The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to produce RL algorithms whose policies generalise well to novel unseen situations at deployment time, avoiding overfitting to their training environments. Tackling this is vital if we are to deploy reinforcement learning algorithms in real world scenarios, where the environment will be diverse, dynamic and unpredictable. This survey is an overview of this nascent field. We rely on a unifying formalism and terminology for discussing different ZSG problems, building upon previous works. We go on to categorise existing benchmarks for ZSG, as well as current methods for tackling these problems. Finally, we provide a critical discussion of the current state of the field, including recommendations for future work. Among other conclusions, we argue that taking a purely procedural content generation approach to benchmark design is not conducive to progress in ZSG, we suggest fast online adaptation and tackling RL-specific problems as some areas for future work on methods for ZSG, and we recommend building benchmarks in underexplored problem settings such as offline RL ZSG and reward-function variation.

For every machine learning enthusiast, we were told to go to Kaggle. Should it end at just making predictions on the test data and submitting it? Or we would always be supplied with excel sheets to make predictions on with the model in real-life scenarios. What do we need to do? Read along to find out what needs to be done. Kowepe bank of Nigeria conducted marketing campaigns via phone calls with their clients. These campaigns prompt their clients to subscribe to a specific financial product of the bank (term deposit).

A major focus of Computer Weekly's technology and ethics coverage in 2022 was on working conditions throughout the tech sector, from the issue of forced labour and slavery throughout technology supply chains, to UK Amazon workers staging spontaneous "wildcat" strikes in response to derisory pay rises and warehouse conditions. Other stories in this vein included coverage of accusations that "soft union-busting" tactics were used by app-based food delivery firm Deliveroo to scupper its workers' grassroots organising efforts, and the ongoing court case against five major tech firms for their alleged role in the maiming and deaths of people extracting raw materials in the Democratic Republic of Congo. Artificial intelligence (AI) also featured heavily in Computer Weekly's technology and ethics coverage in 2022, with stories published on the tech sector's lacklustre commitment to "ethical" AI, as well as on the pitfalls and challenges of auditing AI-powered algorithms. Police technology was another major focus of 2022, as policing bodies continue to push ahead with new tech deployments such as live facial recognition (LFR) despite serious concerns about its effectiveness, proportionality and efficacy. Other stories focused on how technology is developed and deployed, and the underlying power dynamics at play.

Model-based Reinforcement Learning (MBRL) has been widely adapted due to its sample efficiency. However, existing worst-case regret analysis typically requires optimistic planning, which is not realistic in general. In contrast, motivated by the theory, empirical study utilizes ensemble of models, which achieve state-of-the-art performance on various testing environments. Such deviation between theory and empirical study leads us to question whether randomized model ensemble guarantee optimism, and hence the optimal worst-case regret? This paper partially answers such question from the perspective of reward randomization, a scarcely explored direction of exploration with MBRL. We show that under the kernelized linear regulator (KNR) model, reward randomization guarantees a partial optimism, which further yields a near-optimal worst-case regret in terms of the number of interactions. We further extend our theory to generalized function approximation and identified conditions for reward randomization to attain provably efficient exploration. Correspondingly, we propose concrete examples of efficient reward randomization. To the best of our knowledge, our analysis establishes the first worst-case regret analysis on randomized MBRL with function approximation.

The stochastic proximal point (SPP) methods have gained recent attention for stochastic optimization, with strong convergence guarantees and superior robustness to the classic stochastic gradient descent (SGD) methods showcased at little to no cost of computational overhead added. In this article, we study a minibatch variant of SPP, namely M-SPP, for solving convex composite risk minimization problems. The core contribution is a set of novel excess risk bounds of M-SPP derived through the lens of algorithmic stability theory. Particularly under smoothness and quadratic growth conditions, we show that M-SPP with minibatch-size $n$ and iteration count $T$ enjoys an in-expectation fast rate of convergence consisting of an $\mathcal{O}\left(\frac{1}{T^2}\right)$ bias decaying term and an $\mathcal{O}\left(\frac{1}{nT}\right)$ variance decaying term. In the small-$n$-large-$T$ setting, this result substantially improves the best known results of SPP-type approaches by revealing the impact of noise level of model on convergence rate. In the complementary small-$T$-large-$n$ regime, we provide a two-phase extension of M-SPP to achieve comparable convergence rates. Moreover, we derive a near-tight high probability (over the randomness of data) bound on the parameter estimation error of a sampling-without-replacement variant of M-SPP. Numerical evidences are provided to support our theoretical predictions when substantialized to Lasso and logistic regression models.

