We introduce sparse random projection, an important dimension-reduction tool from machine learning, for the estimation of discrete-choice models with high-dimensional choice sets. Initially, high-dimensional data are compressed into a lower-dimensional Euclidean space using random projections. Subsequently, estimation proceeds using cyclic monotonicity moment inequalities implied by the multinomial choice model; the estimation procedure is semi-parametric and does not require explicit distributional assumptions to be made regarding the random utility errors. The random projection procedure is justified via the Johnson-Lindenstrauss Lemma -- the pairwise distances between data points are preserved during data compression, which we exploit to show convergence of our estimator. The estimator works well in simulations and in an application to a supermarket scanner dataset.

In this paper we propose an approach to preference elicitation that is suitable to large configuration spaces beyond the reach of existing state-of-the-art approaches. Our setwise max-margin method can be viewed as a generalization of max-margin learning to sets, and can produce a set of "diverse" items that can be used to ask informative queries to the user. Moreover, the approach can encourage sparsity in the parameter space, in order to favor the assessment of utility towards combinations of weights that concentrate on just few features. We present a mixed integer linear programming formulation and show how our approach compares favourably with Bayesian preference elicitation alternatives and easily scales to realistic datasets.

Predictive models are finding an increasing number of applications in many industries. As a result, a practical means for trading-off the cost of deploying a model versus its effectiveness is needed. Our work is motivated by risk prediction problems in healthcare. Cost-structures in domains such as healthcare are quite complex, posing a significant challenge to existing approaches. We propose a novel framework for designing cost-sensitive structured regularizers that is suitable for problems with complex cost dependencies. We draw upon a surprising connection to boolean circuits. In particular, we represent the problem costs as a multi-layer boolean circuit, and then use properties of boolean circuits to define an extended feature vector and a group regularizer that exactly captures the underlying cost structure. The resulting regularizer may then be combined with a fidelity function to perform model prediction, for example. For the challenging real-world application of risk prediction for sepsis in intensive care units, the use of our regularizer leads to models that are in harmony with the underlying cost structure and thus provide an excellent prediction accuracy versus cost tradeoff.

Drones could be bringing parcels to your door within two years, thanks to a bill that left the Senate today. The bipartisan aviation policy bill, which passed the Senate 95-3 Tuesday, demands that the Federal Aviation Authority (FAA) authorize package deliveries by drones within two years. The bill, which must now be debated by the House, also makes changes to various regulations including those affecting airport security and airplane pricing. It says that the agency must create a small drone'air carrier certificate' for operators of delivery drone fleets, similar to the safety certificates granted to commercial airlines. These rules are needed for Amazon and other companies to deploy fleets of delivery drones.

With the next series of Game of Thrones set to hit our screens this month anxious fans may be wondering which one of their favourite characters is next on the hit list. The first five of the series have not been an easy watch, with the writers killing off key characters just when their luck starts to change and as audiences warmed to them. But researchers have developed a computer algorithm aimed at predicting the next character to die in the hit series. Students in Germany have developed a GoT-related computer algorithm which uses available data from the internet to predict the next character to die. By trawling the internet for data and clues, a team of computer scientists have created a model which crunches the numbers to work out which characters are most likely to die in the upcoming sixth series.

In the busy weeks leading up to RSA this year, I was taking a rare break to drive my daughter to the airport. She was flying back to school to continue her 2nd year at University of Toronto (shout out to all of my Canadian peeps!). Btw, if you've not seen "Stronger Beer" highly recommended. Anyway, my daughter asked me an intriguing question on the ride to LAX. She said, "Last time I got caught in a random search… do you think the TSA finds anything doing that…" Great question, and my answer was "No" it's a horrible way to search people.

April 24 can't come soon enough for Game of Thrones fans eagerly awaiting the premiere of the hit show's sixth season. Naturally, most of us have been speculating wildly about the fate of our favorite characters for the past year, but now there's a clever app to help you withthat. The project, A Song of Ice and Data, was developed by a group of students of a JavaScript course at the Technical University of Munich. Some of the biggest names in tech are coming to TNW Conference in Amsterdam this May. It looks at 24 features of each character, the list of which includes attributes like their age, the House they belong to, whether they're married and how popular they are based on how many wiki pages link to them.

I don't know if you're in the market for a new car, of course, but chances are that soon, possibly the next time you buy a vehicle, it will have so much processing power and artificial intelligence that you won't won't be able to keep up. Because the smarter cars get, the safer we become. It's estimated that we could reduce traffic fatalities by 90% -- or 30,000 lives every year -- by 2050, once cars start driving themselves. To get there, tech companies are creating hardware and software that make semi-autonomous and fully autonomous cars a reality.NVIDIA andAlphabet's Google are two leaders in the car tech space -- and they're just getting started. Advanced hardware NVIDIA released two huge steps forward in automotive technology recently: its Drive PX 2 system and the DGX-1 supercomputer.

If you suspect that your significant other is bringing others into your bedroom, you could have an adult conversation about it or seek couple's counseling. Alternatively, you can buy a 1,700 smart mattress called the Smarttress that will tell you when your partner is having sex with someone that isn't you. Smarttress is the invention of Durmet, a Spanish mattress company that was inspired by the fact that Madrid has the highest number of cheating spouses in Europe. It features 24 sensors within the springs, which the company calls the "Lover Detection System." These sensors know which areas of the mattress are receiving pressure and make a 3D map of the bed.

It's a good time to be Alphabet Inc. (GOOG), the parent company of Google. The holding company that owns Google, YouTube and Android -- as well as so-called moonshots like self-driving cars, the home-networking division Nest and Google Fiber -- is expected to turn in healthy first-quarter results on Thursday, driven by its dominant position in online search and display advertising. On Wednesday, the European Commission is expected to formally charge Google for favoring its own apps and services on its Android mobile operating system, which powers more than 80 percent of the world's smartphones. That will be the latest in a decade of entanglements with regulators on both sides of the Atlantic; Google also got some bad press in Britain earlier this year for having paid just 185 million in taxes over the past decade. Also confronting Google -- and the rest of the tech industry -- is how to manage government and law enforcement requests for information.