Pure Exploration with Multiple Correct Answers

We determine the sample complexity of pure exploration bandit problems with multiple good answers. We derive a lower bound using a new game equilibrium argument. We show how continuity and convexity properties of single-answer problems ensures that the Track-and-Stop algorithm has asymptotically optimal sample complexity. However, that convexity is lost when going to the multiple-answer setting. We present a new algorithm which extends Track-and-Stop to the multiple-answer case and has asymptotic sample complexity matching the lower bound

Structure Adaptive Algorithms for Stochastic Bandits

We study reward maximisation in a wide class of structured stochastic multi-armed bandit problems, where the mean rewards of arms satisfy some given structural constraints, e.g. linear, unimodal, sparse, etc. Our aim is to develop methods that are flexible (in that they easily adapt to different structures), powerful (in that they perform well empirically and/or provably match instance-dependent lower bounds) and efficient in that the per-round computational burden is small. We develop asymptotically optimal algorithms from instance-dependent lower-bounds using iterative saddle-point solvers. Our approach generalises recent iterative methods for pure exploration to reward maximisation, where a major challenge arises from the estimation of the sub-optimality gaps and their reciprocals. Still we manage to achieve all the above desiderata. Notably, our technique avoids the computational cost of the full-blown saddle point oracle employed by previous work, while at the same time enabling finite-time regret bounds. Our experiments reveal that our method successfully leverages the structural assumptions, while its regret is at worst comparable to that of vanilla UCB.Comment: 10+18 pages. To be published in the proceedings of ICML 202

Combining hydrology and mosquito population models to identify the drivers of Rift Valley fever emergence in semi-arid regions of West Africa

BACKGROUND: Rift Valley fever (RVF) is a vector-borne viral zoonosis of increasing global importance. RVF virus (RVFV) is transmitted either through exposure to infected animals or through bites from different species of infected mosquitoes, mainly of Aedes and Culex genera. These mosquitoes are very sensitive to environmental conditions, which may determine their presence, biology, and abundance. In East Africa, RVF outbreaks are known to be closely associated with heavy rainfall events, unlike in the semi-arid regions of West Africa where the drivers of RVF emergence remain poorly understood. The assumed importance of temporary ponds and rainfall temporal distribution therefore needs to be investigated. METHODOLOGY/PRINCIPAL FINDINGS: A hydrological model is combined with a mosquito population model to predict the abundance of the two main mosquito species (Aedes vexans and Culex poicilipes) involved in RVFV transmission in Senegal. The study area is an agropastoral zone located in the Ferlo Valley, characterized by a dense network of temporary water ponds which constitute mosquito breeding sites. The hydrological model uses daily rainfall as input to simulate variations of pond surface areas. The mosquito population model is mechanistic, considers both aquatic and adult stages and is driven by pond dynamics. Once validated using hydrological and entomological field data, the model was used to simulate the abundance dynamics of the two mosquito species over a 43-year period (1961–2003). We analysed the predicted dynamics of mosquito populations with regards to the years of main outbreaks. The results showed that the main RVF outbreaks occurred during years with simultaneous high abundances of both species. CONCLUSION/SIGNIFICANCE: Our study provides for the first time a mechanistic insight on RVFV transmission in West Africa. It highlights the complementary roles of Aedes vexans and Culex poicilipes mosquitoes in virus transmission, and recommends the identification of rainfall patterns favourable for RVFV amplification

Eigenvector localization as a tool to study small communities in online social networks

We present and discuss a mathematical procedure for identification of small "communities" or segments within large bipartite networks. The procedure is based on spectral analysis of the matrix encoding network structure. The principal tool here is localization of eigenvectors of the matrix, by means of which the relevant network segments become visible. We exemplified our approach by analyzing the data related to product reviewing on Amazon.com. We found several segments, a kind of hybrid communities of densely interlinked reviewers and products, which we were able to meaningfully interpret in terms of the type and thematic categorization of reviewed items. The method provides a complementary approach to other ways of community detection, typically aiming at identification of large network modules

Correlated dynamics in egocentric communication networks

We investigate the communication sequences of millions of people through two different channels and analyze the fine grained temporal structure of correlated event trains induced by single individuals. By focusing on correlations between the heterogeneous dynamics and the topology of egocentric networks we find that the bursty trains usually evolve for pairs of individuals rather than for the ego and his/her several neighbors thus burstiness is a property of the links rather than of the nodes. We compare the directional balance of calls and short messages within bursty trains to the average on the actual link and show that for the trains of voice calls the imbalance is significantly enhanced, while for short messages the balance within the trains increases. These effects can be partly traced back to the technological constrains (for short messages) and partly to the human behavioral features (voice calls). We define a model that is able to reproduce the empirical results and may help us to understand better the mechanisms driving technology mediated human communication dynamics.Comment: 7 pages, 6 figure

De l'inefficacité du réseau social : des liens sociaux non mobilisés chez les patients atteints de cancer

International audienceDe nombreuses études sur les réseaux sociaux portent sur la question des ressources auxquelles un individu peut avoir accès à travers ses relations sociales. En s'intéressant aux réseaux qui ont « fonctionné », elles tentent alors de spécifier les facteurs ayant permis l'accès aux ressources. Mais peu de travaux se sont penchés sur les réseaux ayant échoué à fournir à l'individu le bien ou le service qu'il recherchait. Cet article se propose de discuter de ces réseaux « inefficaces » à travers le cas de patients atteints de cancer recherchant des informations relatives à leur pathologie. Les résultats indiquent que les causes de l'inefficacité du réseau sont à rechercher dans la non mobilisation des liens de la part des malades rencontrés. Un certain nombre de contraintes pèsent sur ces liens empêchant ainsi leur mobilisation

A social network approach to examine K-12 educational leaders’ influence on information diffusion on Twitter

This study investigated the relationship between the leader’s gender, leadership position, Twitter use, and influence on information diffusion in the communication network on Twitter. We collected the 30,200 latest tweets of 151 active Twitter users who held educational leadership positions. Results of social network analysis and multiple regression analyses suggest a gender inequality in the leader’s influence on information diffusion in the network. Findings also indicate no significant relationship between leadership position (district vs. building) and a leader\u27s influence in the network. Moreover, Twitter following was positively associated with the leader’s influence in the network, whereas the number of followers, weekly tweets, and the time of Twitter account creation did not predict the leader’s influence. Practical implications on how leaders use Twitter to disseminate information are discussed