After almost four decades in space, a retired NASA satellite is about to fall to earth. The chances of falling satellite debris posing a risk to "anyone on Earth is very low", NASA said in a statement on Saturday. Most of the 2,450kg (5,400lb) satellite will burn up on re-entry but some pieces are expected to survive, according to NASA. The United States space agency put the odds of injury from falling debris at about one-in-9,400. The science satellite is expected to come down on Sunday night at approximately 18:40 EST (23:40 GMT), give or take 17 hours, according to the US defence department.

A key promise of machine learning is the ability to assist users with personal tasks. Because the personal context required to make accurate predictions is often sensitive, we require systems that protect privacy. A gold standard privacy-preserving system will satisfy perfect secrecy, meaning that interactions with the system provably reveal no private information. However, privacy and quality appear to be in tension in existing systems for personal tasks. Neural models typically require copious amounts of training to perform well, while individual users typically hold a limited scale of data, so federated learning (FL) systems propose to learn from the aggregate data of multiple users. FL does not provide perfect secrecy, but rather practitioners apply statistical notions of privacy -- i.e., the probability of learning private information about a user should be reasonably low. The strength of the privacy guarantee is governed by privacy parameters. Numerous privacy attacks have been demonstrated on FL systems and it can be challenging to reason about the appropriate privacy parameters for a privacy-sensitive use case. Therefore our work proposes a simple baseline for FL, which both provides the stronger perfect secrecy guarantee and does not require setting any privacy parameters. We initiate the study of when and where an emerging tool in ML -- the in-context learning abilities of recent pretrained models -- can be an effective baseline alongside FL. We find in-context learning is competitive with strong FL baselines on 6 of 7 popular benchmarks from the privacy literature and a real-world case study, which is disjoint from the pretraining data. We release our code here: https://github.com/simran-arora/focus

From the perspective of any nation, rural areas generally present a comparable set of problems, such as a lack of proper health care, education, living conditions, wages, and market opportunities. Some nations have created and developed the concept of smart villages during the previous few decades, which effectively addresses these issues. The landscape of traditional agriculture has been radically altered by digital agriculture, which has also had a positive economic impact on farmers and those who live in rural regions by ensuring an increase in agricultural production. We explored current issues in rural areas, and the consequences of smart village applications, and then illustrate our concept of smart village from recent examples of how emerging digital agriculture trends contribute to improving agricultural production in this chapter.

In this paper, we study the problem of k-center clustering with outliers. The problem has many important applications in real world, but the presence of outliers can significantly increase the computational complexity. Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem. Our idea is inspired by the greedy method, Gonzalez's algorithm, that was developed for solving the ordinary k-center clustering problem. Based on some novel observations, we show that a simple randomized version of this greedy strategy actually can handle outliers efficiently. We further show that this randomized greedy approach also yields small coreset for the problem in doubling metrics (even if the doubling dimension is not given), which can greatly reduce the computational complexity. Moreover, together with the partial clustering framework proposed by Guha et al. (2019), we prove that our coreset method can be applied to distributed data with a low communication complexity. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower complexities comparing with the existing methods.

$\bf{Purpose}$: To describe the 3D structural changes in both connective and neural tissues of the optic nerve head (ONH) that occur concurrently at different stages of glaucoma using traditional and AI-driven approaches. $\bf{Methods}$: We included 213 normal, 204 mild glaucoma (mean deviation [MD] $\ge$ -6.00 dB), 118 moderate glaucoma (MD of -6.01 to -12.00 dB), and 118 advanced glaucoma patients (MD < -12.00 dB). All subjects had their ONHs imaged in 3D with Spectralis optical coherence tomography. To describe the 3D structural phenotype of glaucoma as a function of severity, we used two different approaches: (1) We extracted human-defined 3D structural parameters of the ONH including retinal nerve fiber layer (RNFL) thickness, lamina cribrosa (LC) shape and depth at different stages of glaucoma; (2) we also employed a geometric deep learning method (i.e. PointNet) to identify the most important 3D structural features that differentiate ONHs from different glaucoma severity groups without any human input. $\bf{Results}$: We observed that the majority of ONH structural changes occurred in the early glaucoma stage, followed by a plateau effect in the later stages. Using PointNet, we also found that 3D ONH structural changes were present in both neural and connective tissues. In both approaches, we observed that structural changes were more prominent in the superior and inferior quadrant of the ONH, particularly in the RNFL, the prelamina, and the LC. As the severity of glaucoma increased, these changes became more diffuse (i.e. widespread), particularly in the LC. $\bf{Conclusions}$: In this study, we were able to uncover complex 3D structural changes of the ONH in both neural and connective tissues as a function of glaucoma severity. We hope to provide new insights into the complex pathophysiology of glaucoma that might help clinicians in their daily clinical care.