Tosatisfythenear -optimalityconstraintof(1), weusean estimateofthefunction (h), see(2), todefine , , (h):=1[ (h)< / ]forparameters , 0, 1. Thefunction , , (h)representswhetheran , / -optimumhasbeenfound. Wedefine r , , (h, a)=

You would be forgiven for thinking the rain this year has been relentless - because in some parts of the UK, it actually has been. Here at BBC Weather we have been watching computer models closely for signs of when that pattern will change. These computer-generated forecasts go out about two weeks into the future - and models have often been hinting at a change to colder and drier weather on that timescale. However, they have then reverted to the familiar wet pattern as we have got closer to the time. Now though, there are stronger signals of a change for some of us - albeit perhaps only a temporary one.

Autonomous driving relies on a huge volume of real-world data to be labeled to high precision. Alternative solutions seek to exploit driving simulators that can generate large amounts of labeled data with aplethora of content variations. However, the domain gap between the synthetic and real data remains, raising the following important question:What arethe best way toutilize aself-driving simulatorforperceptiontasks?

We study the problem of learning a linear model to set the reserve price in an auction, given contextual information, in order to maximize expected revenue fromtheseller side.