Active Learning with Expert Advice

Conventional learning with expert advice methods assumes a learner is always receiving the outcome (e.g., class labels) of every incoming training instance at the end of each trial. In real applications, acquiring the outcome from oracle can be costly or time consuming. In this paper, we address a new problem of active learning with expert advice, where the outcome of an instance is disclosed only when it is requested by the online learner. Our goal is to learn an accurate prediction model by asking the oracle the number of questions as small as possible. To address this challenge, we propose a framework of active forecasters for online active learning with expert advice, which attempts to extend two regular forecasters, i.e., Exponentially Weighted Average Forecaster and Greedy Forecaster, to tackle the task of active learning with expert advice. We prove that the proposed algorithms satisfy the Hannan consistency under some proper assumptions, and validate the efficacy of our technique by an extensive set of experiments.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013

Online active learning with expert advice

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiativ

Dynamic Ensemble Active Learning: A Non-Stationary Bandit with Expert Advice

Active learning aims to reduce annotation cost by predicting which samples are useful for a human teacher to label. However it has become clear there is no best active learning algorithm. Inspired by various philosophies about what constitutes a good criteria, different algorithms perform well on different datasets. This has motivated research into ensembles of active learners that learn what constitutes a good criteria in a given scenario, typically via multi-armed bandit algorithms. Though algorithm ensembles can lead to better results, they overlook the fact that not only does algorithm efficacy vary across datasets, but also during a single active learning session. That is, the best criteria is non-stationary. This breaks existing algorithms' guarantees and hampers their performance in practice. In this paper, we propose dynamic ensemble active learning as a more general and promising research direction. We develop a dynamic ensemble active learner based on a non-stationary multi-armed bandit with expert advice algorithm. Our dynamic ensemble selects the right criteria at each step of active learning. It has theoretical guarantees, and shows encouraging results on $13$ popular datasets.Comment: This work has been accepted at ICPR2018 and won Piero Zamperoni Best Student Paper Awar

Adaptive Selective Sampling for Online Prediction with Experts

We consider online prediction of a binary sequence with expert advice. For this setting, we devise label-efficient forecasting algorithms, which use a selective sampling scheme that enables collecting much fewer labels than standard procedures, while still retaining optimal worst-case regret guarantees. These algorithms are based on exponentially weighted forecasters, suitable for settings with and without a perfect expert. For a scenario where one expert is strictly better than the others in expectation, we show that the label complexity of the label-efficient forecaster scales roughly as the square root of the number of rounds. Finally, we present numerical experiments empirically showing that the normalized regret of the label-efficient forecaster can asymptotically match known minimax rates for pool-based active learning, suggesting it can optimally adapt to benign settings

Lipschitz Adaptivity with Multiple Learning Rates in Online Learning

We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning by the user as possible. A fundamental obstacle that comes up in the design of such adaptive algorithms is to calibrate a so-called step-size or learning rate hyperparameter depending on variance, gradient norms, etc. A recent technique promises to overcome this difficulty by maintaining multiple learning rates in parallel. This technique has been applied in the MetaGrad algorithm for online convex optimization and the Squint algorithm for prediction with expert advice. However, in both cases the user still has to provide in advance a Lipschitz hyperparameter that bounds the norm of the gradients. Although this hyperparameter is typically not available in advance, tuning it correctly is crucial: if it is set too small, the methods may fail completely; but if it is taken too large, performance deteriorates significantly. In the present work we remove this Lipschitz hyperparameter by designing new versions of MetaGrad and Squint that adapt to its optimal value automatically. We achieve this by dynamically updating the set of active learning rates. For MetaGrad, we further improve the computational efficiency of handling constraints on the domain of prediction, and we remove the need to specify the number of rounds in advance.Comment: 22 pages. To appear in COLT 201

Evaluating the Effectiveness of Teaching Assistants in Active Learning Classrooms

Active learning classrooms (ALCs) support teaching approaches that foster greater interaction and student engagement. However, a common challenge for instructors who teach in ALCs is to provide adequate assistance to students while implementing collaborative activities. This study examined the impact of teaching assistants in a large ALC. The results showed that incorporating teaching assistants increases students’ access to expert advice during small group activities; further, students view the teaching assistants as supportive of their success in the classroom. Therefore, availability of teaching assistants for instructors teaching in large ALCs must be considered along with classroom design and pedagogical approach

Designing a physical activity parenting course : parental views on recruitment, content and delivery

Background Many children do not engage in sufficient levels of physical activity (PA) and spend too much time screen-viewing (SV). High levels of SV (e.g. watching TV, playing video games and surfing the internet) and low levels of PA have been associated with adverse health outcomes. Parenting courses may hold promise as an intervention medium to change children’s PA and SV. The current study was formative work conducted to design a new parenting programme to increase children’s PA and reduce their SV. Specifically, we focussed on interest in a course, desired content and delivery style, barriers and facilitators to participation and opinions on control group provision. Methods In-depth telephone interviews were conducted with thirty two parents (29 female) of 6–8 year olds. Data were analysed thematically. An anonymous online survey was also completed by 750 parents of 6–8 year old children and descriptive statistics calculated. Results Interview participants were interested in a parenting course because they wanted general parenting advice and ideas to help their children be physically active. Parents indicated that they would benefit from knowing how to quantify their child’s PA and SV levels. Parents wanted practical ideas of alternatives to SV. Most parents would be unable to attend unless childcare was provided. Schools were perceived to be a trusted source of information about parenting courses and the optimal recruitment location. In terms of delivery style, the majority of parents stated they would prefer a group-based approach that provided opportunities for peer learning and support with professional input. Survey participants reported the timing of classes and the provision of childcare were essential factors that would affect participation. In terms of designing an intervention, the most preferred control group option was the opportunity to attend the same course at a later date. Conclusions Parents are interested in PA/SV parenting courses but the provision of child care is essential for attendance. Recruitment is likely to be facilitated via trusted sources. Parents want practical advice on how to overcome barriers and suggest advice is provided in a mutually supportive group experience with expert input

Farmer groups for animal health and welfare planning in European organic dairy herds

A set of common principles for active animal health and welfare planning in organic dairy farming has been developed in the ANIPLAN project group of seven European countries. Health and welfare planning is a farmer‐owned process of continuous development and improvement and may be practised in many different ways. It should incorporate health promotion and disease handling, based on a strategy where assessment of current status and risks forms the basis for evaluation, action and review. Besides this, it should be 1) farm-specific, 2) involve external person(s) and 3) external knowledge, 4) be based on organic principles, 5) be written, and 6) acknowledge good aspects in addition to targeting the problem areas in order to stimulate the learning process. Establishing farmer groups seems to be a beneficial way of stimulating a dynamic development on the farms towards continuous improvement, as in this case with focus on animal health and welfare planning. Various factors influence the process in different contexts, e.g. geographical, cultural, traditional factors, and a proper analysis of the situation as well as the purpose of the group is necessary, and can relevantly be negotiated and co‐developed with farmers as well as facilitators before being implemented. Farmer groups based on farmer‐to‐farmer advice and co‐development need a facilitator who takes on the role of facilitating the process and ‘decodes’ him‐ or herself from being ‘expert’

Universal Learning of Repeated Matrix Games

We study and compare the learning dynamics of two universal learning algorithms, one based on Bayesian learning and the other on prediction with expert advice. Both approaches have strong asymptotic performance guarantees. When confronted with the task of finding good long-term strategies in repeated 2x2 matrix games, they behave quite differently.Comment: 16 LaTeX pages, 8 eps figure