Collaborative hyperparameter tuning

International audienceHyperparameter learning has traditionally been a manual task because of the limited number of trials. Today's computing infrastructures allow bigger evaluation budgets, thus opening the way for algorithmic approaches. Recently, surrogate-based optimization was successfully applied to hyperparameter learning for deep belief networks and to WEKA classifiers. The methods combined brute force computational power with model building about the behavior of the error function in the hyperparameter space, and they could significantly improve on manual hyperparameter tuning. What may make experienced practitioners even better at hyperparameter optimization is their ability to generalize across similar learning problems. In this paper, we propose a generic method to incorporate knowledge from previous experiments when simultaneously tuning a learning algorithm on new problems at hand. To this end, we combine surrogate-based ranking and optimization techniques for surrogate-based collaborative tuning (SCoT). We demonstrate SCoT in two experiments where it outperforms standard tuning techniques and single-problem surrogate-based optimization

Extreme compass and Dynamic Multi-Armed Bandits for Adaptive Operator Selection

The goal of adaptive operator selection is the on-line control of the choice of variation operators within evolutionary algorithms. The control process is based on two main components, the credit assignment, that defines the reward that will be used to evaluate the quality of an operator after it has been applied, and the operator selection mechanism, that selects one operator based on some operators qualities. Two previously developed adaptive operator selection methods are combined here: Compass evaluates the performance of operators by considering not only the fitness improvements from parent to offspring, but also the way they modify the diversity of the population, and their execution time; dynamic multi-armed bandit proposes a selection strategy based on the well-known UCB algorithm, achieving a compromise between exploitation and exploration, while nevertheless quickly adapting to changes. Tests with the proposed method, called ExCoDyMAB, are carried out using several hard instances of the satisfiability problem (SAT). Results show the good synergetic effect of combining both approaches

Investigating the association of rs2910164 with cancer predisposition in an Irish cohort.

IntroductionMicroRNAs (miRNAs) are small noncoding RNA molecules that exert post-transcriptional effects on gene expression by binding with cis-regulatory regions in target messenger RNA (mRNA). Polymorphisms in genes encoding miRNAs or in miRNA-mRNA binding sites confer deleterious epigenetic effects on cancer risk. miR-146a has a role in inflammation and may have a role as a tumour suppressor. The polymorphism rs2910164 in the MIR146A gene encoding pre-miR-146a has been implicated in several inflammatory pathologies, including cancers of the breast and thyroid, although evidence for the associations has been conflicting in different populations. We aimed to further investigate the association of this variant with these two cancers in an Irish cohort.MethodsThe study group comprised patients with breast cancer (BC), patients with differentiated thyroid cancer (DTC) and unaffected controls. Germline DNA was extracted from blood or from saliva collected using the DNA Genotek Oragene 575 collection kit, using crystallisation precipitation, and genotyped using TaqMan-based PCR. Data were analysed using SPSS, v22.ResultsThe total study group included 1516 participants. This comprised 1386 Irish participants; 724 unaffected individuals (controls), 523 patients with breast cancer (BC), 136 patients with differentiated thyroid cancer (DTC) and three patients with dual primary breast and thyroid cancer. An additional cohort of 130 patients with DTC from the South of France was also genotyped for the variant. The variant was detected with a minor allele frequency (MAF) of 0.19 in controls, 0.22 in BC and 0.27 and 0.26 in DTC cases from Ireland and France, respectively. The variant was not significantly associated with BC (per allele odds ratio = 1.20 (0.98-1.46), P  = 0.07), but was associated with DTC in Irish patients (per allele OR = 1.59 (1.18-2.14), P = 0.002).ConclusionThe rs2910164 variant in MIR146A is significantly associated with DTC, but is not significantly associated with BC in this cohort

Scaling Analysis of Affinity Propagation

We analyze and exploit some scaling properties of the Affinity Propagation (AP) clustering algorithm proposed by Frey and Dueck (2007). First we observe that a divide and conquer strategy, used on a large data set hierarchically reduces the complexity ${\cal O}(N^2)$ to ${\cal O}(N^{(h+2)/(h+1)})$, for a data-set of size $N$ and a depth $h$ of the hierarchical strategy. For a data-set embedded in a $d$-dimensional space, we show that this is obtained without notably damaging the precision except in dimension $d=2$. In fact, for $d$ larger than 2 the relative loss in precision scales like $N^{(2-d)/(h+1)d}$. Finally, under some conditions we observe that there is a value $s^*$ of the penalty coefficient, a free parameter used to fix the number of clusters, which separates a fragmentation phase (for $s<s^*$) from a coalescent one (for $s>s^*$) of the underlying hidden cluster structure. At this precise point holds a self-similarity property which can be exploited by the hierarchical strategy to actually locate its position. From this observation, a strategy based on \AP can be defined to find out how many clusters are present in a given dataset.Comment: 28 pages, 14 figures, Inria research repor

Factors predictive of lymph node metastasis in the follicular variant of papillary thyroid carcinoma

BACKGROUND: The treatment of papillary thyroid carcinomas larger than 1 cm usually consists of total thyroidectomy and central lymph node dissection (LND). In patients with the follicular variant of papillary thyroid carcinoma (FVPTC), preoperative cytology and intraoperative frozen-section analysis cannot always establish the diagnosis. The aim of this study was to evaluate predictive factors for lymph node metastasis in patients with FVPTC and to identify patients who might benefit from LND. METHODS: The study included patients with FVPTC treated by total thyroidectomy and LND between 2000 and 2010 in four departments. When fewer than six non-involved lymph nodes were removed, the patient was excluded from the analysis. RESULTS: Some 199 patients were included. The median tumour size was 17 (range 1-85) mm, and tumours were classified as T1a in 28 patients, T1b in 40, T2 in 53, and T3 in 78. Eighty-one patients (40·7 per cent) had lymph node metastasis (51 classified as N1a and 30 as N1b). Four risk factors were predictive of lymph node metastasis in the multivariable analysis: multifocality (odds ratio (OR) 2·36, 95 per cent confidence interval 1·15 to 4·86), angiolymphatic invasion (OR 3·67, 1·01 to 13·36), absence of tumour capsule (OR 3·00, 1·47 to 6·14) and tumour involvement of perithyroid tissue (OR 3·89, 1·85 to 8·18). The rate of lymph node metastasis varied between 14 and 94 per cent depending on the presence of risk factors. CONCLUSION: The rate of lymph node metastasis in patients with FVPTC varies widely according to the presence or absence of predictive risk factors

Using Datamining Techniques to Help Metaheuristics: A Short Survey

International audienceHybridizing metaheuristic approaches becomes a common way to improve the efficiency of optimization methods. Many hybridizations deal with the combination of several optimization methods. In this paper we are interested in another type of hybridization, where datamining approaches are combined within an optimization process. Hence, we propose to study the interest of combining metaheuristics and datamining through a short survey that enumerates the different opportunities of such combinations based on literature examples

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo