Privacy-preserving distributed machine learning has become more important than ever due to the high demand of large-scale data processing. This paper focuses on a class of machine learning problems that can be formulated as regularized empirical risk minimization, and develops a privacy-preserving learning approach to such problems. We use Alternating Direction Method of Multipliers (ADMM) to decentralize the learning algorithm, and apply Gaussian mechanisms to provide differential privacy guarantee. However, simply combining ADMM and local randomization mechanisms would result in a nonconvergent algorithm with poor performance even under moderate privacy guarantees. Besides, this intuitive approach requires a strong assumption that the objective functions of the learning problems should be differentiable and strongly convex. To address these concerns, we propose an improved ADMM-based Differentially Private distributed learning algorithm, DP-ADMM, where an approximate augmented Lagrangian function and Gaussian mechanisms with time-varying variance are utilized. We also apply the moments accountant method to bound the total privacy loss. Our theoretical analysis shows that DP-ADMM can be applied to a general class of convex learning problems, provides differential privacy guarantee, and achieves a convergence rate of $O(1/\sqrt{t})$, where $t$ is the number of iterations. Our evaluations demonstrate that our approach can achieve good convergence and accuracy with moderate privacy guarantee.

On a hot late-summer day in 2005, I sat in a packed, agreeably air-conditioned auditorium and listened to a university administrator welcome the class of 2009. As the popular saying goes, 'The rich go to Peking U, the poor go to Tsinghua, and the ones willing to work themselves to death come to USTC.'" If Peking University is China's Harvard, and Tsinghua is China's MIT, the University of Science and Technology of China, or USTC, is known as "the Caltech of China" for its small size and intense focus on science and engineering. I was proud to be there. But my pride shifted to awkwardness after the speech, when we stood to sing the university anthem, which ends with an exhortation: "Always learn from the people, and learn from the great leader Mao Zedong!" Hearing Mao's name left a bitter taste. It reminded me of career paths my country had denied me. Without the rule of law, I could not become a lawyer. Without a free press, I could not become a journalist. Without democratic elections, I could not become a politician. Instead I did what was expected of Chinese students without political connections or financial resources but with impeccable grades: I came to USTC to study science. The lyrics of the anthem brought up a question my classmates and I would often ponder: Must scientific research be in service of one's country--or can the pursuit of knowledge transcend nationalism? Generations of scientists at USTC have sought to answer this question. The university gave birth to both China's first satellite, launched in 1970, and the world's first quantum-communication satellite, launched in 2016. It is home to China's first synchrotron particle accelerator, and it will soon host a new multibillion-dollar quantum-science center. Over the years, faculty and students have, at times, wielded the university's scientific prestige as a shield to protect academic freedom and political independence. But if the university's rising trajectory in recent years is any indication, science in China thrives most when it serves the state. Today I live and work in the United States. I spoke to many old schoolmates and current USTC researchers to report this article. The story of USTC that emerges reveals the limits of science's ability to transcend China's authoritarian politics. It is also the story of my family across three generations. USTC was founded in Beijing in 1958, to train scientists for China's fledgling nuclear and space programs. Members of the faculty were drawn from China's scientific elite. Fang Lizhi, one of the first, came to teach physics after being deemed too politically outspoken to work on the bomb. "He was actually happy about it!

First, a couple of pointers to keep in mind when searching for datasets. Kaggle: A data science site that contains a variety of externally contributed interesting datasets. You can find all kinds of niche datasets in its master list, from ramen ratings to basketball data to and even seattle pet licenses. Although the data sets are user-contributed, and thus have varying levels of cleanliness, the vast majority are clean. VisualData: Discover computer vision datasets by category, it allows searchable queries.

A study on the Indian artificial intelligence (AI) industry by Great Learning, the online education company, indicates there are over 4,000 positions related to AI in India that remain vacant due to shortage of qualified talent at mid and senior levels. This is despite the industry growing by 30% in the last one year to $230 million. These opportunities do not include the slew of new jobs advertised every month, but refer to opportunities that have been vacant for a period of 12 months. The 10 leading companies with the most number of AI openings in India this year are: IBM, Accenture, Amazon, Fractal Analytics, Societe Generale, SAP Labs, 24/7 Customer, Atos, Nvidia, Tech Mahindra. When it comes to remuneration, the median salary of AI professionals in India is Rs 14.3 lakh across all experience level and skill-sets, 40% of AI professionals have an entry-level salary of Rs 6 lakh onwards, and 4% command a salary higher than Rs 50 lakh.

Artificial Intelligence is nowadays a hot topic in many industries, from banking to travel, including retail, manufacturing, tech… But, what about artificial intelligence in the energy industry? Are utilities and ESCOs already applying it? What shall you know about it? Join our Data Science team and discover what is AI and how it's been used in the Energy industry. What is data science, artificial intelligence and other cool words like machine learning or neuronal networks.

Recent breakthroughs in Go play and strategic games have witnessed the great potential of reinforcement learning in intelligently scheduling in uncertain environment, but some bottlenecks are also encountered when we generalize this paradigm to universal complex tasks. Among them, the low efficiency of data utilization in model-free reinforcement algorithms is of great concern. In contrast, the model-based reinforcement learning algorithms can reveal underlying dynamics in learning environments and seldom suffer the data utilization problem. To address the problem, a model-based reinforcement learning algorithm with attention mechanism embedded is proposed as an extension of World Models in this paper. We learn the environment model through Mixture Density Network Recurrent Network(MDN-RNN) for agents to interact, with combinations of variational auto-encoder(VAE) and attention incorporated in state value estimates during the process of learning policy. In this way, agent can learn optimal policies through less interactions with actual environment, and final experiments demonstrate the effectiveness of our model in control problem.

Many modern learning tasks involve fitting nonlinear models to data which are trained in an overparameterized regime where the parameters of the model exceed the size of the training dataset. Due to this overparameterization, the training loss may have infinitely many global minima and it is critical to understand the properties of the solutions found by first-order optimization schemes such as (stochastic) gradient descent starting from different initializations. In this paper we demonstrate that when the loss has certain properties over a minimally small neighborhood of the initial point, first order methods such as (stochastic) gradient descent have a few intriguing properties: (1) the iterates converge at a geometric rate to a global optima even when the loss is nonconvex, (2) among all global optima of the loss the iterates converge to one with a near minimal distance to the initial point, (3) the iterates take a near direct route from the initial point to this global optima. As part of our proof technique, we introduce a new potential function which captures the precise tradeoff between the loss function and the distance to the initial point as the iterations progress. For Stochastic Gradient Descent (SGD), we develop novel martingale techniques that guarantee SGD never leaves a small neighborhood of the initialization, even with rather large learning rates. We demonstrate the utility of our general theory for a variety of problem domains spanning low-rank matrix recovery to neural network training. Underlying our analysis are novel insights that may have implications for training and generalization of more sophisticated learning problems including those involving deep neural network architectures.

Want to master machine learning? Gain in-demand skills & expand your opportunities by taking this ultimate machine learning masterclass today! If you are super keen on building more intelligent apps using Machine Learning, this new foundational framework is one to take advantage of! A lot of people ask: 'What is machine learning?' Well, think about it as programming a machine to mimic the human mind!

HOy traemos a este espacio el último número el Vol 13, No 10 (2018) del International Journal of Emerging Technologies in Learning (iJET) This interdisciplinary journal aims to focus on the exchange of relevant trends and research results as well as the presentation of practical experiences gained while developing and testing elements of technology enhanced learning. So it aims to bridge the gap between pure academic research journals and more practical publications. So it covers the full range from research, application development to experience reports and product descriptions. Readers don't have to pay any fee. Vol 13, No 10 (2018) Table of Contents Papers Innovative English Classroom Teaching Based on Online Computer Technology in Rural Middle and Primary Schools Application of Brain Neural Network in Personalized English Education System Songlin Yang, Min Zhang A Generic Tool for Generating and Assessing Problems Automatically using Spreadsheets Maria Assumpció Rafart Serra, Andrea Bikfalvi, Josep Soler Masó, Jordi Poch Garcia A High Security Distance Education Platform Infrastructure Based on Private Cloud Jingtai Ran, Kepeng Hou, Kegang Li, Niya Dai Student Performance Prediction Model Based on Discriminative Feature Selection Haixia Lu, Jinsong Yuan An Eight-Layer Model for Mathematical Cognition Marios A. Pappas, Athanasios S. Drigas, Fotini Polychroni Design and Implementation of University Art Education Management System Based on JAVA Technology The Design and Application of Flip Classroom Teaching Based on Computer Technology Jia Li, Xiaoxia Zhang, Zijun Hu Feature Extraction and Learning Effect Analysis for MOOCs Users Based on Data Mining Intelligent System for College English Listening and Writing Training Development of an Accounting Skills Simulation Practice System Based on the B/S Architecture Jianmei Liu, Rong Fu Teaching Quality Evaluation and Scheme Prediction Model Based on Improved Decision Tree Algorithm Sujuan Jia, Yajing Pang Blended Learning Innovation Model among College Students Based on Internet Score Prediction Model of MOOCs Learners Based on Neural Network Yuan Zhang, Wenbo Jiang Design and Implementation of the Online Computer-Assisted Instruction System Based on Object-Oriented Analysis Technology Wenbo Zhou, Lei Shi, Jian Chen Matlab-Realized Visual A* Path Planning Teaching Platform Communication Jigsaw: A Teaching Method that Promotes Scholarly Communication A Tablet-Computer-Based Tool to Facilitate Accurate Self-Assessments in Third- and Fourth-Graders Denise Villanyi, Romain Martin, Philipp Sonnleitner, Christina Siry, Antoine Fischbach Short Papers Application of Blockchain Technology in Online Education Han Sun, Xiaoyue Wang, Xinge Wang The Grading Multiple Choice Tests System via Mobile Phone using Image Processing Technique Worawut Yimyam, Mahasak Ketcham Offline Support Model for Low Bandwidth Users to Survive in MOOCs International Journal of Emerging Technologies in Learning.

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