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  1. Book ; Thesis: Extrazelluläres S100A1

    Rohde, David

    ein neuer Regulator der Funktion kardialer Fibroblasten nach Myokardinfarkt

    2011  

    Author's details vorgelegt von David Rohde
    Language German
    Size 117 Bl. : Ill., graph. Darst.
    Publishing country Germany
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Heidelberg, Univ., Diss., 2012
    HBZ-ID HT017433545
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2012 E 1761 order for loan Magazin

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  2. Article ; Online: Bad company: monocytes in HIV and atherosclerosis.

    Rohde, David / Nahrendorf, Matthias

    Cardiovascular research

    2021  Volume 117, Issue 4, Page(s) 993–994

    MeSH term(s) Antiretroviral Therapy, Highly Active ; Atherosclerosis ; HIV Infections/drug therapy ; Humans ; Monocytes
    Language English
    Publishing date 2021-03-07
    Publishing country England
    Document type Editorial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 80340-6
    ISSN 1755-3245 ; 0008-6363
    ISSN (online) 1755-3245
    ISSN 0008-6363
    DOI 10.1093/cvr/cvab058
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 565: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article ; Online: An Optimized Deconvolution Algorithm for Energy-Dispersive X-ray Spectroscopy.

    Klus, Jakub / Seddio, Stephen M / Rohde, David B / Hlavenka, Petr

    Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

    2023  Volume 29, Issue Supplement_1, Page(s) 231–232

    Language English
    Publishing date 2023-08-23
    Publishing country England
    Document type Journal Article
    ZDB-ID 1385710-1
    ISSN 1435-8115 ; 1431-9276
    ISSN (online) 1435-8115
    ISSN 1431-9276
    DOI 10.1093/micmic/ozad067.103
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Fast Slate Policy Optimization

    Sakhi, Otmane / Rohde, David / Chopin, Nicolas

    Going Beyond Plackett-Luce

    2023  

    Abstract: An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When ... ...

    Abstract An increasingly important building block of large scale machine learning systems is based on returning slates; an ordered lists of items given a query. Applications of this technology include: search, information retrieval and recommender systems. When the action space is large, decision systems are restricted to a particular structure to complete online queries quickly. This paper addresses the optimization of these large scale decision systems given an arbitrary reward function. We cast this learning problem in a policy optimization framework and propose a new class of policies, born from a novel relaxation of decision functions. This results in a simple, yet efficient learning algorithm that scales to massive action spaces. We compare our method to the commonly adopted Plackett-Luce policy class and demonstrate the effectiveness of our approach on problems with action space sizes in the order of millions.

    Comment: Transactions on Machine Learning Research
    Keywords Computer Science - Machine Learning ; Computer Science - Information Retrieval ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2023-08-03
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Clonal and diverse: revisiting cardiac endothelial cells after myocardial infarction.

    Rohde, David / Nahrendorf, Matthias

    European heart journal

    2019  Volume 40, Issue 30, Page(s) 2521–2522

    MeSH term(s) Cells, Cultured ; Endothelial Cells ; Gene Expression Profiling ; Humans ; Myocardial Infarction ; Neovascularization, Pathologic
    Language English
    Publishing date 2019-06-04
    Publishing country England
    Document type Editorial ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Comment
    ZDB-ID 603098-1
    ISSN 1522-9645 ; 0195-668X
    ISSN (online) 1522-9645
    ISSN 0195-668X
    DOI 10.1093/eurheartj/ehz375
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1563: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MO 640: Show issues

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  6. Book ; Online: Exponential Smoothing for Off-Policy Learning

    Aouali, Imad / Brunel, Victor-Emmanuel / Rohde, David / Korba, Anna

    2023  

    Abstract: Off-policy learning (OPL) aims at finding improved policies from logged bandit data, often by minimizing the inverse propensity scoring (IPS) estimator of the risk. In this work, we investigate a smooth regularization for IPS, for which we derive a two- ... ...

    Abstract Off-policy learning (OPL) aims at finding improved policies from logged bandit data, often by minimizing the inverse propensity scoring (IPS) estimator of the risk. In this work, we investigate a smooth regularization for IPS, for which we derive a two-sided PAC-Bayes generalization bound. The bound is tractable, scalable, interpretable and provides learning certificates. In particular, it is also valid for standard IPS without making the assumption that the importance weights are bounded. We demonstrate the relevance of our approach and its favorable performance through a set of learning tasks. Since our bound holds for standard IPS, we are able to provide insight into when regularizing IPS is useful. Namely, we identify cases where regularization might not be needed. This goes against the belief that, in practice, clipped IPS often enjoys favorable performance than standard IPS in OPL.

    Comment: ICML 2023 (Oral and Poster)
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2023-05-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Fast Offline Policy Optimization for Large Scale Recommendation

    Sakhi, Otmane / Rohde, David / Gilotte, Alexandre

    2022  

    Abstract: Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of these systems can be achieved by a relaxation of the discrete problem ... ...

    Abstract Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of these systems can be achieved by a relaxation of the discrete problem resulting in policy learning or REINFORCE style learning algorithms. Unfortunately, this relaxation step requires computing a sum over the entire catalogue making the complexity of the evaluation of the gradient (and hence each stochastic gradient descent iterations) linear in the catalogue size. This calculation is untenable in many real world examples such as large catalogue recommender systems, severely limiting the usefulness of this method in practice. In this paper, we derive an approximation of these policy learning algorithms that scale logarithmically with the catalogue size. Our contribution is based upon combining three novel ideas: a new Monte Carlo estimate of the gradient of a policy, the self normalised importance sampling estimator and the use of fast maximum inner product search at training time. Extensive experiments show that our algorithm is an order of magnitude faster than naive approaches yet produces equally good policies.

    Comment: Accepted at AAAI 2023
    Keywords Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence ; Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2022-08-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Welfare-Optimized Recommender Systems

    Heymann, Benjamin / Vasile, Flavian / Rohde, David

    2022  

    Abstract: We present a recommender system based on the Random Utility Model. Online shoppers are modeled as rational decision makers with limited information, and the recommendation task is formulated as the problem of optimally enriching the shopper's awareness ... ...

    Abstract We present a recommender system based on the Random Utility Model. Online shoppers are modeled as rational decision makers with limited information, and the recommendation task is formulated as the problem of optimally enriching the shopper's awareness set. Notably, the price information and the shopper's Willingness-To-Pay play crucial roles. Furthermore, to better account for the commercial nature of the recommendation, we unify the retailer and shoppers' contradictory objectives into a single welfare metric, which we propose as a new recommendation goal. We test our framework on synthetic data and show its performance in a wide range of scenarios. This new framework, that was absent from the Recommender System literature, opens the door to Welfare-Optimized Recommender Systems, couponing, and price optimization.
    Keywords Computer Science - Computer Science and Game Theory ; Mathematics - Optimization and Control
    Publishing date 2022-06-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book: Beyond war

    Rohde, David

    reimagining American influence in a new Middle East

    2013  

    Abstract: This book distills eleven years of expert reporting for The New York Times, Reuters, and The Atlantic Monthly into a clarion call for change. An incisive look at the evolving nature of war, Rohde exposes how a dysfunctional Washington squandered ... ...

    Author's details David Rohde
    Abstract "This book distills eleven years of expert reporting for The New York Times, Reuters, and The Atlantic Monthly into a clarion call for change. An incisive look at the evolving nature of war, Rohde exposes how a dysfunctional Washington squandered billions on contractors in Iraq and Afghanistan, neglected its true allies in the war on terror and failed to employ its most potent nonmilitary weapons: American consumerism, technology, and investment. Rohde then surveys post-Arab Spring Tunisia, Turkey, and Egypt, and finds a yearning for American technology, trade, and education. He argues that only Muslim moderates, not Americans, can eradicate militancy. For readers of Steve Coll, Tom Ricks, and Ahmed Rashid, Beyond War shows how the failed American effort to back moderate Muslims since 9/11 can be salvaged"--
    Keywords Middle East ; United States
    Language English
    Size 221 S
    Publisher Viking
    Publishing place New York
    Document type Book
    Note Includes bibliographical references and index
    ISBN 9780670026449 ; 0670026441
    Database Former special subject collection: coastal and deep sea fishing

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  10. Book ; Online: Causal inference with Bayes rule

    Lattimore, Finnian / Rohde, David

    2019  

    Abstract: The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do-calculus is required has been hotly debated, ...

    Abstract The concept of causality has a controversial history. The question of whether it is possible to represent and address causal problems with probability theory, or if fundamentally new mathematics such as the do-calculus is required has been hotly debated, In this paper we demonstrate that, while it is critical to explicitly model our assumptions on the impact of intervening in a system, provided we do so, estimating causal effects can be done entirely within the standard Bayesian paradigm. The invariance assumptions underlying causal graphical models can be encoded in ordinary Probabilistic graphical models, allowing causal estimation with Bayesian statistics, equivalent to the do-calculus.

    Comment: 5 pages. arXiv admin note: substantial text overlap with arXiv:1906.07125
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Publishing date 2019-10-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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