Most of the existing literature on supervised learning problems focuses on the case when the training data set is drawn from an i.i.d. sample. However, many practical supervised learning problems are characterized by temporal dependence and strong correlation between the marginals of the data-generating process, suggesting that the i.i.d. assumption is not always justified. This problem has been already considered in the context of Markov chains satisfying the Doeblin condition. This condition, among other things, implies that the chain is not singular in its behavior, i.e. it is irreducible. In this article, we focus on the case when the training data set is drawn from a not necessarily irreducible Markov chain. Under the assumption that the chain is uniformly ergodic with respect to the $\mathrm{L}^1$-Wasserstein distance, and certain regularity assumptions on the hypothesis class and the state space of the chain, we first obtain a uniform convergence result for the corresponding sample error, and then we conclude learnability of the approximate sample error minimization algorithm and find its generalization bounds. At the end, a relative uniform convergence result for the sample error is also discussed.

In a recently published paper, a pair of academics propose that the application of artificial intelligence can offer a potent weapon against antitrust behavior in the Big Tech sector. This is the very industry that has advanced this technology, noted one of those academics, Giovana Massarotto, a Center for Technology, Innovation and Competition academic fellow at the University of Pennsylvania Carey Law School and an adjunct professor at the University of Iowa. She underscored this fact in an article for Bloomberg Law, in which she maintains that "the present economic democracy propaganda against Big Tech is not the solution to increase competition in fast-moving technology markets." In fact, she says, the industry's ingenuity is needed to achieve our nation's pro-competition goals. Massarotto and University of Liege (Belgium) Associate Professor Ashwin Ittoo write about their "antitrust machine learning application" (AML) which shows the potential for AI to "assist antitrust agencies in detecting anticompetitive practices faster."

Early in 2021, the Stanford Virtual Human Interaction Lab looked at the psychological consequences of spending long days videoconferencing and in virtual meetings. The popularized term "Zoom fatigue," is the result of maxing out cognitive load and even reducing effectiveness. For all of that investment in remote work technology, senior managers feel there is very little payoff. The University of North Carolina surveyed 182 senior managers and 65% of them felt meetings kept them from completing their own work, 71% felt meetings were inefficient and unproductive, and 64% felt meetings undercut deep thinking. As technology-dependent remote workers proliferate, new solutions are coming to the fore that may make both in-person and virtual meetings more productive.

Behind Sahej, hanging on the wall, Nayana could see rows of colorful masks, which, she learned, Sahej had carved and painted himself. On the first day of the new term, the teacher had asked Sahej about the masks, and the new student shyly gave a show-and-tell, explaining how the masks combined Indian gods and spirits with the powers of superheroes. Now, in an invitation-only room on her ShareChat, some of Nayana's classmates were gossiping about Sahej. From the way his room was furnished to the fact that his surname was hidden from public view in school records, these girls were certain Sahej was among the "vulnerable group" that the government mandated make up at least 15% of their student body. At private schools across India, such children were practically guaranteed spots and their tuition, books, and uniforms were covered by scholarships.

In this project we will be working with a data set, indicating whether it rain the next day in Australia, Yes or No? This column is Yes if the rain for that day was 1mm or more. We will try to create a model that will predict using the available data. Welcome to this project on predict whether it will rain tomorrow in Australia in Apache Spark Machine Learning using Databricks platform community edition server which allows you to execute your spark code, free of cost on their server just by registering through email id. In this project, we explore Apache Spark and Machine Learning on the Databricks platform.

How one Kaggler took top marks across multiple Covid-related challenges. Today we interview Daniel, whose notebooks earned him top marks in Kaggle's CORD-19 challenges. Kaggle hosted multiple challenges that worked with the Kaggle CORD-19 dataset, and Daniel won 1st place three times, including by a huge margin in the TREC-COVID challenge. My research interests include probabilistic forecasting, causal inference and machine learning. As part of the Kaggle CORD-19 challenge I developed discovid.ai I'm also a student assistant where I've worked on several data science projects for the last 3 years and had the opportunity to work with real world data from different companies in highly diverse domains -- from predicting the waste in a sawmill to analyzing flaws in the process of surface galvanization and testing the efficiency of a marketing campaign.

Enter Docker Masterclass for Machine Learning and Data Science. Led by Docker evangelist and Cybersecurity expert Jordan Sauchuk, this course is designed to get ... Every data scientist is aware that, at some point or another, they'll need to show off their progress and results. And there couldn't be a bigger fear than not having your algorithm run on another computer for reasons you can't define. Enter Docker Masterclass for Machine Learning and Data Science. Led by Docker evangelist and Cybersecurity expert Jordan Sauchuk, this course is designed to get you up and running with Docker, so you will always be prepared to ship your content no matter the situation.

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Federated learning (FL) is a privacy-preserving paradigm where multiple participants jointly solve a machine learning problem without sharing raw data. Unlike traditional distributed learning, a unique characteristic of FL is statistical heterogeneity, namely, data distributions across participants are different from each other. Meanwhile, recent advances in the interpretation of neural networks have seen a wide use of neural tangent kernel (NTK) for convergence and generalization analyses. In this paper, we propose a novel FL paradigm empowered by the NTK framework. The proposed paradigm addresses the challenge of statistical heterogeneity by transmitting update data that are more expressive than those of the traditional FL paradigms. Specifically, sample-wise Jacobian matrices, rather than model weights/gradients, are uploaded by participants. The server then constructs an empirical kernel matrix to update a global model without explicitly performing gradient descent. We further develop a variant with improved communication efficiency and enhanced privacy. Numerical results show that the proposed paradigm can achieve the same accuracy while reducing the number of communication rounds by an order of magnitude compared to federated averaging.

We teach goal-driven agents to interactively act and speak in situated environments by training on generated curriculums. Our agents operate in LIGHT (Urbanek et al., 2019)--a large-scale crowd-sourced fantasy text adventure game wherein an agent perceives and interacts with the world through textual natural language. Goals in this environment take the form of character-based quests, consisting of personas and motivations. We augment LIGHT by learning to procedurally generate additional novel textual worlds and quests to create a curriculum of steadily increasing difficulty for training agents to achieve such goals. In particular, we measure curriculum difficulty in terms of the rarity of the quest in the original training distribution--an easier environment is one that is more likely to have been found in the unaugmented dataset. An ablation study shows that this method of learning from the tail of a distribution results in significantly higher generalization abilities as measured by zeroshot performance on never-before-seen quests. Figure 1: The LIGHT questing environment presented as a 2 player game deployed in Messenger.