Opening of the Conference

We will talk about the development of the Metadata Team at the University of Leeds. Over the last seven years the team has evolved from a traditional, book-in-hand Cataloguing Team to a forward thinking, dynamic Metadata & Discovery Team. We are now involved in myriad projects ranging from internal Special Collections cataloguing to the development of international linked data and BIBFRAME partnerships. 

We will cover five key areas that have contributed to this development. We will share techniques, approaches and examples from the last few years that illustrate the value and importance of a people-centric approach to metadata and discovery.

From Cataloguing to Discovery: a journey of innovation and resilience

How do we evidence the impact of inclusive library collections on the university community? How do we convince university leadership that the value-added potential of the library is worth the investment?

Raising the grade and closing the gap? Exploring the link between inclusive library collections and student success

PGRs are some of the most excited and driven researchers, eager to engage with open access and open research. However, they are also incredibly influenced by the environment in which they find themselves, particularly by their research supervisor’s own behaviours and opinions around open research. Their status as students often limits their access to tools and support available to employed researchers. Equally, their status as researchers can lead to the assumption that they already understand the research and publishing landscape. What can libraries do to support PGRs in becoming ethical and open researchers? Are the one-shot training courses, often voluntary, enough? This talk will discuss barriers and strategies around support for PGRs as they embark on their research careers.

Supporting postgraduate research students in scholarly communications and open research

In this highly interactive, practical session, we’ll introduce the COUNTER Code of Practice for usage for usage reporting and guide you through the ins and outs of obtaining and working with your reports. You’ll come away understanding the value of normalised metrics for comparing across publishers and over time, which metrics to use and when, and of course best practice for harvesting your data using automated tools

COUNTER-intuitive: up close and personal with usage metrics

Inspired by colleagues’ work to embed the principles of DORA but frustrated by the snail-like pace at our own institution, we embarked on a plan of small workshops to Do DORAif/where we could. What developed over the next 12 months was in the spirit of our original plan but far beyond what we had envisaged. This breakout will discuss the ways in which circumstance actually embedded, promoted and advocated for DORA with a shoestring budget and time where available. We will discuss practical promotion, guerrilla good-practice, unexpected outcomes and embracing flexible opportunities when it comes to Doing DORA.

Doing DORA: A small-scale project to upscale research culture and research integrity

Beginning with Witkowski et al. (2021), recent work on forecasting competitions has addressed incentive problems with the common winner-take-all mechanism. Frongillo et al. (2021) propose a competition mechanism based on Multiplicative Weights, an online learning algorithm. They show that their mechanism selects an $\epsilon$-optimal forecaster with high probability using only $O(\log(n)/\epsilon^2)$ events. These works, together with all prior work on this problem thus far, assume that events are independent. We prove the first accuracy and approximate truthfulness guarantees for forecasting competitions with correlated events. To quantify correlation, we introduce a notion of block correlation, which allows each event to be strongly correlated with up to $b$ others and weakly correlated with the rest. We show that under distributions with this correlation, the Multiplicative Weights mechanism retains its $\epsilon$-optimal guarantee using $O(b^2 \log(n)/\epsilon^2)$ events. Our proof involves a novel concentration bound for correlated random variables which may be of broader interest.

Forecasting Competitions with Correlated Events

Coalition formation concerns autonomous agents that strategically interact to form self-organized coalitions. When agents lack initial sufficient information to evaluate their preferences before interacting with others, they learn them online through repeated feedback while iteratively forming coalitions. In this work, we introduce online learning in coalition formation from a non-cooperative perspective, studying the impact of collective data utilization where selfish agents aim to accelerate their learning by leveraging a shared data platform. Thus, the efficiency and dynamics of the learning process are affected by each agent's local feedbacks, motivating us to explore the tension between semi-bandit and bandit feedback, which differ in the granularity of utility information observed by each agent. Under our non-cooperative viewpoint, we evaluate the system by means of Nash stability, where no agent can improve her utility by unilaterally deviating. Our main result is a sample-efficient algorithm for selfish agents that aims to minimize their Nash regret under both semi-bandit and bandit feedback, implying approximately Nash stable outcomes. Under both feedback settings, our algorithm enjoys Nash regret and sample complexity bounds that are optimal up to logarithmic factors.

