Correction to “The Importance of Convexity in Learning With Squared Loss” [Sep 98 1974-1980]

The paper "the importance of convexity in learning with squared loss" gave a lower bound on the sample complexity of learning with quadratic loss using a nonconvex function class. The proof contains an error. We show that the lower bound is true under a stronger condition that holds for many cases of interest.

Open problems in the security of learning

Machine learning has become a valuable tool for detecting and preventing malicious activity. However, as more applications employ machine learning techniques in adversarial decision-making situations, increasingly powerful attacks become possible against machine learning systems. In this paper, we present three broad research directions towards the end of developing truly secure learning. First, we suggest that finding bounds on adversarial influence is important to understand the limits of what an attacker can and cannot do to a learning system. Second, we investigate the value of adversarial capabilities-the success of an attack depends largely on what types of information and influence the attacker has. Finally, we propose directions in technologies for secure learning and suggest lines of investigation into secure techniques for learning in adversarial environments. We intend this paper to foster discussion about the security of machine learning, and we believe that the research directions we propose represent the most important directions to pursue in the quest for secure learning.

Learning to act in uncertain environments : technical perspective

The problem of decision making in an uncertain environment arises in many diverse contexts: deciding whether to keep a hard drive spinning in a net-book; choosing which advertisement to post to a Web site visitor; choosing how many newspapers to order so as to maximize profits; or choosing a route to recommend to a driver given limited and possibly out-of-date information about traffic conditions. All are sequential decision problems, since earlier decisions affect subsequent performance; all require adaptive approaches, since they involve significant uncertainty. The key issue in effectively solving problems like these is known as the exploration/exploitation trade-off: If I am at a cross-roads, when should I go in the most advantageous direction among those that I have already explored, and when should I strike out in a new direction, in the hopes I will discover something better?

Implicit online learning

Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data analysis problems. In this paper, we analyze a class of online learning algorithms based on fixed potentials and nonlinearized losses, which yields algorithms with implicit update rules. We show how to efficiently compute these updates, and we prove regret bounds for the algorithms. We apply our formulation to several special cases where our approach has benefits over existing online learning methods. In particular, we provide improved algorithms and bounds for the online metric learning problem, and show improved robustness for online linear prediction problems. Results over a variety of data sets demonstrate the advantages of our framework.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying general optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion's dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

Matrix regularization techniques for online multitask learning

In this paper we examine the problem of prediction with expert advice in a setup where the learner is presented with a sequence of examples coming from different tasks. In order for the learner to be able to benefit from performing multiple tasks simultaneously, we make assumptions of task relatedness by constraining the comparator to use a lesser number of best experts than the number of tasks. We show how this corresponds naturally to learning under spectral or structural matrix constraints, and propose regularization techniques to enforce the constraints. The regularization techniques proposed here are interesting in their own right and multitask learning is just one application for the ideas. A theoretical analysis of one such regularizer is performed, and a regret bound that shows benefits of this setup is reported.

Multitask learning with expert advice

We consider the problem of prediction with expert advice in the setting where a forecaster is presented with several online prediction tasks. Instead of competing against the best expert separately on each task, we assume the tasks are related, and thus we expect that a few experts will perform well on the entire set of tasks. That is, our forecaster would like, on each task, to compete against the best expert chosen from a small set of experts. While we describe the "ideal" algorithm and its performance bound, we show that the computation required for this algorithm is as hard as computation of a matrix permanent. We present an efficient algorithm based on mixing priors, and prove a bound that is nearly as good for the sequential task presentation case. We also consider a harder case where the task may change arbitrarily from round to round, and we develop an efficient approximate randomized algorithm based on Markov chain Monte Carlo techniques.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Scoping work integrated learning purposes, practices and issues

A range of terms is used in Australian higher education institutions to describe learning approaches and teaching models that provide students with opportunities to engage in learning connected to the world of work. The umbrella term currently being used widely is Work Integrated Learning (WIL). The common aim of approaches captured under the term WIL is to integrate discipline specific knowledge learnt in university setting with that learnt in the practice of work through purposefully designed curriculum. In endeavours to extend WIL opportunities for students, universities are currently exploring authentic learning experiences, both within and outside of university settings. Some universities describe these approaches as ‘real world learning’ or ‘professional learning’. Others refer to ‘social engagement’ with the community and focus on building social capital and citizenship through curriculum design that enables students to engage with the professions through a range of learning experiences. This chapter discusses the context for, the scope, purposes, characteristics and effectiveness of WIL across Australian universities as derived from a national scoping study. This study, undertaken in response to a high level of interest in WIL, involved data collection from academic and professional staff, and students at nearly all Australian universities. Participants in the study consistently reported the benefits, especially in relation to the student learning experience. Responses highlight the importance of strong partnerships between stakeholders to facilitate effective learning outcomes and a range of issues that shape the quality of approaches and models being adopted, in promoting professional learning.

Work integrated learning for life : encouraging agentic engagement

In a rapidly changing world where new work patterns impact on our health, relationships and social fabric, it is critical that we reconsider the role universities could or should play in helping students prepare for the complexities of the 21st century. Efforts to respond to economic imperatives such as the skills shortage have seen a rush to embed work integrated and career development learning in the curriculum as well as a strengthening of the discourse that the university’s role is primarily to produce industry ready or ‘oven ready and self basting’ graduates (Atkins, 1999). This narrow focus on ‘giving industry what industry wants’ (Patrick, Peach & Pocknee, 2009) ignores the importance of helping students develop the types of skills and dispositions they will need. To enable students to thrive not just survive socially and economically in a radically unknowable world, where knowledge becomes obsolete, we need to be ready to develop new futures (Barnett, 2004). This paper considers the concept of ‘work’, the role it plays in our lives, and our aspirations to build sustainable, socially connected communities. We revisit the assumptions underlying the employability argument (Atkins, 1999) in the light of changing notions of work (Hagel, Seely Brown & Davison, 2010), and the need for higher education to contribute to a better and more sustainable society (Pocock, 2003). Specifically we present initiatives developed from work integrated learning (WIL) programs in the United Kingdom and Australia, where WIL programs are framed within the broader context of real world and life-wide curriculum (Jackson, 2010), and where transferable skills and elements of work-related learning programs prepare students for less certain job futures. Such approaches encourage students to take an agentic role (Billett & Pavlova, 2005) in selecting their work possibilities to develop resilience and capabilities to deal with new and challenging situations, assisting students to become who they want to be not just what they want to be. The theoretical and operational implications and challenges of shaping real world and life-wide curriculum will be investigated in more depth in the next phase of this research.

The WIL (Work Integrated Learning) report : a national scoping study [Final Report]

This report provides an account of the first large-scale scoping study of work integrated learning (WIL) in contemporary Australian higher education. The explicit aim of the project was to identify issues and map a broad and growing picture of WIL across Australia and to identify ways of improving the student learning experience in relation to WIL. The project was undertaken in response to high levels of interest in WIL, which is seen by universities both as a valid pedagogy and as a means to respond to demands by employers for work-ready graduates, and demands by students for employable knowledge and skills. Over a period of eight months of rapid data collection, 35 universities and almost 600 participants contributed to the project. Participants consistently reported the positive benefits of WIL and provided evidence of commitment and innovative practice in relation to enhancing student learning experiences. Participants provided evidence of strong partnerships between stakeholders and highlighted the importance of these relationships in facilitating effective learning outcomes for students. They also identified a range of issues and challenges that face the sector in growing WIL opportunities; these issues and challenges will shape the quality of WIL experiences. While the majority of comments focused on issues involved in ensuring quality placements, it was recognised that placements are just one way to ensure the integration of work with learning. Also, the WIL experience is highly contextualised and impacted by the expectations of students, employers, the professions, the university and government policy.

Malnutrition and clinical outcomes in elderly patients from a Singapore acute hospital

