue.lib.umich.edu/bitstream/2027.42/117519/1/2015 Tilting at Windmills.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/117519/2/2015 Tilting at Windmills_bhl-c8a06335.pdfDescription of 2015 Tilting at Windmills.pdf : OriginalDescription of 2015 Tilting at Windmills_bhl-c8a06335.pdf : Preservation versio