Learning and teaching from the edge to centre stage : Critical factors in embedding sustainable university-wide engagement in external awards and grants funding initiatives.

Teaching awards, grants and fellowships are strategies used to recognise outstanding contributions to learning and teaching, encourage innovation, and to shift learning and teaching from the edge to centre stage. Examples range from school, faculty and institutional award and grant schemes to national schemes such as those offered by the Australian Learning and Teaching Council (ALTC), the Carnegie Foundation for the Advancement of Teaching in the United States, and the Fund for the Development of Teaching and Learning in higher education in the United Kingdom. The Queensland University of Technology (QUT) has experienced outstanding success in all areas of the ALTC funding since the inception of the Carrick Institute for Learning and Teaching in 2004. This paper reports on a study of the critical factors that have enabled sustainable and resilient institutional engagement with ALTC programs. As a lens for examining the QUT environment and practices, the study draws upon the five conditions of the framework for effective dissemination of innovation developed by Southwell, Gannaway, Orrell, Chalmers and Abraham (2005, 2010): 1. Effective, multi-level leadership and management 2. Climate of readiness for change 3. Availability of resources 4. Comprehensive systems in institutions and funding bodies 5. Funding design The discussion on the critical factors and practical and strategic lessons learnt for successful university-wide engagement offer insights for university leaders and staff who are responsible for learning and teaching award, grant and associated internal and external funding schemes.

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.

A learning-based approach to reactive security

Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst-case assumptions about the attacker: we grant the attacker complete knowledge of the defender’s strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker’s incentives and knowledge.

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 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.

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?

Ceremony and cybrary : Digital libraries and the dialectic of place and space

This article examines social, cultural and technological change in the systems and economies of educational information management. Since the Sumerians first collected, organized and supervised administrative and religious records some six millennia ago, libraries have been key physical depositories and cultural signifiers in the production and mediation of social capital and power through education. To date, the textual, archival and discursive practices perpetuating libraries have remained exempt from inquiry. My aim here is to remedy this hiatus by making the library itself the terrain and object of critical analysis and investigation. The paper argues that in the three dominant communications eras—namely, oral, print and digital cultures—society’s centres of knowledge and learning have resided in the ceremony, the library and the cybrary respectively. In a broad-brush historical grid, each of these key educational institutions—the ceremony in oral culture, the library in print culture and the cybrary in digital culture—are mapped against social, cultural and technological orders pertaining to their era. Following a description of these shifts in society’s collective cultural memory, the paper then examines the question of what the development of global information systems and economies mean for schools and libraries of today, and for teachers and learners as knowledge consumers and producers?

Exponentiated gradient algorithms for conditional random fields and max-margin Markov Networks

Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of parameters in these models is therefore an important problem, and becomes a key factor when learning from very large data sets. This paper describes exponentiated gradient (EG) algorithms for training such models, where EG updates are applied to the convex dual of either the log-linear or max-margin objective function; the dual in both the log-linear and max-margin cases corresponds to minimizing a convex function with simplex constraints. We study both batch and online variants of the algorithm, and provide rates of convergence for both cases. In the max-margin case, O(1/ε) EG updates are required to reach a given accuracy ε in the dual; in contrast, for log-linear models only O(log(1/ε)) updates are required. For both the max-margin and log-linear cases, our bounds suggest that the online EG algorithm requires a factor of n less computation to reach a desired accuracy than the batch EG algorithm, where n is the number of training examples. Our experiments confirm that the online algorithms are much faster than the batch algorithms in practice. We describe how the EG updates factor in a convenient way for structured prediction problems, allowing the algorithms to be efficiently applied to problems such as sequence learning or natural language parsing. We perform extensive evaluation of the algorithms, comparing them to L-BFGS and stochastic gradient descent for log-linear models, and to SVM-Struct for max-margin models. The algorithms are applied to a multi-class problem as well as to a more complex large-scale parsing task. In all these settings, the EG algorithms presented here outperform the other methods.

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.

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.