Machine learning in real-world high-skew domains is difficult, because traditional strategies for crowdsourcing labeled training examples are ineffective at locating the scarce minority-class examples. For example, both random sampling and traditional active learning (which reduces to random sampling when just starting) will most likely recover very few minority-class examples. To bootstrap the machine learning process, researchers have proposed tasking the crowd with finding or generating minority-class examples, but such strategies have their weaknesses as well. They are unnecessarily expensive in well-balanced domains, and they often yield samples from a biased distribution that is unrepresentative of the one being learned.This paper extends the traditional active learning framework by investigating the problem of intelligently switching between various crowdsourcing strategies for obtaining labeled training examples in order to optimally train a classifier. We start by analyzing several such strategies (e.g., annotate an example, generate a minority-class example, etc.), and then develop a novel, skew-robust algorithm, called MB-CB, for the control problem. Experiments show that our method outperforms state-of-the-art GL-Hybrid by up to 14.3 points in F1 AUC, across various domains and class-frequency settings.

Recent work has begun exploring the value of domain abstractions in Monte-Carlo Tree Search (MCTS) algorithms for probabilistic planning. These algorithms automatically aggregate symmetric search nodes (states or state-action pairs) saving valuable planning time. Existing algorithms alternate between two phases: (1) abstraction computation forcomputing node aggregations, and (2) modified MCTS that use aggregate nodes. We believe that these algorithms do not achieve the full potential of abstractions because of disjoint phases – e.g., it can take a while to recover from erroneous abstractions, or compute better abstractions based on newly found knowledge.In response, we propose On-the-Go Abstractions (OGA), a novel approach in which abstraction computation is tightlyintegrated into the MCTS algorithm. We implement these on top of UCT and name the resulting algorithm OGA-UCT.It has several desirable properties, including (1) rapid use of new information in modifying existing abstractions, (2) elimination of the expensive batch abstraction computationphase, and (3) focusing abstraction computation on important part of the sampled search space. We experimentally compare OGA-UCT against ASAP-UCT, a recent state-of-the-art MDP algorithm as well as vanilla UCT algorithm. We find that OGA-UCT is robust across a suite of planning competition and other MDP domains, and obtains up to 28 % quality improvements.

Active learning seeks to train the best classifier at the lowest annotation cost by intelligently picking the best examples to label. Traditional algorithms assume there is a single annotator and disregard the possibility of requesting additional independent annotations for a previously labeled example. However, relabeling examples is important, because all annotators make mistakes — especially crowdsourced workers, who have become a common source of training data. This paper seeks to understand the difference in marginal value between decreasing the noise of the training set via relabeling and increasing the size and diversity of the (noisier) training set by labeling new examples. We use the term re-active learning to denote this generalization of active learning. We show how traditional active learning methods perform poorly at re-active learning, present new algorithms designed for this important problem, formally characterize their behavior, and empirically show that our methods effectively make this tradeoff.

An ideal crowdsourcing or citizen-science system would route tasks to the most appropriate workers, but the best assignment is unclear because workers have varying skill, tasks have varying difficulty, and assigning several workers to a single task may significantly improve output quality. This paper defines a space of task routing problems, proves that even the simplest is NP-hard, and develops several approximation algorithms for parallel routing problems. We show that an intuitive class of requesters' utility functions is submodular, which lets us provide iterative methods for dynamically allocating batches of tasks that make near-optimal use of available workers in each round. Experiments with live oDesk workers show that our task routing algorithm uses only 48% of the human labor compared to the commonly used round-robin strategy. Further, we provide versions of our task routing algorithm which enable it to scale to large numbers of workers and questions and to handle workers with variable response times while still providing significant benefit over common baselines.

One of the most popular uses of crowdsourcing is to provide training data for supervised machine learning algorithms. Since human annotators often make errors, requesters commonly ask multiple workers to label each example.  But is this strategy always the most cost effective use of crowdsourced workers? We argue "No" --- often classifiers can achieve higher accuracies when trained with noisy "unilabeled" data. However, in some cases relabeling is extremely important.  We discuss three factors that may make relabeling an effective strategy: classifier expressiveness, worker accuracy, and budget.