4,568 research outputs found
Compromise, don't optimize:Generalizing perfect Bayesian equilibrium to allow for ambiguity
We introduce a solution concept for extensive-form games of incomplete information in which players can have multiple priors. Playersâ choices are based on the notions of complaints and compromises. Complaints come from hypothetical assessors who have different priors and evaluate the choices of the players. Compromises are choices that aim to make these complaints small. The resulting solution concept is called perfect compromise equilibrium and generalizes perfect Bayesian equilibrium. We use this concept to provide insights into how ambiguity influences Cournot and Bertrand markets, public good provision, markets for lemons, job market signaling, bilateral trade with common value, and forecasting
Meta-learning algorithms and applications
Meta-learning in the broader context concerns how an agent learns about their own learning, allowing them to improve their learning process. Learning how to learn is not only beneficial for humans, but it has also shown vast benefits for improving how machines learn. In the context of machine learning, meta-learning enables models to improve their learning process by selecting suitable meta-parameters that influence the learning. For deep learning specifically, the meta-parameters typically describe details of the training of the model but can also include description of the model itself - the architecture. Meta-learning is usually done with specific goals in mind, for example trying to improve ability to generalize or learn new concepts from only a few examples.
Meta-learning can be powerful, but it comes with a key downside: it is often computationally costly. If the costs would be alleviated, meta-learning could be more accessible to developers of new artificial intelligence models, allowing them to achieve greater goals or save resources. As a result, one key focus of our research is on significantly improving the efficiency of meta-learning. We develop two approaches: EvoGrad and PASHA, both of which significantly improve meta-learning efficiency in two common scenarios. EvoGrad allows us to efficiently optimize the value of a large number of differentiable meta-parameters, while PASHA enables us to efficiently optimize any type of meta-parameters but fewer in number.
Meta-learning is a tool that can be applied to solve various problems. Most commonly it is applied for learning new concepts from only a small number of examples (few-shot learning), but other applications exist too. To showcase the practical impact that meta-learning can make in the context of neural networks, we use meta-learning as a novel solution for two selected problems: more accurate uncertainty quantification (calibration) and general-purpose few-shot learning. Both are practically important problems and using meta-learning approaches we can obtain better solutions than the ones obtained using existing approaches. Calibration is important for safety-critical applications of neural networks, while general-purpose few-shot learning tests model's ability to generalize few-shot learning abilities across diverse tasks such as recognition, segmentation and keypoint estimation.
More efficient algorithms as well as novel applications enable the field of meta-learning to make more significant impact on the broader area of deep learning and potentially solve problems that were too challenging before. Ultimately both of them allow us to better utilize the opportunities that artificial intelligence presents
A Survey on Imitation Learning Techniques for End-to-End Autonomous Vehicles
Funding Agency: 10.13039/100016335-Jaguar Land Rover 10.13039/501100000266-U.K. Engineering and Physical Sciences Research Council (EPSRC) (Grant Number: EP/N01300X/1) jointly funded Towards Autonomy: Smart and Connected Control (TASCC) ProgramPeer reviewedPostprin
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (âAIâ) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics â and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the CatĂłlica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Exploring the dimensionality of fear of missing out: Associations with related constructs
A growing body of research has examined the potential effects of the Fear of Missing Out (FoMO) whereby the Fear of Missing Out Scale (FoMOs; Przybylski et al., 2013) has become the most popular measure for assessing the construct. However, there is ambiguity regarding FoMOâs conceptualization and dimensionality. Employing a large representative sample (N = 2,041), this study provides direct empirical support for the conceptualization of FoMO as a second-order construct with two underlying dimensions, i.e., âpervasive apprehensionâ and âdesire for connectionâ, each with distinct relations with variables that have been theoretically linked with FoMO. More specifically, problematic social media use, deficits in needs satisfaction, and neuroticism are more strongly correlated with âpervasive apprehensionâ, while social media use and extraversion are more strongly correlated with âdesire for connectionâ. As such, this study contributes to future research as it offers a new perspective on the FoMO construct by showing the importance of giving adequate consideration (statistically and conceptually) to the structure of the construct and how the two dimensions relate to other constructs of interest
Conversations on Empathy
In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy â be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" â others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
Stackelberg equilibria arise naturally in a range of popular learning
problems, such as in security games or indirect mechanism design, and have
received increasing attention in the reinforcement learning literature. We
present a general framework for implementing Stackelberg equilibria search as a
multi-agent RL problem, allowing a wide range of algorithmic design choices. We
discuss how previous approaches can be seen as specific instantiations of this
framework. As a key insight, we note that the design space allows for
approaches not previously seen in the literature, for instance by leveraging
multitask and meta-RL techniques for follower convergence. We propose one such
approach using contextual policies, and evaluate it experimentally on both
standard and novel benchmark domains, showing greatly improved sample
efficiency compared to previous approaches. Finally, we explore the effect of
adopting algorithm designs outside the borders of our framework
Game-theoretic statistics and safe anytime-valid inference
Safe anytime-valid inference (SAVI) provides measures of statistical evidence
and certainty -- e-processes for testing and confidence sequences for
estimation -- that remain valid at all stopping times, accommodating continuous
monitoring and analysis of accumulating data and optional stopping or
continuation for any reason. These measures crucially rely on test martingales,
which are nonnegative martingales starting at one. Since a test martingale is
the wealth process of a player in a betting game, SAVI centrally employs
game-theoretic intuition, language and mathematics. We summarize the SAVI goals
and philosophy, and report recent advances in testing composite hypotheses and
estimating functionals in nonparametric settings.Comment: 25 pages. Under review. ArXiv does not compile/space some references
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