Influential Papers for 2013
This post has been viewed 2,919 times.
Googlers across the company actively engage with the scientific community by publishing technical papers, contributing open-source packages, working on standards, introducing new APIs and tools, giving talks and presentations, participating in ongoing technical debates, and much more. Our publications offer technical and algorithmic advances, feature aspects we learn as we develop novel products and services, and shed light on some of the technical challenges we face at Google. Below are some of the especially influential papers co-authored by Googlers in 2013. In the coming weeks we will be offering a more in-depth look at some of these publications.
Algorithms
Online Matching and Ad Allocation, by Aranyak Mehta [Foundations and Trends in Theoretical Computer Science]
Matching is a classic problem with a rich history and a significant impact, both on the theory of algorithms and in practice. There has recently been a surge of interest in the online version of the matching problem, due to its application in the domain of Internet advertising. The theory of online matching and allocation has played a critical role in the design of algorithms for ad allocation. This monograph provides a survey of the key problems and algorithmic techniques in this area, and provides a glimpse into their practical impact.
Machine Learning
Ad Click Prediction: a View from the Trenches, by H. Brendan McMahan, Gary Holt, D. Sculley, Michael Young, Dietmar Ebner, Julian Grady, Lan Nie, Todd Phillips, Eugene Davydov, Daniel Golovin, Sharat Chikkerur, Dan Liu, Martin Wattenberg, Arnar Mar Hrafnkelsson, Tom Boulos, Jeremy Kubica [KDD]
How should one go about making predictions in extremely large scale production systems? We provide a case study for ad click prediction, and illustrate best practices for combining rigorous theory with careful engineering and evaluation. The paper contains a mix of novel algorithms, practical approaches, and some surprising negative results.
Learning kernels using local rademacher complexity, by Corinna Cortes, Marius Kloft, Mehryar Mohri [Advances in Neural Information Processing Systems (NIPS 2013)]
This paper shows how the notion of local Rademacher complexity, which leads to sharp learning guarantees, can be used to derive algorithms for the important problem of learning kernels. It also reports the results of several experiments with these algorithms which yield performance improvements in some challenging tasks.
Efficient Estimation of Word Representations in Vector Space, by Tomas Mikolov, Kai Chen, Greg S. Corrado, Jeffrey Dean [ICLR Workshop 2013]
We describe a simple and speedy method for training vector representations of words. The resulting vectors naturally capture the semantics and syntax of word use, such that simple analogies can be solved with vector arithmetic. For example, the vector difference between ‘man’ and ‘woman’ is approximately equal to the difference between ‘king’ and ‘queen’, and vector displacements between any given country’s name and its capital are aligned. We provide an open source implementation as well as pre trained vector representations at word2vec.googlecode.com
Large-Scale Learning with Less RAM via Randomization, by Daniel Golovin, D. Sculley, H. Brendan McMahan, Michael Young [Proceedings of the 30 International Conference on Machine Learning (ICML)]
We show how a simple technique — using limited precision coefficients and randomized rounding — can dramatically reduce the RAM needed to train models with online convex optimization methods such as stochastic gradient descent. In addition to demonstrating excellent empirical performance, we provide strong theoretical guarantees.
Tags:
