An efficient algorithm for learning with semi-bandit feedback

We consider the problem of online combinatorial optimization under semi-bandit feedback. The goal of the learner is to sequentially select its actions from a combinatorial decision set so as to minimize its cumulative loss. We propose a learning algorithm for this problem based on combining the Follow-the-Perturbed-Leader (FPL) prediction method with a novel loss estimation procedure called Geometric Resampling (GR). Contrary to previous solutions, the resulting algorithm can be efficiently implemented for any decision set where efficient offline combinatorial optimization is possible at all. Assuming that the elements of the decision set can be described with d-dimensional binary vectors with at most m non-zero entries, we show that the expected regret of our algorithm after T rounds is O(m sqrt(dT log d)). As a side result, we also improve the best known regret bounds for FPL in the full information setting to O(m^(3/2) sqrt(T log d)), gaining a factor of sqrt(d/m) over previous bounds for this algorithm.Comment: submitted to ALT 201

Laplace's rule of succession in information geometry

Laplace's "add-one" rule of succession modifies the observed frequencies in a sequence of heads and tails by adding one to the observed counts. This improves prediction by avoiding zero probabilities and corresponds to a uniform Bayesian prior on the parameter. The canonical Jeffreys prior corresponds to the "add-one-half" rule. We prove that, for exponential families of distributions, such Bayesian predictors can be approximated by taking the average of the maximum likelihood predictor and the \emph{sequential normalized maximum likelihood} predictor from information theory. Thus in this case it is possible to approximate Bayesian predictors without the cost of integrating or sampling in parameter space

IR ion spectroscopy in a combined approach with MS/MS and IM-MS to discriminate epimeric anthocyanin glycosides (cyanidin 3-O-glucoside and -galactoside)

Anthocyanins are widespread in plants and flowers, being responsible for their different colouring. Two representative members of this family have been selected, cyanidin 3-O-β-glucopyranoside and 3-O-β-galactopyranoside, and probed by mass spectrometry based methods, testing their performance in discriminating between the two epimers. The native anthocyanins, delivered into the gas phase by electrospray ionization, display a comparable drift time in ion mobility mass spectrometry (IM-MS) and a common fragment, corresponding to loss of the sugar moiety, in their collision induced dissociation (CID) pattern. However, the IR multiple photon dissociation (IRMPD) spectra in the fingerprint range show a feature particularly evident in the case of the glucoside. This signature is used to identify the presence of cyanidin 3-O-β-glucopyranoside in a natural extract of pomegranate. In an effort to increase any differentiation between the two epimers, aluminum complexes were prepared and sampled for elemental composition by FT-ICR-MS. CID experiments now display an extensive fragmentation pattern, showing few product ions peculiar to each species. More noteworthy is the IRMPD behavior in the OH stretching range showing significant differences in the spectra of the two epimers. DFT calculations allow to interpret the observed distinct bands due to a varied network of hydrogen bonding and relative conformer stability

On the Prior Sensitivity of Thompson Sampling

The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algorithm is that it allows domain knowledge to be conveniently encoded as a prior distribution to balance exploration and exploitation more effectively. While it is generally believed that the algorithm's regret is low (high) when the prior is good (bad), little is known about the exact dependence. In this paper, we fully characterize the algorithm's worst-case dependence of regret on the choice of prior, focusing on a special yet representative case. These results also provide insights into the general sensitivity of the algorithm to the choice of priors. In particular, with $p$ being the prior probability mass of the true reward-generating model, we prove $O(\sqrt{T/p})$ and $O(\sqrt{(1-p)T})$ regret upper bounds for the bad- and good-prior cases, respectively, as well as \emph{matching} lower bounds. Our proofs rely on the discovery of a fundamental property of Thompson Sampling and make heavy use of martingale theory, both of which appear novel in the literature, to the best of our knowledge.Comment: Appears in the 27th International Conference on Algorithmic Learning Theory (ALT), 201

The development, educational stratification and decomposition of mothers' and fathers' childcare time in Germany: an update for 2001-2013

"This study updates empirical knowledge about the development,(the educational stratification, and the decomposition of mothers' and fathers' childcare time in Germany with the most recent time use data. Using time series data from the German Time Use Study 2001/2002 and 2012/ 2013, we analyze time budgets for total childcare and six specific childcare activities on weekdays and weekends and estimate OLS regressions and Oaxaca decompositions. The study found that total childcare time has increased for mothers and fathers between 2001 and 2013 and that this change is predominantly due to increased time for basic childcare. It also found consistent evidence of an education gradient only for reading time with children. If there is significant change of time budgets between 2001 and 2013, this change seems to be driven by behavioral change rather than changing demographics. Our empirical findings on childcare time in Germany do not provide evidence of dynamics and stratification but rather of stability and similarity across parents’ educational levels. Besides the updates on German parents' development, stratification and decomposition of time use for childcare, these analyses show that change in total childcare is not due to a proportional change over all single activities but due to changes in a few activities only." (author's abstract)"Diese Studie aktualisiert das empirische Wissen über die Entwicklung, die Bildungsstratifizierung und die Dekomposition der Zeitverwendung von Müttern und Vätern für Kinderbetreuung mit den aktuellen Zeitbudgetdaten für Deutschland. Auf Basis der der letzten beiden Erhebungen der Deutschen Zeitverwendungsstudie 2001/2002 und 2012/2013 werden die Zeitbudgets für die Gesamtzeit für Kinderbetreuung sowie sechs Einzeltätigkeiten mit OLS-Regressionen und Oaxaca- Dekompositionen untersucht. Die Studie zeigt, dass die Zeit für Kinderbetreuung von Müttern und Vätern zwischen 2001 und 2013 angestiegen ist, es einen Bildungsgradienten für Vorlesen gibt und signifikante Veränderungen in den Zeitbudgets nicht auf Kompositionsveränderung der Bevölkerung zurückgeführt werden können. Insgesamt belegt die Studie weniger die Dynamik als vielmehr die Stabilität und die geringe Bildungsdifferenzierung der Zeitverwendung für Kinderbetreuung. Darüber hinaus wird gezeigt, dass die Veränderungen in der Gesamtzeit für Kinderbetreuung nicht auf proportionale Veränderungen in allen, sondern nur auf Veränderungen in wenigen Einzeltätigkeiten zurückgeführt werden können." (Autorenreferat

Jornalismo e comunicação na aprendizagem do mundo

info:eu-repo/semantics/publishedVersio

Time series prediction via aggregation : an oracle bound including numerical cost

We address the problem of forecasting a time series meeting the Causal Bernoulli Shift model, using a parametric set of predictors. The aggregation technique provides a predictor with well established and quite satisfying theoretical properties expressed by an oracle inequality for the prediction risk. The numerical computation of the aggregated predictor usually relies on a Markov chain Monte Carlo method whose convergence should be evaluated. In particular, it is crucial to bound the number of simulations needed to achieve a numerical precision of the same order as the prediction risk. In this direction we present a fairly general result which can be seen as an oracle inequality including the numerical cost of the predictor computation. The numerical cost appears by letting the oracle inequality depend on the number of simulations required in the Monte Carlo approximation. Some numerical experiments are then carried out to support our findings

Espaço cívico : expressões de liberdade e criatividade

info:eu-repo/semantics/publishedVersio