In this paper we propose a primal-dual proximal extragradient algorithm to solve the generalized Dantzig selector (GDS) estimation problem, based on a new convex-concave saddle-point (SP) reformulation. Our new formulation makes it possible to adopt recent developments in saddle-point optimization, to achieve the optimal $O(1/k)$ rate of convergence. Compared to the optimal non-SP algorithms, ours do not require specification of sensitive parameters that affect algorithm performance or solution quality. We also provide a new analysis showing a possibility of local acceleration to achieve the rate of $O(1/k^2)$ in special cases even without strong convexity or strong smoothness. As an application, we propose a GDS equipped with the ordered $\ell_1$-norm, showing its false discovery rate control properties in variable selection. Algorithm performance is compared between ours and other alternatives, including the linearized ADMM, Nesterov's smoothing, Nemirovski's mirror-prox, and the accelerated hybrid proximal extragradient techniques.

Motivated by the scarcity of accurate payoff feedback in practical applications of game theory, we examine a class of learning dynamics where players adjust their choices based on past payoff observations that are subject to noise and random disturbances. First, in the single-player case (corresponding to an agent trying to adapt to an arbitrarily changing environment), we show that the stochastic dynamics under study lead to no regret almost surely, irrespective of the noise level in the player's observations. In the multi-player case, we find that dominated strategies become extinct and we show that strict Nash equilibria are stochastically stable and attracting; conversely, if a state is stable or attracting with positive probability, then it is a Nash equilibrium. Finally, we provide an averaging principle for 2-player games, and we show that in zero-sum games with an interior equilibrium, time averages converge to Nash equilibrium for any noise level. Contents 1. Introduction 2 2. The model 5 3. Regret minimization 11 4. Extinction of dominated strategies 14 5.

In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solution was benchmarked using the actual load data and the country provided estimates (where available). We conclude that the proposed system can produce timely forecasts with comparable prediction accuracy in a number of cases. We also investigate the probabilistic case of forecasting - that is, providing a probability distribution rather than a simple point forecast - and incorporate it into a web based API that provides quick and easy access to reliable forecasts.

The technology behind Alexa, the voice inside Amazon's Echo speaker, could become Amazon's fourth business pillar, alongside retail marketplace, Amazon Prime, and Amazon Web Services. Amazon has more than 1,000 people working on artificial intelligence and third-party apps that people have built using the company's SDK, Bezos, the founder and CEO of the company, said at Recode's Code Conference Tuesday. During an interview with Walt Mossberg, he said the company licenses the technology to others so they can embed it in an app or device. There's also a program that allows companies to build apps that teaches Alexa new skills. Bezos called artificial intelligence, natural language processing and machine learning intelligence "gigantic" and says it's probably difficult to "overstate the impact it will have on society over the next 20 years."

Note: This is Part 2 of a two-part series on AI. PDF: We made a fancy PDF of this post for printing and offline viewing. We have what may be an extremely difficult problem with an unknown time to solve it, on which quite possibly the entire future of humanity depends. Welcome to Part 2 of the "Wait how is this possibly what I'm reading I don't get why everyone isn't talking about this" series. Part 1 started innocently enough, as we discussed Artificial Narrow Intelligence, or ANI (AI that specializes in one narrow task like coming up with driving routes or playing chess), and how it's all around us in the world today. We then examined why it was such a huge challenge to get from ANI to Artificial General Intelligence, or AGI (AI that's at least as intellectually capable as a human, across the board), and we discussed why the exponential rate of technological advancement we've seen in the past suggests that AGI might not be as far away as it seems. This left us staring at the screen, ...

Morgan Stanley analysts cautioned that all of the above predictions are far from certain, as public planners and policymakers could take "meaningful detours" in their various approaches to ride-hailing services. Perhaps most significantly, policymakers could decide to view such services as a public detriment, rather than as an opportunity for collaboration. "The transition to fully autonomous vehicles will likely be gradual," the report notes. "Yet this may not diminish the cumulative impact over time. For workers displaced by shared mobility and autonomous technology, there are no easy solutions."

Keras is a Python library for deep learning that wraps the efficient numerical libraries Theano and TensorFlow. In this post you will discover how you can use Keras to develop and evaluate neural network models for multi-class classification problems. Multi-Class Classification Tutorial with the Keras Deep Learning Library Photo by houroumono, some rights reserved. In this tutorial we will use the standard machine learning problem called the iris flowers dataset. This dataset is well studied and is a good problem for practicing on neural networks because all of the 4 input variables are numeric and have the same scale in centimeters.

This list of hand-picked leaders was compiled by Wojtek Aleksander, from GetResponse.com. Other bigger lists (sometimes created by robots) can be found here and are usually based on your Klout score, which in my opinion is not accurate. The list below is truly original and I would even add, somewhat unexpected, as you won't find Bernard Marr, Kirk Borne and other well known gurus. Just in case you're wondering, @FILWD stands for Fell In Love With Data, which happens to be the name of Enrico Bertini's blog. While the Assistant Professor at NYU doesn't talk much on Twitter himself, he uses the platform very effectively to share news and insights about data visualizations and adds his highly-valued opinions.

With Udacity's Nanodegree Programs, you'll build and design amazing projects, learn from top experts at leading companies in Silicon Valley, and land your dream job in technology. Enroll in a Nanodegree program, graduate in under 12 months, and get a 50% tuition refund! With Udacity's Nanodegree Plus program, you'll get hired within 6 months of graduating, or we'll refund 100% of your tuition. Learn in-depth skills in machine learning and artificial intelligence to get the hottest jobs building the products of the future in robotics, transportation and healthcare. What will you build today?

In May on /r/MachineLearning we get jokes, more jokes, bad news about freely-available study material, good news about some other freely-available study material, some videos, news from Google, and a walkthrough for setting up a deep learning machine. This bit of news has made the rounds over the past week, so you may have already heard: Andrej Karpathy has been forced to take down the previously publicly-available videos for his Convolutional Neural Networks course at Stanford. This is a link to the tweet announcing it. Long-time Python tutorial make sentdex has shared his latest series of machine learning video tutorials, aimed at beginner to intermediate programmers. The most recent series is an in-depth machine learning course, aimed at breaking down the complex ML concepts that are typically just "done for you" in a hand-wavy fashion with packages and modules.