Blog
The latest news from Google AI
Third Market Algorithms and Optimization Workshop at Google NYC
Friday, June 15, 2012
Posted by Nitish Korula and Vahab Mirrokni, Google Research, New York
There are fascinating algorithmic and game theoretic challenges in designing both Google’s internal systems and our core products facing hundreds of millions of users. For example, both Google AdWords and the
Ad Exchange
run billions of auctions a day; showing the perfect ad to every user requires simple mechanisms to align incentives while simultaneously optimizing efficiency and revenue.
We think that research in these areas benefits from close cooperation between academia and industry. To this end, last week we held the
Third Market Algorithms and Optimization Workshop at Google
, immediately after
STOC 2012
. We invited several leading academics in these fields to meet with researchers and engineers at Google for a day of talks and discussions.
As a recent winner of the Godel prize,
Éva Tardos
from Cornell led off with a discussion of how to achieve efficiency in sequential auctions where bidders arrive and depart one at a time instead of all bidding simultaneously.
Eyal Manor, Google engineering director for the Ad Exchange, gave an overview of the design and functioning of the exchange. This was an opportunity to have questions answered by the absolute expert, and the participants took full advantage of it!
Costis Daskalakis
and
Pablo Azar
from MIT and
Tim Roughgarden
from Stanford talked about different aspects of Optimal Auctions in Bayesian Settings. Costis talked about efficient implementation of optimal auctions in a class of combinatorial auctions. Both Tim and Pablo discussed optimal auctions in Bayesian settings with limited information. Tim, our other Godel prize winner, promoted the idea of designing simple auction rules that are independent of the distributions of buyers’ valuations, and Pablo presented optimal auction rules using only the mean and standard deviation of buyers’ valuations.
Bobby Kleinberg
from Cornell and
Gagan Goel
from Google NYC presented recent work on pricing with budget constraints. Bobby’s talk was about procurement auctions where the auctioneer acts as a buyer with a budget constraining her procurements. Gagan, on the other hand, discussed Pareto-optimal ascending auctions where the auctioneer is selling to budget-constrained buyers. This has direct applications in Google AdWords auctions as advertisers aim to increase performance while staying within budget constraints.
With our mission of organizing all the world’s information, Google needs superior algorithmic techniques to analyze extremely large data sets. We had two talks on new algorithmic ideas for Big Data. From academia,
Andrew McGregor
gave an introduction to the new field of graph sketching. Though a graph on n nodes is O(n^2)-dimensional, Andy described how to find interesting properties of the graph (such as connectivity, approximate Minimum Spanning Trees, etc.) using only O(n polylog(n)) bits of information. These algorithms were based on clever use of the homomorphic properties of random projections of the graph’s
adjacency matrix
. In the next talk,
Mohammad Mahdian
from Google MTV explained a new model for evolving data; even a ‘simple’ problem like sorting becomes interesting when the order of elements changes over time. Mohammad showed that even if element swaps occur at the same rate as comparisons, one can compute an ordering with
Kendall-Tau distance
O(n ln ln n) from the true ordering at any time, very close to the optimal Ω(n).
Later,
Mukund Sundararajan
from Google MTV discussed algorithmic problems in interpreting and presenting sales data to advertisers. He challenged us to design flexible human-friendly optimization algorithms that can be adopted and tuned by humans. Toward the end of the workshop,
Varun Gupta
, Google NYC postdoctoral researcher, gave a short presentation about the use of primal-dual techniques for online stochastic bin packing with application in assigning jobs to data centers.
We also discussed some of the main activities in the algorithms research group in New York, like the use of primal-dual techniques in online stochastic display ad allocation at Google and large-scale graph mining techniques based on
MapReduce
and
Pregel
.
Corinna Cortes
, Director of Research in New York, and
Alfred Spector
, VP of Research and Special Projects, gave short speeches. Corinna talked about our statistics, machine learning, and NLP research groups in New York, and Alfred challenged us to design mechanisms to take into account fairness in allocations and pricing. For more details, see the
blog post
by our colleague,
‘Muthu’ Muthukrishnan
.
Part of what makes Google a fascinating place to work is the wealth of algorithmic and economic research challenges posed by Google advertising and large-scale data analysis systems. These challenges define research directions for the computer science and economics research communities. Workshops like this and our weekly research seminars help us continue collaborations between Google and academia. We hope to post videos of this workshop shortly, and look forward to organizing many more such events in the future.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2020
May
Apr
Mar
Feb
Jan
2019
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
