In a supervised learning setting, we have a yardstick or plumbline to judge how well we are doing: the response itself. A frequent question in biological and biomedical applications is whether a property of interest (say, disease type, cell type, the prognosis of a patient) can be "predicted", given one or more other properties, called the predictors. Often we are motivated by a situation in which the property to be predicted is unknown (it lies in the future, or is hard to measure), while the predictors are known. The crucial point is that we learn the prediction rule from a set of training data in which the property of interest is also known. Once we have the rule, we can either apply it to new data, and make actual predictions of unknown outcomes; or we can dissect the rule with the aim of better understanding the underlying biology. Compared to unsupervised learning and what we have seen in Chapters 5, 7 and 9, where we do not know what we are looking for or how to decide whether our result is "right", we are on much more solid ground with supervised learning: the objective is clearly stated, and there are straightforward criteria to measure how well we are doing. The central issues in supervised learning151151 Sometimes the term statistical learning is used, more or less exchangeably. Or did our rule indeed pick up some of the pertinent patterns in the system being studied, which will also apply to yet unseen new data? An example for overfitting: two regression lines are fit to data in the \((x, y)\)-plane (black points). We can think of such a line as a rule that predicts the \(y\)-value, given an \(x\)-value. Both lines are smooth, but the fits differ in what is called their bandwidth, which intuitively can be interpreted their stiffness. The blue line seems overly keen to follow minor wiggles in the data, while the orange line captures the general trend but is less detailed. The effective number of parameters needed to describe the blue line is much higher than for the orange line. Also, if we were to obtain additional data, it is likely that the blue line would do a worse job than the orange line in modeling the new data. We'll formalize these concepts –training error and test set error– later in this chapter. Although exemplified here with line fitting, the concept applies more generally to prediction models. See exemplary applications that motivate the use of supervised learning methods.

As a newly emerging unsupervised learning paradigm, self-supervised learning (SSL) recently gained widespread attention, which usually introduces a pretext task without manual annotation of data. With its help, SSL effectively learns the feature representation beneficial for downstream tasks. Thus the pretext task plays a key role. However, the study of its design, especially its essence currently is still open. In this paper, we borrow a multi-view perspective to decouple a class of popular pretext tasks into a combination of view data augmentation (VDA) and view label classification (VLC), where we attempt to explore the essence of such pretext task while providing some insights into its design. Specifically, a simple multi-view learning framework is specially designed (SSL-MV), which assists the feature learning of downstream tasks (original view) through the same tasks on the augmented views. SSL-MV focuses on VDA while abandons VLC, empirically uncovering that it is VDA rather than generally considered VLC that dominates the performance of such SSL. Additionally, thanks to replacing VLC with VDA tasks, SSL-MV also enables an integrated inference combining the predictions from the augmented views, further improving the performance. Experiments on several benchmark datasets demonstrate its advantages.

Recent advances in machine learning highlight successes on a small set of tasks where a large number of labeled examples have been collected and exploited. These include image classification with 1.2 million labeled examples Deng et al. (2009) and French-English machine translation with 40 million paired sentences Bojar et al. (2014). For common tasks, however, collecting clean labels is costly, as they require human expertise (as in medical imaging) or physical interactions (as in robotics), for example. Thus collected real-world datasets follow a long-tailed distribution, in which a dominant set of tasks only have a small number of training examples Wang et al. (2017). Inspired by human ingenuity in quickly solving novel problems by leveraging prior experience, meta-learning approaches aim to jointly learn from past experience to quickly adapt to new tasks with little available data Schmidhuber (1987); Thrun & Pratt (2012). This has had a significant impact in few-shot supervised learning, where each task is associated with only a few training examples. By leveraging structural similarities among those tasks, one can achieve accuracy far greater than what can be achieved for each task in isolation Finn et al. (2017); Ravi & Larochelle (2016); Koch et al. (2015); Oreshkin et al. (2018); Triantafillou et al. (2019); Rusu et al. (2018). The success of such approaches hinges on the following fundamental question: When can we jointly train small data tasks to achieve the accuracy of large data tasks? We investigate this tradeoff under a canonical scenario where the tasks are linear regressions in d-dimensions and the regression parameters are drawn i.i.d.

