Best-of-Both-Worlds Algorithms for Partial Monitoring

This study considers the partial monitoring problem with $k$-actions and $d$-outcomes and provides the first best-of-both-worlds algorithms, whose regrets are favorably bounded both in the stochastic and adversarial regimes. In particular, we show that for non-degenerate locally observable games, the regret is $O(m^2 k^4 \log(T) \log(k_{\Pi} T) / \Delta_{\min})$ in the stochastic regime and $O(m k^{2/3} \sqrt{T \log(T) \log k_{\Pi}})$ in the adversarial regime, where $T$ is the number of rounds, $m$ is the maximum number of distinct observations per action, $\Delta_{\min}$ is the minimum suboptimality gap, and $k_{\Pi}$ is the number of Pareto optimal actions. Moreover, we show that for globally observable games, the regret is $O(c_{\mathcal{G}}^2 \log(T) \log(k_{\Pi} T) / \Delta_{\min}^2)$ in the stochastic regime and $O((c_{\mathcal{G}}^2 \log(T) \log(k_{\Pi} T))^{1/3} T^{2/3})$ in the adversarial regime, where $c_{\mathcal{G}}$ is a game-dependent constant. We also provide regret bounds for a stochastic regime with adversarial corruptions. Our algorithms are based on the follow-the-regularized-leader framework and are inspired by the approach of exploration by optimization and the adaptive learning rate in the field of online learning with feedback graphs.Comment: 31 page

Stability-penalty-adaptive Follow-the-regularized-leader: Sparsity, Game-dependency, and Best-of-both-worlds

Adaptivity to the difficulties of a problem is a key property in sequential decision-making problems to broaden the applicability of algorithms. Follow-the-Regularized-Leader (FTRL) has recently emerged as one of the most promising approaches for obtaining various types of adaptivity in bandit problems. Aiming to further generalize this adaptivity, we develop a generic adaptive learning rate, called Stability-Penalty-Adaptive (SPA) learning rate for FTRL. This learning rate yields a regret bound jointly depending on stability and penalty of the algorithm, into which the regret of FTRL is typically decomposed. With this result, we establish several algorithms with three types of adaptivity: sparsity, game-dependency, and Best-of-Both-Worlds (BOBW). Sparsity frequently appears in real-world problems. However, existing sparse multi-armed bandit algorithms with $k$-arms assume that the sparsity level $s \leq k$ is known in advance, which is often not the case in real-world scenarios. To address this problem, with the help of the new learning rate framework, we establish $s$-agnostic algorithms with regret bounds of $\tilde{O}(\sqrt{sT})$ in the adversarial regime for $T$ rounds, which matches the existing lower bound up to a logarithmic factor. Meanwhile, BOBW algorithms aim to achieve a near-optimal regret in both the stochastic and adversarial regimes. Leveraging the new adaptive learning rate framework and a novel analysis to bound the variation in FTRL output in response to changes in a regularizer, we establish the first BOBW algorithm with a sparsity-dependent bound. Additionally, we explore partial monitoring and demonstrate that the proposed learning rate framework allows us to achieve a game-dependent bound and the BOBW simultaneously.Comment: 30 page

Information Diffusion and the Role of Central Figures: Experimental Evidence of Network-based Agricultural Extension in Sri Lanka

This study examines the effect of social networks and central figures in networks on information diffusion. Exploiting a government subsidy program and training workshops regarding the fair-trade and organic farming certifications in Sri Lanka, we conducted a randomized experiment to investigate the role of farmers’ social networks and “key farmers” in information transmission to workshop non-participants and their application to the certifications. Key farmers are agricultural village leaders unofficially appointed by local government officials. The estimation results show that key farmers’ involvement in the workshop amplifies information diffusion through social networks. In the treatment villages with key farmers involved, non-participants increase their knowledge of certifications and the likelihood of being a member of the applicant organization when directly connected with key farmers in their networks. Moreover, they are more likely to receive information goods from other peers in the network. However, in the control villages with key farmers uninvolved, direct connections with key farmers and farmers’ networks do not influence the diffusion of information goods and knowledge and participation in the applicant group. These findings suggest that central figures’ involvement is the key to the success of network-based programs.This work was supported by JSPS KAKENHI (grant numbers 19K01627)

Prior learning materials to facilitate “dialogue and inquiry” in remote lectures- Publication of scenarios for the production of instructional videos of artworks for use in flipped classes -

　国立大学法人岡山大学大学院教育学研究科《国吉康雄記念・美術教育研究と地域創生講座》では，美術鑑賞の機会に，対話と探究を用い，アート作品の制作背景を社会課題の解決と関 連づけて考察する手法を開発し，実践している。現在，COVID⊖19の感染拡大を防ぐことを目的としたリモートでの講義プログラムにおいても，この鑑賞手法を用いるため，「反転学習用動画教材」として，岡山県に関係する洋画家ふたりに関する動画を制作した。ここでは，制作のために執筆したシナリオを，反転学習用の動画制作のための作例として公開する

Circadian-regulated expression of a nuclear-encoded plastid σ factor gene (sigA) in wheat seedlings

AbstractThe activity of a light-responsive psbD promoter in plastids is known to be regulated by a circadian clock. However, the mechanism of the circadian regulation of the psbD light-responsive promotor, which is recognized by an Escherichia coli-type RNA polymerase, is not yet known. We examined the time course of mRNA accumulation of two E. coli-type RNA polymerase subunit genes, sigA and rpoA, under a continuous light condition after 12 h light/12 h dark entrainment. Accumulation of the sigA mRNA was found to be regulated by a circadian clock, while rpoA mRNA did not show any significant oscillation throughout the experiment