7,991 research outputs found

    Benefits of Alaska Native Corporations and the SBA 8(a) Program to Alaska Natives and Alaska

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    Senator Begich’s office asked ISER for assistance assembling information to document the social and economic status of Alaska Natives and the benefits of the 8(a) program. His purpose is to brief Missouri Senator McCaskill and her committee which is reviewing the status of ANC contracts awarded under SBA’s 8(a) program. This review was triggered by a 2006 GAO report recommending increased SBA oversight to 8(a) contracting activity. Highlights of the GAO report are provided in Tab A.1; a letter dated May 15, 2009, from Senators Begich and Murkowski to Sentaor McCaskill, outlining their concerns is provided in Tab A.2. As the Congressional Research Service report (Tab A.3) explains, the Small Business Administration’s 8(a) program targeting socially and economically disadvantaged individuals was operating under executive authority from about 1970, and under statutory authority starting in 1978. A series of amendments from 1986 to 1992 recognized Alaska Native Corporations (ANCs) as socially and economically disadvantaged for purposes of program eligibility, exempted them from limitations on the number of qualifying subsidiaries, from some restrictions on size and minimum time in business, and from the ceiling on amounts for sole-source contracts. Between 1988 and 2005, the number of 8(a) qualified ANC subsidiaries grew from one to 154 subsidiaries owned by 49 ANCs. The dollar amount of 8(a) contracts to ANCs grew from 265millioninFY2000to265 million in FY 2000 to 1.1 billion in 2004, approximately 80 percent of which was in sole-source contracts. (GAO Highlights, Tab A.1) The remainder of this briefing book is divided in three sections. Section 2 addresses changes in the social and economic status of Alaska Natives from 1970--the year before the enactment of the Alaska Native Claims Settlement Act and the subsequent creation of the ANCs--to the present. ISER’s report on the “Status of Alaska Natives 2004” (Tab B.1) finds that despite really significant improvements in social and economic conditions among Alaska Natives, they still lag well behind other Alaskans in employment, income, education, health status and living conditions. A collection of more recent analyses updates the social and economic indicators to 2008. There were many concurrent changes throughout this dynamic period of Alaska’s history and we cannot attribute all the improvements to the ANCs, though it is clear that they play an important catalyst role. In the final part of section 2 we attempt to provide some historical context for understanding the role ANCs have played in improving the well-being of Alaska Natives. Section C. documents the growth in ANCs and their contributions to Alaska Native employment, income, social and cultural programs and wellbeing, and their major contributions to the Alaska economy and society overall. Section D. Looks specifically at the 8(a) program. Although there are a handful of 8(a) firms with large federal contracts, the majority are small, village-based corporations engaged in enterprise development in very challenging conditions. A collection of six case studies illustrate the barriers to business development these small firms face and the critical leverage that 8(a) contracting offers them.Mark BegichIntroduction / Status of Alaska Natives 1970 to 2000 / Benefits from Alaska Native Corporations / Benefits from the 8(a) progra

    Lipschitz Adaptivity with Multiple Learning Rates in Online Learning

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    We aim to design adaptive online learning algorithms that take advantage of any special structure that might be present in the learning task at hand, with as little manual tuning by the user as possible. A fundamental obstacle that comes up in the design of such adaptive algorithms is to calibrate a so-called step-size or learning rate hyperparameter depending on variance, gradient norms, etc. A recent technique promises to overcome this difficulty by maintaining multiple learning rates in parallel. This technique has been applied in the MetaGrad algorithm for online convex optimization and the Squint algorithm for prediction with expert advice. However, in both cases the user still has to provide in advance a Lipschitz hyperparameter that bounds the norm of the gradients. Although this hyperparameter is typically not available in advance, tuning it correctly is crucial: if it is set too small, the methods may fail completely; but if it is taken too large, performance deteriorates significantly. In the present work we remove this Lipschitz hyperparameter by designing new versions of MetaGrad and Squint that adapt to its optimal value automatically. We achieve this by dynamically updating the set of active learning rates. For MetaGrad, we further improve the computational efficiency of handling constraints on the domain of prediction, and we remove the need to specify the number of rounds in advance.Comment: 22 pages. To appear in COLT 201

    Dynamic Ad Allocation: Bandits with Budgets

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    We consider an application of multi-armed bandits to internet advertising (specifically, to dynamic ad allocation in the pay-per-click model, with uncertainty on the click probabilities). We focus on an important practical issue that advertisers are constrained in how much money they can spend on their ad campaigns. This issue has not been considered in the prior work on bandit-based approaches for ad allocation, to the best of our knowledge. We define a simple, stylized model where an algorithm picks one ad to display in each round, and each ad has a \emph{budget}: the maximal amount of money that can be spent on this ad. This model admits a natural variant of UCB1, a well-known algorithm for multi-armed bandits with stochastic rewards. We derive strong provable guarantees for this algorithm

    Second-order Quantile Methods for Experts and Combinatorial Games

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    We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly better on easy data. Two popular ways to formalize such adaptivity are second-order regret bounds and quantile bounds. The underlying notions of 'easy data', which may be paraphrased as "the learning problem has small variance" and "multiple decisions are useful", are synergetic. But even though there are sophisticated algorithms that exploit one of the two, no existing algorithm is able to adapt to both. In this paper we outline a new method for obtaining such adaptive algorithms, based on a potential function that aggregates a range of learning rates (which are essential tuning parameters). By choosing the right prior we construct efficient algorithms and show that they reap both benefits by proving the first bounds that are both second-order and incorporate quantiles

    Fighting Bandits with a New Kind of Smoothness

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    We define a novel family of algorithms for the adversarial multi-armed bandit problem, and provide a simple analysis technique based on convex smoothing. We prove two main results. First, we show that regularization via the \emph{Tsallis entropy}, which includes EXP3 as a special case, achieves the Θ(TN)\Theta(\sqrt{TN}) minimax regret. Second, we show that a wide class of perturbation methods achieve a near-optimal regret as low as O(TNlogN)O(\sqrt{TN \log N}) if the perturbation distribution has a bounded hazard rate. For example, the Gumbel, Weibull, Frechet, Pareto, and Gamma distributions all satisfy this key property.Comment: In Proceedings of NIPS, 201

    PAC-Bayesian Analysis of Martingales and Multiarmed Bandits

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    We present two alternative ways to apply PAC-Bayesian analysis to sequences of dependent random variables. The first is based on a new lemma that enables to bound expectations of convex functions of certain dependent random variables by expectations of the same functions of independent Bernoulli random variables. This lemma provides an alternative tool to Hoeffding-Azuma inequality to bound concentration of martingale values. Our second approach is based on integration of Hoeffding-Azuma inequality with PAC-Bayesian analysis. We also introduce a way to apply PAC-Bayesian analysis in situation of limited feedback. We combine the new tools to derive PAC-Bayesian generalization and regret bounds for the multiarmed bandit problem. Although our regret bound is not yet as tight as state-of-the-art regret bounds based on other well-established techniques, our results significantly expand the range of potential applications of PAC-Bayesian analysis and introduce a new analysis tool to reinforcement learning and many other fields, where martingales and limited feedback are encountered
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