Older adults, especially those acutely ill, are vulnerable to developing malnutrition due to a range of risk factors. The high prevalence and extensive consequences of malnutrition in hospitalised older adults have been reported extensively. However, there are few well-designed longitudinal studies that report the independent relationship between malnutrition and clinical outcomes after adjustment for a wide range of covariates. Acutely ill older adults are exceptionally prone to nutritional decline during hospitalisation, but few reports have studied this change and impact on clinical outcomes. In the rapidly ageing Singapore population, all this evidence is lacking, and the characteristics associated with the risk of malnutrition are also not well-documented. Despite the evidence on malnutrition prevalence, it is often under-recognised and under-treated. It is therefore crucial that validated nutrition screening and assessment tools are used for early identification of malnutrition. Although many nutrition screening and assessment tools are available, there is no universally accepted method for defining malnutrition risk and nutritional status. Most existing tools have been validated amongst Caucasians using various approaches, but they are rarely reported in the Asian elderly and none has been validated in Singapore. Due to the multiethnicity, cultural, and language differences in Singapore older adults, the results from non-Asian validation studies may not be applicable. Therefore it is important to identify validated population and setting specific nutrition screening and assessment methods to accurately detect and diagnose malnutrition in Singapore. The aims of this study are therefore to: i) characterise hospitalised elderly in a Singapore acute hospital; ii) describe the extent and impact of admission malnutrition; iii) identify and evaluate suitable methods for nutritional screening and assessment; and iv) examine changes in nutritional status during admission and their impact on clinical outcomes. A total of 281 participants, with a mean (+SD) age of 81.3 (+7.6) years, were recruited from three geriatric wards in Tan Tock Seng Hospital over a period of eight months. They were predominantly Chinese (83%) and community-dwellers (97%). They were screened within 72 hours of admission by a single dietetic technician using four nutrition screening tools [Tan Tock Seng Hospital Nutrition Screening Tool (TTSH NST), Nutritional Risk Screening 2002 (NRS 2002), Mini Nutritional Assessment-Short Form (MNA-SF), and Short Nutritional Assessment Questionnaire (SNAQ©)] that were administered in no particular order. The total scores were not computed during the screening process so that the dietetic technician was blinded to the results of all the tools. Nutritional status was assessed by a single dietitian, who was blinded to the screening results, using four malnutrition assessment methods [Subjective Global Assessment (SGA), Mini Nutritional Assessment (MNA), body mass index (BMI), and corrected arm muscle area (CAMA)]. The SGA rating was completed prior to computation of the total MNA score to minimise bias. Participants were reassessed for weight, arm anthropometry (mid-arm circumference, triceps skinfold thickness), and SGA rating at discharge from the ward. The nutritional assessment tools and indices were validated against clinical outcomes (length of stay (LOS) >11days, discharge to higher level care, 3-month readmission, 6-month mortality, and 6-month Modified Barthel Index) using multivariate logistic regression. The covariates included age, gender, race, dementia (defined using DSM IV criteria), depression (defined using a single question “Do you often feel sad or depressed?”), severity of illness (defined using a modified version of the Severity of Illness Index), comorbidities (defined using Charlson Comorbidity Index, number of prescribed drugs and admission functional status (measured using Modified Barthel Index; MBI). The nutrition screening tools were validated against the SGA, which was found to be the most appropriate nutritional assessment tool from this study (refer section 5.6) Prevalence of malnutrition on admission was 35% (defined by SGA), and it was significantly associated with characteristics such as swallowing impairment (malnourished vs well-nourished: 20% vs 5%), poor appetite (77% vs 24%), dementia (44% vs 28%), depression (34% vs 22%), and poor functional status (MBI 48.3+29.8 vs 65.1+25.4). The SGA had the highest completion rate (100%) and was predictive of the highest number of clinical outcomes: LOS >11days (OR 2.11, 95% CI [1.17- 3.83]), 3-month readmission (OR 1.90, 95% CI [1.05-3.42]) and 6-month mortality (OR 3.04, 95% CI [1.28-7.18]), independent of a comprehensive range of covariates including functional status, disease severity and cognitive function. SGA is therefore the most appropriate nutritional assessment tool for defining malnutrition. The TTSH NST was identified as the most suitable nutritional screening tool with the best diagnostic performance against the SGA (AUC 0.865, sensitivity 84%, specificity 79%). Overall, 44% of participants experienced weight loss during hospitalisation, and 27% had weight loss >1% per week over median LOS 9 days (range 2-50). Wellnourished (45%) and malnourished (43%) participants were equally prone to experiencing decline in nutritional status (defined by weight loss >1% per week). Those with reduced nutritional status were more likely to be discharged to higher level care (adjusted OR 2.46, 95% CI [1.27-4.70]). This study is the first to characterise malnourished hospitalised older adults in Singapore. It is also one of the very few studies to (a) evaluate the association of admission malnutrition with clinical outcomes in a multivariate model; (b) determine the change in their nutritional status during admission; and (c) evaluate the validity of nutritional screening and assessment tools amongst hospitalised older adults in an Asian population. Results clearly highlight that admission malnutrition and deterioration in nutritional status are prevalent and are associated with adverse clinical outcomes in hospitalised older adults. With older adults being vulnerable to risks and consequences of malnutrition, it is important that they are systematically screened so timely and appropriate intervention can be provided. The findings highlighted in this thesis provide an evidence base for, and confirm the validity of the current nutrition screening and assessment tools used among hospitalised older adults in Singapore. As the older adults may have developed malnutrition prior to hospital admission, or experienced clinically significant weight loss of >1% per week of hospitalisation, screening of the elderly should be initiated in the community and continuous nutritional monitoring should extend beyond hospitalisation.

Ensuring student success : the role of support services in improving the quality of the student learning experience

This paper is based on the premise that universities have an obligation to provide adequate student support services, such as learning assistance (that is, assistance with academic writing and other study skills) and that in order to be effective such services must be responsive to the wider policy and social implications of student attrition and retention. The paper outlines briefly some of the factors that have influenced the development of learning assistance practices in Australia and America. This is followed by an account of experiences at one Australian metropolitan university where learning assistance service provision shifted from a decentralised, faculty-based model to a centralised model of service delivery. This shift was in response to concerns about lack of quality and consistency in a support model dependent upon faculty resources yet a follow up study identified other problems in the centralised delivery of learning assistance services. These problems, clustered under the heading contextualised versus decontextualised learning assistance, include the relevance of generic learning assistance services to students struggling with specific course related demands; the apparent tensions between challenging students and assisting students at risk of failure; and variations in the level of collaboration between learning advisers and academic staff in supporting students in the learning environment. These problems are analysed using the theoretical modelling derived from the tools made available through cultural historical activity theory and expansive visibilisation (Engeström & Miettinen, 1999).