The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches. The problem is further exacerbated when supervised learning is applied to a number of correlated tasks simultaneously since the amount of labels required scales with the number of tasks. To mitigate this concern, we propose an active multitask learning algorithm that achieves knowledge transfer between tasks. The approach forms a so-called committee for each task that jointly makes decisions and directly shares data across similar tasks. Our approach reduces the number of queries needed during training while maintaining high accuracy on test data. Empirical results on benchmark datasets show significant improvements on both accuracy and number of query requests. A triumph of machine learning is the ability to predict with high accuracy. However, for the dominant paradigm, which is supervised learning, the main bottleneck is the need to annotate data, namely, to obtain labeled training examples. The problem becomes more pronounced in applications and systems which require a high level of personalization, such as music recommenders, spam filters, etc. Several thousand labeled emails are usually sufficient for training a good spam filter for a particular user. However, in real world email systems, the number of registered users is potentially in the millions, and it might not be feasible to learn a highly personalized spam filter for each of them by getting several thousand labeled data points for each user.One method to relieve the need of the prohibitively large amount of labeled data is to leverage the relationship between the tasks, especially by transferring relevant knowledge from information-rich tasks to information-poor ones, which is called multitask learning in the literature. We consider multitask learning in an online setting where the learner sees the data sequentially, which is more practical in real world applications. In this setting, the learner receives an example at each time round, along with its task identifier, and then predicts its true label. Afterwards, the learner queries the true label and updates the model(s) accordingly.The online multitask setting has received increasing attention in the machine learning community in recent years BID6 BID0 BID7 BID9 BID4 BID13 BID11 . However, they make the assumption that the true label is readily available to be queried, which is impractical in many applications. Also, querying blindly can be inefficient when annotation is costly.Active learning further reduces the work of the annotator by selectively requesting true labels from the oracles. Most approaches in active learning for sequential and streambased problems adopt a measure of uncertainty / confidence of the learner in the current example BID5 BID3 BID12 BID8 BID1 .The recent work by BID10 combines active learning with online multitask learning using peers or related tasks. When the classifier of the current task is not confident, it first queries its similar tasks before requesting a true label from the oracle, incurring a lower cost. Their learner gives priority to the current task by always checking its confidence first. In the case when the current task is confident, the opinions of its peers are ignored. This paper proposes an active multitask learning framework which is more humble, in a sense that both the current task and its peers' predictions are considered simultaneously using a weighted sum. We have a committee which makes joint decisions for each task. In addition , after the true label of a training sample is obtained, this sample is shared directly to similar tasks, which makes training more efficient. We propose a new active multitask learning algorithm that encourages more knowledge transfer among tasks compared to the state-of-the-art models, by using joint decision / prediction and directly sharing training examples with true labels among similar tasks. Our proposed methods achieve both higher accuracy and lower number of queries on three benchmark datasets for multitask learning problems. Future work includes theoretical analysis of the error bound and comparison with those of the baseline models. Another interesting direction is to handle unbalanced task data. In other words, one task has much more / less training data than the others.
We propose an active multitask learning algorithm that achieves knowledge transfer between tasks.
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