The problem of vocabulary gap among students in the early years of school, and the resulting impact on school success have received significant attention in the past [3-5, 18, 20, 23]. Early introduction of vocabulary through either direct or indirect instruction helps children learn to read well and forms a strong foundation for literacy, which in turn helps children in accelerated reading to learn. Importantly, while reading new texts, children tend to connect the words they are familiar with to the words exposed in the texts; hence, greater and diverse vocabulary leads to better comprehension of the texts being read. Given the enormity of vocabulary in English language (and most languages in general), new word acquisition is an ongoing process for many years, and 1 sometimes is even lifelong for many people. However, the highest rate of vocabulary development happens in the early years, and teachers in elementary schools focus (often in their own ways, since no universally standardized word lists or procedures exist) a nontrivial amount of time in introducing words to children through both direct and implicit instruction.

This paper studies bandit learning problems with delayed feedback, which included multi-armed bandit (MAB) and bandit convex optimization (BCO). Given only function value information (a.k.a. bandit feedback), algorithms for both MAB and BCO typically rely on (possibly randomized) gradient estimators based on function values, and then feed them into well-studied gradient-based algorithms. Different from existing works however, the setting considered here is more challenging, where the bandit feedback is not only delayed but also the presence of its delay is not revealed to the learner. Existing algorithms for delayed MAB and BCO become intractable in this setting. To tackle such challenging settings, DEXP3 and DBGD have been developed for MAB and BCO, respectively. Leveraging a unified analysis framework, it is established that both DEXP3 and DBGD guarantee an ${\cal O}\big( \sqrt{T+D} \big)$ regret over $T$ time slots with $D$ being the overall delay accumulated over slots. The new regret bounds match those in full information settings.

We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model training equally well and to quickly adapt to concept drifts. This leads to a reduction of communication by an order of magnitude compared to periodically communicating state-of-the-art approaches. Moreover, we derive a communication bound that scales well with the hardness of the serialized learning problem. The reduction in communication comes at almost no cost, as the predictive performance remains virtually unchanged. Indeed, the proposed protocol retains loss bounds of periodically averaging schemes. An extensive empirical evaluation validates major improvement of the trade-off between model performance and communication which could be beneficial for numerous decentralized learning applications, such as autonomous driving, or voice recognition and image classification on mobile phones.

Collectively, machine learning (ML) researchers are engaged in the creation and dissemination of knowledge about data-driven algorithms. In a given paper, researchers might aspire to any subset of the following goals, among others: to theoretically characterize what is learnable, to obtain understanding through empirically rigorous experiments, or to build a working system that has high predictive accuracy. While determining which knowledge warrants inquiry may be subjective, once the topic is fixed, papers are most valuable to the community when they act in service of the reader, creating foundational knowledge and communicating as clearly as possible. Recent progress in machine learning comes despite frequent departures from these ideals. In this paper, we focus on the following four patterns that appear to us to be trending in ML scholarship: (i) failure to distinguish between explanation and speculation; (ii) failure to identify the sources of empirical gains, e.g., emphasizing unnecessary modifications to neural architectures when gains actually stem from hyper-parameter tuning; (iii) mathiness: the use of mathematics that obfuscates or impresses rather than clarifies, e.g., by confusing technical and non-technical concepts; and (iv) misuse of language, e.g., by choosing terms of art with colloquial connotations or by overloading established technical terms. While the causes behind these patterns are uncertain, possibilities include the rapid expansion of the community, the consequent thinness of the reviewer pool, and the often-misaligned incentives between scholarship and short-term measures of success (e.g., bibliometrics, attention, and entrepreneurial opportunity). While each pattern offers a corresponding remedy (don't do it), we also discuss some speculative suggestions for how the community might combat these trends.

We consider a system where agents enter in an online fashion and are evaluated based on their attributes or context vectors. There can be practical situations where this context is partially observed, and the unobserved part comes after some delay. We assume that an agent, once left, cannot re-enter the system. Therefore, the job of the system is to provide an estimated score for the agent based on her instantaneous score and possibly some inference of the instantaneous score over the delayed score. In this paper, we estimate the delayed context via an online convex game between the agent and the system. We argue that the error in the score estimate accumulated over $T$ iterations is small if the regret of the online convex game is small. Further, we leverage side information about the delayed context in the form of a correlation function with the known context. We consider the settings where the delay is fixed or arbitrarily chosen by an adversary. Furthermore, we extend the formulation to the setting where the contexts are drawn from some Banach space. Overall, we show that the average penalty for not knowing the delayed context while making a decision scales with $\mathcal{O}(\frac{1}{\sqrt{T}})$, where this can be improved to $\mathcal{O}(\frac{\log T}{T})$ under special setting.

One of the most fundamental problems in network study is community detection. The stochastic block model (SBM) is one widely used model for network data with different estimation methods developed with their community detection consistency results unveiled. However, the SBM is restricted by the strong assumption that all nodes in the same community are stochastically equivalent, which may not be suitable for practical applications. We introduce pairwise covariates-adjusted stochastic block model (PCABM), a generalization of SBM that incorporates pairwise covariate information. We study the maximum likelihood estimates of the coefficients for the covariates as well as the community assignments. It is shown that both the coefficient estimates of the covariates and the community assignments are consistent under suitable sparsity conditions. Spectral clustering with adjustment (SCWA) is introduced to efficiently solve PCABM. Under certain conditions, we derive the error bound of community estimation under SCWA and show that it is community detection consistent. PCABM compares favorably with the SBM or degree-corrected stochastic block model (DCBM) under a wide range of simulated and real networks when covariate information is accessible.

Though yet to become a standard in schools, artificial intelligence in education has been "a thing" since AI's uptick in the 1980s. In many ways, the two seem made for each other. We use education as a means to develop minds capable of expanding and leveraging the knowledge pool, while AI provides tools for developing a more accurate and detailed picture of how the human mind works. AI's digital, dynamic nature also offers opportunities for student engagement that cannot be found in often out-dated textbooks or in the fixed environment of the typical four-walled classroom. In synergistic fashion, they each have the potential to propel the other forward and accelerate the discovery of new learning frontiers and the creation of innovative technologies.

Front office jobs at investment banks are increasingly being taken over by intelligent machines. Many current front office employees are worried about being displaced by artificial intelligence, and their fears are not unfounded. Huy Nguyen Trieu, former head of macro structuring at Citigroup, has a positive message for traders who risk being replaced by automation: become a data facilitator. After 13 years in financial engineering at SocGen and RBS, before becoming an Managing Director and head of macro structuring at Citi, he has shifted his focus to acting as a thought leader in the fintech space. Currently a fintech fellow at London's Imperial College and mentor at fintech accelerator, Level 39, Nguyen Trieu is both fintech guru and entrepreneur.

The use of computers and robots is becoming more prevalent in societies worldwide. More schools are integrating basic robotics and programming concepts in their lessons and curricula. Such initiative is true not only in advanced countries but also in third world countries like the Philippines. "Robotics must be integrated in the schools. It is one of the skills 21st century learners need in order to succeed in life," De La Salle Santiago Zobel School International Robotics Coordinator Genevieve Pillar told Philippine News Agency (PNA).

The race to become the global leader in artificial intelligence (AI) has officially begun. In the past fifteen months, Canada, Japan, Singapore, China, the UAE, Finland, Denmark, France, the UK, the EU Commission, South Korea, and India have all released strategies to promote the use and development of AI. No two strategies are alike, with each focusing on different aspects of AI policy: scientific research, talent development, skills and education, public and private sector adoption, ethics and inclusion, standards and regulations, and data and digital infrastructure. It also highlights relevant policies and initiatives that the countries have announced since the release of their initial strategies. I plan to continuously update this article as new strategies and initiatives are announced. If a country or policy is missing (or if something in the summary is incorrect), please leave a comment and I will update the article as soon as possible.