14,695 research outputs found

    MERMAIDE: Learning to Align Learners using Model-Based Meta-Learning

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    We study how a principal can efficiently and effectively intervene on the rewards of a previously unseen learning agent in order to induce desirable outcomes. This is relevant to many real-world settings like auctions or taxation, where the principal may not know the learning behavior nor the rewards of real people. Moreover, the principal should be few-shot adaptable and minimize the number of interventions, because interventions are often costly. We introduce MERMAIDE, a model-based meta-learning framework to train a principal that can quickly adapt to out-of-distribution agents with different learning strategies and reward functions. We validate this approach step-by-step. First, in a Stackelberg setting with a best-response agent, we show that meta-learning enables quick convergence to the theoretically known Stackelberg equilibrium at test time, although noisy observations severely increase the sample complexity. We then show that our model-based meta-learning approach is cost-effective in intervening on bandit agents with unseen explore-exploit strategies. Finally, we outperform baselines that use either meta-learning or agent behavior modeling, in both 00-shot and K=1K=1-shot settings with partial agent information

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    Regionale Versicherungsrisiken unter dem morbiditätsorientierten Risikostrukturausgleich: Detektion, Ursachen und Reformbedarf der Wettbewerbsbedingungen in der GKV

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    Der Risikostrukturausgleich (RSA) ist der finanzielle Ausgleichsmechanismus zwischen den Krankenkassen. Er beschreibt, wie die Gelder des Gesundheitsfonds, dem Risiko gerecht, zwischen den Krankenkassen zu verteilen sind. Es ist das vordergründige Ziel des RSA die Möglichkeit der Selektion von guten und schlechten Risiken (Risikoselektion) durch die Krankenkassen zu verhindern. Ohne einen RSA sind neben einem Verstoß gegen das Solidaritätsprinzip (BVerfG, Rn. 162 (18.07.2005)) Effizienzverluste durch die Verschiebung des Wettbewerbes zwischen den Krankenkassen von Qualität auf Risikoselektion (z.B. die Attrahierung von jungen und gesunden Personen), zu befürchten. Die These, die in dieser kumulativen Dissertation untersucht wird, ist, dass das Merkmal der regionalen Herkunft der Versicherten geeignet ist, um gute Risiken von schlechten Risiken zu trennen und somit Anreize zur Risikoselektion bietet. Es wird argumentiert, dass die räumliche Autokorrelation von individuellen Deckungsbeiträgen ein geeignetes Maß ist, um Anreize zur regionalen Risikoselektion zu erkennen. Dabei steht das Argument im Vordergrund, dass neben absoluten Deckungsbeitragsunterschieden die Validität der Information „regionale Herkunft“ für Risikoselektion entscheidend ist. Die zweite Fragestellung der Dissertation betrifft die Ursachen der regionalen Risiken für Krankenkassen. Die Identifikation von Ursachen verfolgt dabei das Ziel zu begründen, ob die Versicherungsrisiken, die mit der regionalen Herkunft assoziiert sind, gemäß des Solidaritätsprinzips durch die Gesamtheit der Versichertengemeinschaft zu tragen wären. Drittens wird die geographisch gewichtete Regression auf die Aspekte des Risikostrukturausgleichs angepasst und ein Verfahren beschrieben, wie die Regression auf dem sehr umfangreichen Datensatz des RSA effizient umgesetzt werden kann. Nach einer langen Debatte unter Gesundheitsökonomen wurde für das Ausgleichsjahr 2021 erstmals eine Regionalisierung im RSA vorgenommen. Den Einzelveröffentlichungen dieser Dissertation war es beschieden, am gesundheitsökonomischen Diskurs teilzuhaben und letztlich die Einführung der Regionalisierung im RSA begleitet zu haben.:1 Einleitung 1.1 Solidarität und Wettbewerb in der GKV 1.2 Motivation der Arbeit und Einordnung in die Literatur 1.3 Forschungsfragen und Gang der Arbeit 2 Der Einfluss der Regionalität auf den Versicherungswettbewerb 2.1 Der wettbewerbliche Ordnungsrahmen der GKV 2.2 Dysfunktionale Folgen eines regional unvollständigen RSA 2.3 Maßzahlen der wettbewerblichen Neutralität des 3 Räumliche Versicherungsrisiken im solidarischen Wettbewerb 3.1 Solidarität im RSA 3.2 Ursachen für regionale Risiken 3.3 Einnahmerisiko 3.4 Mengen- und Strukturrisiko 3.5 Preisrisiko 4 Abbildung von räumlichen Versicherungsrisiken im RSA 4.1 Die Funktionsweise des RSA zwischen 2009 und 2020 4.2 Das M2-Modell 4.3 Das GWR-Modell 4.4 Ein empirischer Vergleich der Regionalisierungsansätze 5 Fazi

