KDD 2012 Conference

KDD 2012 was held in Beijing, China from 12th-16th August. Here is the main link to the conference: http://kdd2012.sigkdd.org/ . Complete list of accepted papers is available at: http://kdd2012.sigkdd.org/papers.shtml

Best Paper Awards

Some of the papers covering topics of crowdsourcing, social networks, online learning, online auctions and advertisements :

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WebSci 2012 Conference

WebSci 2012 was held in Chicago, USA from 22nd-24th June. Here is the main link to the conference: http://www.websci12.org. Complete list of accepted papers is available here.

Best Paper Awards

Papers from session of Social Computing and Collective Intelligence

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AAAI 2012 Conference

AAAI 2012 was held in Toronto, Canada from 22nd-26th July. Here is the main link to the conference: http://www.aaai.org/Conferences/AAAI/aaai12.php/ . Complete list of accepted papers is available at: http://www.aaai.org/Conferences/AAAI/2012/aaai12accepts.pdf

Best Paper Award goes to: MOMDP: a Solution for modelling adaptive management problems. Iadine Chadès, Josie Carwardine, Tara G. Martin, Samuel Nicol, Régis Sabbadin, Olivier Buffet

Some of the papers covering topics of Machine Learning, Game Theory and Social Computing

  • Automated Strategies for Determining Rewards for Human Work. Amos Azaria, Yonatan Aumann, Sarit Kraus
  • Sembler: Ensembling Crowd Sequential Labeling for Improved Quality. Xian Wu, Wei Fan, Yong Yu
  • Predicting Disease Transmission from Geo-Tagged Micro-Blog Data. Adam Sadilek, Henry Kautz, Vincent Silenzio
  • Fairness and Welfare through Redistribution When Utility Is Transferable. Ruggiero Cavallo
  • DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization
    Problems
    . Brammert Ottens, Christos Dimitrakakis, Boi Faltings
  • Learning to Learn: Algorithmic Inspirations from Human Problem Solving. Ashish Kapoor, Bongshin Lee, Desney Tan, Eric Horvitz
  • Knapsack Based Optimal Policies for Budget-Limited Multi-Armed Bandits. Long Tran-Thanh, Archie Chapman, Alex Rogers, Nicholas R. Jennings
  • Learning Qualitative Models by Demonstration. Thomas R. Hinrichs, Kenneth D. Forbus
  • A Convex Formulation for Learning from Crowds. Hiroshi Kajino, Yuta Tsuboi, Hisashi Kashima
  • Computing Optimal Strategies to Commit to in Stochastic GamesJoshua Letchford, Liam MacDermed, Vincent Conitzer, Ronald Parr, Charles L. Isbell
  • Optimizing Payments in Dominant-Strategy Mechanisms for Multi-Parameter DomainsLachlan Dufton, Victor Naroditskiy, Maria Polukarov, Nicholas R. Jennings
  • Strategic Advice Provision in Repeated Human-Agent InteractionsAmos Azaria, Zinovi Rabinovich, Sarit Kraus, Claudia V. Goldman, Ya’akov Gal
  • Negotiation in Exploration-Based EnvironmentIsrael Sofer, David Sarne, Avinatan Hassidim
  • Approximately Revenue-Maximizing Auctions for Deliberative AgentsL. Elisa Celis, Anna R. Karlin, Kevin Leyton-Brown, C. Thach Nguyen, David R. M. Thompson
  • Sequential Decision Making with Rank Dependent Utility: A Minimax Regret ApproachGildas Jeantet, Patrice Perny, Olivier Spanjaard
  • Discovering Spammers in Social NetworksYin Zhu, Xiao Wang, Erheng Zhong, Nanthan N. Liu, He Li, Qiang Yang
  • Optimal Auctions for Spiteful Bidders. Pingzhong Tang, Tuomas Sandholm
  • Online Task Assignment in Crowdsourcing Markets. Chien-Ju Ho, Jennifer Wortman Vaughan
  • Algorithmic and Human Teaching of Sequential Decision TasksMaya Cakmak, Manuel Lopes
  • Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion ProcessWei Chen, Wei Lu, Ning Zhang
  • Quality Expectation-Variance Tradeoffs in Crowdsourcing ContestsXi Alice Gao, Yoram Bachrach, Peter Key, Thore Graepel
  • Dynamically Switching between Synergistic Workflows for CrowdsourcingChristopher H. Lin, Mausam, Daniel S. Weld
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AAMAS 2012 Conference

AAMAS 2012 was held in Valencia, Spain from 4th-8th June. Here is the main link to the conference: http://aamas2012.webs.upv.es. Complete list of accepted papers is available here

Best Paper Awards

Some of the papers covering topics of Machine Learning, Game Theory and Social Computing

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EC 2012 Conference

Electronics Commerce(EC) 2012 conference was held in Valencia, Spain from 4th-8th June (EC 2012 link). It was co-located with AAMAS 2012 (AAMAS 2012 link). Here are some of the papers from EC12.