Online Learning of Coalition Structures by Selfish Agents

This paper studies a generalized variant of the Colonel Blotto game, referred to as the Colonel Blotto game with costs. Unlike the classic Colonel Blotto game, which imposes the \textit{use-it-or-lose-it} budget assumption, the Colonel Blotto game with costs captures the strategic importance of costs related both to obtaining resources and assigning them across battlefields. We show that every instance of the Colonel Blotto game with costs is strategically equivalent to an instance of the zero-sum Colonel Blotto game with one additional battlefield. This enables the computation of Nash equilibria of the Colonel Blotto game with costs in polynomial time with respect to the game parameters: the number of battlefields plus the number of resources available to the players.

Equilibria of the Colonel Blotto Games with Costs

We consider correlated equilibria in an adversarial environment, where an adversary can compromise the public signal used by the players for choosing their strategies, while players aim at detecting a potential attack as soon as possible to avoid loss of utility. We model the interaction between the adversary and the players as a zero-sum game and we derive the maxmin strategies for both the defender and the attacker using the framework of quickest change detection. We define a class of adversarial strategies that achieve the optimal trade-off between the impact and the detectability of the attack for the adversary and show that a generalized CUSUM scheme is asymptotically optimal for their detection. Our numerical results on the Sioux-Falls benchmark traffic routing game show that the proposed detection scheme can effectively limit the utility loss by a potential adversary.

Quickest Detection of Adversarial Attacks against Correlated Equilibria

We study the exploration-exploitation trade-off for large multiplayer coordination games where players strategise via Q-Learning, a common learning framework in multi-agent reinforcement learning. Q-learning is known to have two shortcomings, namely  non-convergence and potential equilibrium selection problems, when there are multiple fixed points, called Quantal Response Equilibria (QRE). Furthermore, whilst QRE have full support for finite games, it is not clear how Q-learning behaves as the game becomes large. 

In this paper, we characterise the critical exploration rate that guarantees convergence to a unique fixed point, addressing the two shortcomings above. Using a generating-functional method, we show that this rate increases with the number of players and the alignment of their payoffs. For many-player coordination games with perfectly aligned payoffs, this exploration rate is roughly twice that of $p$-player zero-sum games. As for large games, we provide a structural result for QRE, which suggests that as the game size increases, Q-Learning converges to a QRE near the boundary of the simplex of the action space, a phenomenon we term asymptotic extinction, where a constant fraction of the actions are played with zero probability at a rate $o(1/N)$ for an $N$-action game.

Asymptotic Extinction in Large Coordination Games

Common knowledge/belief in rationality is the traditional standard assumption in analysing interaction between rational agents. The paper proposes a graph-based language for capturing significantly more complicated structures of higher-order beliefs that agents might have about the rationality of the other agents. The two main contributions are a solution concept that captures the reasoning process based on a given belief structure and an efficient algorithm for compressing any belief structure into a unique minimal form.

Underline digital video library

Opening of the Conference

From Cataloguing to Discovery: a journey of innovation and resilience

Raising the grade and closing the gap? Exploring the link between inclusive library collections and student success

Supporting postgraduate research students in scholarly communications and open research

COUNTER-intuitive: up close and personal with usage metrics

Doing DORA: A small-scale project to upscale research culture and research integrity

Forecasting Competitions with Correlated Events

Online Learning of Coalition Structures by Selfish Agents

Equilibria of the Colonel Blotto Games with Costs

Quickest Detection of Adversarial Attacks against Correlated Equilibria

Asymptotic Extinction in Large Coordination Games

Uncommon Belief in Rationality

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES