A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search

Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature that investigate how to design an auction mechanism in order to optimize the revenue of the search engine. However, due to some unrealistic assumptions used, the practical values of these studies are not very clear. In this paper, we propose a novel \emph{game-theoretic machine learning} approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimization framework. In particular, we first learn a Markov model from historical data to describe how advertisers change their bids in response to an auction mechanism, and then for any given auction mechanism, we use the learnt model to predict its corresponding future bid sequences. Next we learn the auction mechanism through empirical revenue maximization on the predicted bid sequences. We show that the empirical revenue will converge when the prediction period approaches infinity, and a Genetic Programming algorithm can effectively optimize this empirical revenue. Our experiments indicate that the proposed approach is able to produce a much more effective auction mechanism than several baselines.Comment: Twenty-third International Conference on Artificial Intelligence (IJCAI 2013

Characterising the friction and wear between the piston ring and cylinder liner based on acoustic emission analysis

In this paper, an experimental investigation was carried out to evaluate the friction and wear between the cylinder liner and piston ring using acoustic emission (AE) technology. Based on a typical compression ignition (CI) diesel engine, four types of alternative fuels (Fischer-Tropsch fuel, methanol-diesel, emulsified diesel and standard diesel) were tested under dif-ferent operating conditions. AE signals collected from the cylinder block of the testing en-gine. In the meantime, the AE signals in one engine cycle are further segregated into small segments to eliminate the effects of valve events on friction events of cylinder liner. In this way, the resulted AE signals are consistent with the prediction of hydrodynamic lubrication processes. Test results show that there are clear evidences of high AE deviations between dif-ferent fuels. In particular, the methanol-diesel blended fuel produces higher AE energy, which indicates there are more wear between the piston ring and cylinder liner than using standard diesel. On the other hand, the other two alternative fuels have been found little dif-ferences in AE signal from the normal diesel. This paper has shown that AE analysis is an ef-fective technique for on-line assessment of engine friction and wear, which provides a novel approach to support the development of new engine fuels and new lubricants

Convergence Theory of Learning Over-parameterized ResNet: A Full Characterization

ResNet structure has achieved great empirical success since its debut. Recent work established the convergence of learning over-parameterized ResNet with a scaling factor $\tau=1/L$ on the residual branch where $L$ is the network depth. However, it is not clear how learning ResNet behaves for other values of $\tau$. In this paper, we fully characterize the convergence theory of gradient descent for learning over-parameterized ResNet with different values of $\tau$. Specifically, with hiding logarithmic factor and constant coefficients, we show that for $\tau\le 1/\sqrt{L}$ gradient descent is guaranteed to converge to the global minma, and especially when $\tau\le 1/L$ the convergence is irrelevant of the network depth. Conversely, we show that for $\tau>L^{-\frac{1}{2}+c}$, the forward output grows at least with rate $L^c$ in expectation and then the learning fails because of gradient explosion for large $L$. This means the bound $\tau\le 1/\sqrt{L}$ is sharp for learning ResNet with arbitrary depth. To the best of our knowledge, this is the first work that studies learning ResNet with full range of $\tau$.Comment: 31 page

Illusion Media: Generating Virtual Objects Using Realizable Metamaterials

We propose a class of optical transformation media, illusion media, which render the enclosed object invisible and generate one or more virtual objects as desired. We apply the proposed media to design a microwave device, which transforms an actual object into two virtual objects. Such an illusion device exhibits unusual electromagnetic behavior as verified by full-wave simulations. Different from the published illusion devices which are composed of left-handed materials with simultaneously negative permittivity and permeability, the proposed illusion media have finite and positive permittivity and permeability. Hence the designed device could be realizable using artificial metamaterials.Comment: 9 pages, 4 figures, published in Appl. Phys. Lett