Optimization-based meta-learning algorithms are a powerful class of methods for learning-to-learn applications such as few-shot learning. They tackle the limited availability of training data by leveraging the experience gained from previously observed tasks. However, when the complexity of the tasks distribution cannot be captured by a single set of shared meta-parameters, existing methods may fail to fully adapt to a target task. We address this issue with a novel perspective on conditional meta-learning based on structured prediction. We propose task-adaptive structured meta-learning (TASML), a principled estimator that weighs meta-training data conditioned on the target task to design tailored meta-learning objectives. In addition, we introduce algorithmic improvements to tackle key computational limitations of existing methods. Experimentally, we show that TASML outperforms state-of-the-art methods on benchmark datasets both in terms of accuracy and efficiency. An ablation study quantifies the individual contribution of model components and suggests useful practices for meta-learning.

We introduce a method called TrackIn that computes the influence of a training example on a prediction made by the model, by tracking how the loss on the test point changes during the training process whenever the training example of interest was utilized. We provide a scalable implementation of TrackIn via a combination of a few key ideas: (a) a first-order approximation to the exact computation, (b) using random projections to speed up the computation of the first-order approximation for large models, (c) using saved checkpoints of standard training procedures, and (d) cherry-picking layers of a deep neural network. An experimental evaluation shows that TrackIn is more effective in identifying mislabelled training examples than other related methods such as influence functions and representer points. We also discuss insights from applying the method on vision, regression and natural language tasks.

Partial-label learning is one of the important weakly supervised learning problems, where each training example is equipped with a set of candidate labels that contains the true label. Most existing methods elaborately designed learning objectives as constrained optimizations that must be solved in specific manners, making their computational complexity a bottleneck for scaling up to big data. The goal of this paper is to propose a novel framework of partial-label learning without implicit assumptions on the model or optimization algorithm. More specifically, we propose a general estimator of the classification risk, theoretically analyze the classifier-consistency, and establish an estimation error bound. We then explore a progressive identification method for approximately minimizing the proposed risk estimator, where the update of the model and identification of true labels are conducted in a seamless manner. The resulting algorithm is model-independent and loss-independent, and compatible with stochastic optimization. Thorough experiments demonstrate it sets the new state of the art.

Data annotation is an essential stage in supervised learning. However, the annotation process is exhaustive and time consuming, specially for large datasets. Activities of Daily Living (ADL) recognition is an example of systems that exploit very large raw sensor data readings. In such systems, sensor readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the generated dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting supervised learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single "Unknown" or "Do-Nothing" label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every one of them a unique label identifying the encapsulating deterministic labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than $2.5\times 10^6$ sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.

Semi-Supervised Learning (SSL) has achieved great success in overcoming the difficulties of labeling and making full use of unlabeled data. However, SSL has a limited assumption that the numbers of samples in different classes are balanced, and many SSL algorithms show lower performance for the datasets with the imbalanced class distribution. In this paper, we introduce a task of class-imbalanced semi-supervised learning (CISSL), which refers to semi-supervised learning with class-imbalanced data. In doing so, we consider class imbalance in both labeled and unlabeled sets. First, we analyze existing SSL methods in imbalanced environments and examine how the class imbalance affects SSL methods. Then we propose Suppressed Consistency Loss (SCL), a regularization method robust to class imbalance. Our method shows better performance than the conventional methods in the CISSL environment. In particular, the more severe the class imbalance and the smaller the size of the labeled data, the better our method performs.

Machine learning algorithms learn in three ways: unsupervised, supervised, and semi supervised. This picture illustrates the differences between the three types.

We address the problem of semi-supervised learning in an adversarial setting. Instead of assuming that labels are missing at random, we analyze a less favorable scenario where the label information can be missing partially and arbitrarily, which is motivated by several practical examples. Motivated by the analysis, we formulate a convex optimization problem for parameter estimation, derive an efficient algorithm, and analyze its convergence. We provide experimental results on several standard data sets showing the robustness of our algorithm to the pattern of missing label information, outperforming several strong baselines. Papers published at the Neural Information Processing Systems Conference.