    Countermeasures for the majority attack in blockchain distributed systems

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    La tecnología Blockchain es considerada como uno de los paradigmas informáticos más importantes posterior al Internet; en función a sus características únicas que la hacen ideal para registrar, verificar y administrar información de diferentes transacciones. A pesar de esto, Blockchain se enfrenta a diferentes problemas de seguridad, siendo el ataque del 51% o ataque mayoritario uno de los más importantes. Este consiste en que uno o más mineros tomen el control de al menos el 51% del Hash extraído o del cómputo en una red; de modo que un minero puede manipular y modificar arbitrariamente la información registrada en esta tecnología. Este trabajo se enfocó en diseñar e implementar estrategias de detección y mitigación de ataques mayoritarios (51% de ataque) en un sistema distribuido Blockchain, a partir de la caracterización del comportamiento de los mineros. Para lograr esto, se analizó y evaluó el Hash Rate / Share de los mineros de Bitcoin y Crypto Ethereum, seguido del diseño e implementación de un protocolo de consenso para controlar el poder de cómputo de los mineros. Posteriormente, se realizó la exploración y evaluación de modelos de Machine Learning para detectar software malicioso de tipo Cryptojacking.DoctoradoDoctor en Ingeniería de Sistemas y Computació

    Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk

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    We study the trade-off between expectation and tail risk for regret distribution in the stochastic multi-armed bandit problem. We fully characterize the interplay among three desired properties for policy design: worst-case optimality, instance-dependent consistency, and light-tailed risk. We show how the order of expected regret exactly affects the decaying rate of the regret tail probability for both the worst-case and instance-dependent scenario. A novel policy is proposed to characterize the optimal regret tail probability for any regret threshold. Concretely, for any given α∈[1/2,1)\alpha\in[1/2, 1) and β∈[0,α]\beta\in[0, \alpha], our policy achieves a worst-case expected regret of O~(Tα)\tilde O(T^\alpha) (we call it α\alpha-optimal) and an instance-dependent expected regret of O~(Tβ)\tilde O(T^\beta) (we call it β\beta-consistent), while enjoys a probability of incurring an O~(Tδ)\tilde O(T^\delta) regret (δ≥α\delta\geq\alpha in the worst-case scenario and δ≥β\delta\geq\beta in the instance-dependent scenario) that decays exponentially with a polynomial TT term. Such decaying rate is proved to be best achievable. Moreover, we discover an intrinsic gap of the optimal tail rate under the instance-dependent scenario between whether the time horizon TT is known a priori or not. Interestingly, when it comes to the worst-case scenario, this gap disappears. Finally, we extend our proposed policy design to (1) a stochastic multi-armed bandit setting with non-stationary baseline rewards, and (2) a stochastic linear bandit setting. Our results reveal insights on the trade-off between regret expectation and regret tail risk for both worst-case and instance-dependent scenarios, indicating that more sub-optimality and inconsistency leave space for more light-tailed risk of incurring a large regret, and that knowing the planning horizon in advance can make a difference on alleviating tail risks

    Learning Spiking Neural Systems with the Event-Driven Forward-Forward Process

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    We develop a novel credit assignment algorithm for information processing with spiking neurons without requiring feedback synapses. Specifically, we propose an event-driven generalization of the forward-forward and the predictive forward-forward learning processes for a spiking neural system that iteratively processes sensory input over a stimulus window. As a result, the recurrent circuit computes the membrane potential of each neuron in each layer as a function of local bottom-up, top-down, and lateral signals, facilitating a dynamic, layer-wise parallel form of neural computation. Unlike spiking neural coding, which relies on feedback synapses to adjust neural electrical activity, our model operates purely online and forward in time, offering a promising way to learn distributed representations of sensory data patterns with temporal spike signals. Notably, our experimental results on several pattern datasets demonstrate that the even-driven forward-forward (ED-FF) framework works well for training a dynamic recurrent spiking system capable of both classification and reconstruction

    Plateau-reduced Differentiable Path Tracing

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    Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee useful update steps in an optimization. Instead, inverse rendering might not converge due to inherent plateaus, i.e., regions of zero gradient, in the objective function. We propose to alleviate this by convolving the high-dimensional rendering function that maps scene parameters to images with an additional kernel that blurs the parameter space. We describe two Monte Carlo estimators to compute plateau-free gradients efficiently, i.e., with low variance, and show that these translate into net-gains in optimization error and runtime performance. Our approach is a straightforward extension to both black-box and differentiable renderers and enables optimization of problems with intricate light transport, such as caustics or global illumination, that existing differentiable renderers do not converge on.Comment: Accepted to CVPR 2023. Project page and interactive demos at https://mfischer-ucl.github.io/prdpt

    Model Diagnostics meets Forecast Evaluation: Goodness-of-Fit, Calibration, and Related Topics

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    Principled forecast evaluation and model diagnostics are vital in fitting probabilistic models and forecasting outcomes of interest. A common principle is that fitted or predicted distributions ought to be calibrated, ideally in the sense that the outcome is indistinguishable from a random draw from the posited distribution. Much of this thesis is centered on calibration properties of various types of forecasts. In the first part of the thesis, a simple algorithm for exact multinomial goodness-of-fit tests is proposed. The algorithm computes exact pp-values based on various test statistics, such as the log-likelihood ratio and Pearson\u27s chi-square. A thorough analysis shows improvement on extant methods. However, the runtime of the algorithm grows exponentially in the number of categories and hence its use is limited. In the second part, a framework rooted in probability theory is developed, which gives rise to hierarchies of calibration, and applies to both predictive distributions and stand-alone point forecasts. Based on a general notion of conditional T-calibration, the thesis introduces population versions of T-reliability diagrams and revisits a score decomposition into measures of miscalibration, discrimination, and uncertainty. Stable and efficient estimators of T-reliability diagrams and score components arise via nonparametric isotonic regression and the pool-adjacent-violators algorithm. For in-sample model diagnostics, a universal coefficient of determination is introduced that nests and reinterprets the classical R2R^2 in least squares regression. In the third part, probabilistic top lists are proposed as a novel type of prediction in classification, which bridges the gap between single-class predictions and predictive distributions. The probabilistic top list functional is elicited by strictly consistent evaluation metrics, based on symmetric proper scoring rules, which admit comparison of various types of predictions

    Forested buffers in agricultural landscapes : mitigation effects on stream–riparian meta-ecosystems

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    Stream–riparian meta-ecosystems are strongly connected through exchanges of energy, material and organisms. Land use can disrupt ecological connectivity by affecting community composition directly and/or indirectly by altering the instream and riparian habitats that support biological structure and function. Although forested riparian buffers are increasingly used as a management intervention, our understanding of their effects on the functioning of stream–riparian metaecosystems is limited. This study assessed patterns in the longitudinal and lateral profiles of streams in modified landscapes across Europe and Sweden using a pairedreach approach, with upstream unbuffered reaches lacking woody riparian vegetation and with downstream reaches having well-developed forested buffers. The presence of buffers was positively associated with stream ecological status as well as important attributes, which included instream shading and the provision of suitable habitats for instream and riparian communities, thus supporting more aquatic insects (especially EPT taxa). Emergence of aquatic insects is particularly important because they mediate reciprocal flows of subsidies into terrestrial systems. Results of fatty acid analysis and prey DNA from spiders further supported the importance of buffers in providing more aquatic-derived quality food (i.e. essential fatty acids) for riparian spiders. Findings presented in this thesis show that buffers contribute to the strengthening of cross-ecosystem connectivity and have the potential to affect a wide range of consumers in modified landscapes
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