Structured prediction problems are one of the fundamental tools in machine learning. In order to facilitate algorithm development for their numerical solution, we collect in one place a large number of datasets in easy to read formats for a diverse set of problem classes. We provide archival links to datasets, description of the considered problems and problem formats, and a short summary of problem characteristics including size, number of instances etc. For reference we also give a non-exhaustive selection of algorithms proposed in the literature for their solution. We hope that this central repository will make benchmarking and comparison to established works easier.

Consider the typical supervised learning pipeline that we teach students learning statistical machine learning: we collect data in (X, Y) pairs, where Y is a label or target to be predicted; we pick a model and loss measuring the fidelity of the model to observed data; we choose the model minimizing the loss and validate it on held-out data. This picture obscures what is becoming one of the major challenges in this endeavor: that of actually collecting highquality labeled data [44, 13, 38]. Hand labeling large-scale training sets is often impractically expensive. Consider, as simple motivation, a ranking problem: a prediction is an ordered list of a set of items, yet available feedback is likely to be incomplete and partial, such as a top element (for example, in web search a user clicks on a single preferred link, or in a grocery, an individual buys one kind of milk but provides no feedback on the other brands present). Developing methods to leverage such partial and weak feedback is therefore becoming a major focus, and researchers have developed methods to transform weak and noisy labels into a dataset with strong, "gold-standard" labels [38, 56]. In this paper, we adopt this weakly labeled setting, but instead of considering model fitting and the construction of strong labels, we focus on validation, model confidence, and predictive inference, moving beyond point predictions and single labels. Our goal is to develop methods to rigorously quantify the confidence a practitioner should have in a model given only weak labels.

In multi-label learning, each object is represented by a single instance while associated with a set of class labels. Due to the huge (exponential) number of possible label sets for prediction, existing approaches mainly focus on how to exploit label correlations to facilitate the learning process. Nevertheless, an intrinsic characteristic of learning from multi-label data, i.e. the widely-existing class-imbalance among labels, has not been well investigated. Generally, the number of positive training instances w.r.t. each class label is far less than its negative counterparts, which may lead to performance degradation for most multi-label learning techniques. In this paper, a new multi-label learning approach named Cross-Coupling Aggregation (COCOA) is proposed, which aims at leveraging the exploitation of label correlations as well as the exploration of class-imbalance. Briefly, to induce the predictive model on each class label, one binary-class imbalance learner corresponding to the current label and several multi-class imbalance learners coupling with other labels are aggregated for prediction. Extensive experiments clearly validate the effectiveness of the proposed approach, especially in terms of imbalance-specific evaluation metrics such as F-measure and area under the ROC curve.

Research in learning from demonstration can generally be grouped into either imitation learning or intention learning. In imitation learning, the goal is to imitate the observed behavior of an expert and is typically achieved using supervised learning techniques. In intention learning, the goal is to learn the intention that motivated the expert's behavior and to use a planning algorithm to derive behavior. Imitation learning has the advantage of learning a direct mapping from states to actions, which bears a small computational cost. Intention learning has the advantage of behaving well in novel states, but may bear a large computational cost by relying on planning algorithms in complex tasks. In this work, we introduce receding horizon inverse reinforcement learning, in which the planning horizon induces a continuum between these two learning paradigms. We present empirical results on multiple domains that demonstrate that performing IRL with a small, but non-zero, receding planning horizon greatly decreases the computational cost of planning while maintaining superior generalization performance compared to imitation learning.

This paper presents a novel approach for learning strips action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given). Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given strips action model, even if this model is not fully specified.

The detection of orchestrated and potentially manipulative campaigns in social media is far more meaningful than analyzing single account behaviour but also more challenging in terms of pattern recognition, data processing, and computational complexity. While supervised learning methods need an enormous amount of reliable ground truth data to find rather inflexible patterns, classical unsupervised learning techniques need a lot of computational power to handle large amount of data. This makes them infeasible for real-time analysis. In this work, we demonstrate the applicability of text stream clustering for the real-time detection of coordinated campaigns.

To build a French national electronic injury surveillance system based on emergency room visits, we aim to develop a coding system to classify their causes from clinical notes in free-text. Supervised learning techniques have shown good results in this area but require a large amount of expert annotated dataset which is time consuming and costly to obtain. We hypothesize that the Natural Language Processing Transformer model incorporating a generative self-supervised pre-training step can significantly reduce the required number of annotated samples for supervised fine-tuning. In this preliminary study, we test our hypothesis in the simplified problem of predicting whether a visit is the consequence of a traumatic event or not from free-text clinical notes. Using fully re-trained GPT-2 models (without OpenAI pre-trained weights), we assess the gain of applying a self-supervised pre-training phase with unlabeled notes prior to the supervised learning task.

In many applications, training machine learning models involves using large amounts of human-annotated data. Obtaining precise labels for the data is expensive. Instead, training with weak supervision provides a low-cost alternative. We propose a novel weak supervision algorithm that processes noisy labels, i.e., weak signals, while also considering features of the training data to produce accurate labels for training. Our method searches over classifiers of the data representation to find plausible labelings. We call this paradigm data consistent weak supervision. A key facet of our framework is that we are able to estimate labels for data examples low or no coverage from the weak supervision. In addition, we make no assumptions about the joint distribution of the weak signals and true labels of the data. Instead, we use weak signals and the data features to solve a constrained optimization that enforces data consistency among the labels we generate. Empirical evaluation of our method on different datasets shows that it significantly outperforms state-of-the-art weak supervision methods on both text and image classification tasks.

Recent advance in deep learning has led to rapid adoption of machine learning based NLP models in a wide range of applications. Despite the continuous gain in accuracy, backward compatibility is also an important aspect for industrial applications, yet it received little research attention. Backward compatibility requires that the new model does not regress on cases that were correctly handled by its predecessor. This work studies model update regression in structured prediction tasks. We choose syntactic dependency parsing and conversational semantic parsing as representative examples of structured prediction tasks in NLP. First, we measure and analyze model update regression in different model update settings. Next, we explore and benchmark existing techniques for reducing model update regression including model ensemble and knowledge distillation. We further propose a simple and effective method, Backward-Congruent Re-ranking (BCR), by taking into account the characteristics of structured output. Experiments show that BCR can better mitigate model update regression than model ensemble and knowledge distillation approaches.

The exponential number of research and publications have introduced many terms and concepts in the domain of machine learning, yet many have degenerated to merely buzzwords without many people fully understanding their differences. The most common, and perhaps THE type that we refer to when talking about machine learning is supervised learning. In simple words, supervised learning provides a set of input-output pairs such that we can learn an intermediate system that maps inputs to correct outputs. A naive example of supervised learning is determining the class (i.e., dogs/cats, etc) of an image based on a dataset of images and their corresponding classes, which we will refer to as their labels. With the given input-label pair, the current popular approach will be to directly train a deep neural network (i.e., a convolutional neural network) to output a label prediction from the given image, compute a differentiable loss between the prediction and the actual correct answers, and backpropagate through the network to update weights to optimise the predictions.