11 research outputs found
Near-Optimal Machine Teaching via Explanatory Teaching Sets
Modern applications of machine teaching for humans often involve domain-specific, non- trivial target hypothesis classes. To facilitate understanding of the target hypothesis, it is crucial for the teaching algorithm to use examples which are interpretable to the human learner. In this paper, we propose NOTES, a principled framework for constructing interpretable teaching sets, utilizing explanations to accelerate the teaching process. Our algorithm is built upon a natural stochastic model of learners and a novel submodular surrogate objective function which greedily selects interpretable teaching examples. We prove that NOTES is competitive with the optimal explanation-based teaching strategy. We further instantiate NOTES with a specific hypothesis class, which can be viewed as an interpretable approximation of any hypothesis class, allowing us to handle complex hypothesis in practice. We demonstrate the effectiveness of NOTES on several image classification tasks, for both simulated and real human learners. Our experimental results suggest that by leveraging explanations, one can significantly speed up teaching
Near-Optimal Machine Teaching via Explanatory Teaching Sets
Modern applications of machine teaching for humans often involve domain-specific, non- trivial target hypothesis classes. To facilitate understanding of the target hypothesis, it is crucial for the teaching algorithm to use examples which are interpretable to the human learner. In this paper, we propose NOTES, a principled framework for constructing interpretable teaching sets, utilizing explanations to accelerate the teaching process. Our algorithm is built upon a natural stochastic model of learners and a novel submodular surrogate objective function which greedily selects interpretable teaching examples. We prove that NOTES is competitive with the optimal explanation-based teaching strategy. We further instantiate NOTES with a specific hypothesis class, which can be viewed as an interpretable approximation of any hypothesis class, allowing us to handle complex hypothesis in practice. We demonstrate the effectiveness of NOTES on several image classification tasks, for both simulated and real human learners. Our experimental results suggest that by leveraging explanations, one can significantly speed up teaching
Linear Bandits with Feature Feedback
This paper explores a new form of the linear bandit problem in which the
algorithm receives the usual stochastic rewards as well as stochastic feedback
about which features are relevant to the rewards, the latter feedback being the
novel aspect. The focus of this paper is the development of new theory and
algorithms for linear bandits with feature feedback. We show that linear
bandits with feature feedback can achieve regret over time horizon that
scales like , without prior knowledge of which features are relevant
nor the number of relevant features. In comparison, the regret of
traditional linear bandits is , where is the total number of
(relevant and irrelevant) features, so the improvement can be dramatic if . The computational complexity of the new algorithm is proportional to
rather than , making it much more suitable for real-world applications
compared to traditional linear bandits. We demonstrate the performance of the
new algorithm with synthetic and real human-labeled data
Teaching Categories to Human Learners with Visual Explanations
We study the problem of computer-assisted teaching with explanations.
Conventional approaches for machine teaching typically only provide feedback at
the instance level e.g., the category or label of the instance. However, it is
intuitive that clear explanations from a knowledgeable teacher can
significantly improve a student's ability to learn a new concept. To address
these existing limitations, we propose a teaching framework that provides
interpretable explanations as feedback and models how the learner incorporates
this additional information. In the case of images, we show that we can
automatically generate explanations that highlight the parts of the image that
are responsible for the class label. Experiments on human learners illustrate
that, on average, participants achieve better test set performance on
challenging categorization tasks when taught with our interpretable approach
compared to existing methods
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
In real-world applications of education, an effective teacher adaptively
chooses the next example to teach based on the learner's current state.
However, most existing work in algorithmic machine teaching focuses on the
batch setting, where adaptivity plays no role. In this paper, we study the case
of teaching consistent, version space learners in an interactive setting. At
any time step, the teacher provides an example, the learner performs an update,
and the teacher observes the learner's new state. We highlight that adaptivity
does not speed up the teaching process when considering existing models of
version space learners, such as "worst-case" (the learner picks the next
hypothesis randomly from the version space) and "preference-based" (the learner
picks hypothesis according to some global preference). Inspired by human
teaching, we propose a new model where the learner picks hypotheses according
to some local preference defined by the current hypothesis. We show that our
model exhibits several desirable properties, e.g., adaptivity plays a key role,
and the learner's transitions over hypotheses are smooth/interpretable. We
develop efficient teaching algorithms and demonstrate our results via
simulation and user studies.Comment: NeurIPS 2018 (extended version
Teaching categories to human learners with visual explanations
We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student's ability to learn a new concept. To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information. In the case of images, we show that we can automatically generate explanations that highlight the parts of the image that are responsible for the class label. Experiments on human learners illustrate that, on average, participants achieve better test set performance on challenging categorization tasks when taught with our interpretable approach compared to existing methods
ML-Based Teaching Systems: A Conceptual Framework
As the shortage of skilled workers continues to be a pressing issue, exacerbated by demographic change, it is becoming a critical challenge for organizations to preserve the knowledge of retiring experts and to pass it on to novices. While this knowledge transfer has traditionally taken place through personal interaction, it lacks scalability and requires significant resources and time. IT-based teaching systems have addressed this scalability issue, but their development is still tedious and time-consuming. In this work, we investigate the potential of machine learning (ML) models to facilitate knowledge transfer in an organizational context, leading to more cost-effective IT-based teaching systems. Through a systematic literature review, we examine key concepts, themes, and dimensions to better understand and design ML-based teaching systems. To do so, we capture and consolidate the capabilities of ML models in IT-based teaching systems, inductively analyze relevant concepts in this context, and determine their interrelationships. We present our findings in the form of a review of the key concepts, themes, and dimensions to understand and inform on ML-based teaching systems. Building on these results, our work contributes to research on computer-supported cooperative work by conceptualizing how ML-based teaching systems can preserve expert knowledge and facilitate its transfer from SMEs to human novices. In this way, we shed light on this emerging subfield of human-computer interaction and serve to build an interdisciplinary research agenda
Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
In real-world applications of education, an effective teacher adaptively chooses the next example to teach based on the learner's current state. However, most existing work in algorithmic machine teaching focuses on the batch setting, where adaptivity plays no role. In this paper, we study the case of teaching consistent, version space learners in an interactive setting. At any time step, the teacher provides an example, the learner performs an update, and the teacher observes the learner's new state. We highlight that adaptivity does not speed up the teaching process when considering existing models of version space learners, such as "worst-case" (the learner picks the next hypothesis randomly from the version space) and "preference-based" (the learner picks hypothesis according to some global preference). Inspired by human teaching, we propose a new model where the learner picks hypotheses according to some local preference defined by the current hypothesis. We show that our model exhibits several desirable properties, e.g., adaptivity plays a key role, and the learner's transitions over hypotheses are smooth/interpretable. We develop efficient teaching algorithms and demonstrate our results via simulation and user studies