Best Papers Award

  • Payment Rules through Discriminant-Based Classifiers. Paul Duetting, Felix Fischer, Pichayut Jirapinyo, John Lai, Benjamin Lubin and David Parkes.
    Authors use SVM to come up with payment schemes which minimize ex-post regret and show them to be close to incentive compatible payments.
  • Improving the Effectiveness of Time-Based Display Advertising. Daniel G. Goldstein, R. Preston MCafee and Siddharth Suri.
    Authors shows that impact display advertisement has some diminishing returns. Hence displaying one ad for t time is less efficient compared to displaying two ads for t/2 time.

Some of the papers covering different flavors

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FOCS 2012 Notification

Accepted papers in FOCS 2012 were out couple weeks ago and the list is available at: http://theory.stanford.edu/~tim/focs12/accepted.txt

Here are some of the papers related the areas of Machine Learning, Game Theory and Social Computing:

  • A PTAS for Computing the Supremum of Gaussian Processes [ abstract ]
  • Matching with our Eyes Closed
  • A Tight Combinatorial Algorithm for Submodular Maximization Subject to a Matroid Constraint [abstract]
  • A Tight Linear Time (1/2)-Approximation for Unconstrained Submodular Maximization [blog]
  • Learning Topic Models — Going beyond SVD [abstract ]
  • Active Property Testing [pdf]
  • The Dynamics of Inuence Systems [abstract]
  • Online Matching with Stochastic Rewards
  • Finding Correlations in Subquadratic Time, with Applications to Learning Parities and Juntas[abstract]
  • The Exponential Mechanism for Social Welfare: Private, Truthful, and Nearly Optimal [abstract]
  • Optimal Multi-Dimensional Mechanism Design: Reducing Revenue to Welfare Maximizatio Equilibria
  • Concave Generalized Flows with Applications to Market Equilibria [abstract]
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Mechanical Turk Experimentation

I did some experiments on Mechanical Turk platform, to get an understanding of the platform and see the quality of data that flows in. Here are some details.

Account Setup

  • You can register on this link: https://www.mturk.com/mturk/welcome
  • You can get yourself registered as “Requester” to publish the HIT, and as “Worker” to complete the HIT. (HIT stands for Human Intelligence Tasks)
  • It took me just 2 mins for registration, given I have Amazon account with US adress and US credit card. If you are out of US, it could be somewhat tricky on how to do this since currently this is only available for US residents.
  • Money management of Requester is easy. I just brought 5 dollars credit online which is then used to complete the tasks I publish.
  • The credit of Worker goes into account once the tasks have been approved by requester. Normally, the tasks get auto-approved in 1 week under default settings.
  • HIT is designed in form of html page, which should take about 10 mins to finish. Then, you need to fill in some details like a) minimal qualification of work (at least X% approval rate in past), b) budget per task , c) total tasks in the HIT and so on. Overall, in 20 mins, you should be able to publish a HIT.

Recording the results

  • Once the HIT is published, the results start flowing in quite instantaneously. I published my HIT of 100 tasks (details below) around 9pm Pacific Time and the HIT was completed in a timeframe of 5 hrs.
  • The results can be viewed in almost real time and you do not need to wait for whole HIT to complete. The results can be exported to an Excel file.

Experimental HIT about Facebook

Answer a short survey related to your Facebook

1. Since how many years have you been using Facebook?

  • Less than 1 year
  • 1 – 2 years
  • 2 – 3 years
  • 3 or more years

2. Roughly, how many Facebook friends you have in following years? (enter 0 if you were not using Facebook at time of question)

  • Number of friends in your Facebook, currently (June 2012)
  • Number of friends in your Facebook, one year ago (June 2011)
  • Number of friends in your Facebook, two years ago (June 2010)

3. Roughly, how many minutes in a day you spend on using Facebook

  • Minutes spend on Facebook per day, currently (June 2012)
  • Minutes spend on Facebook per day, one year ago (June 2011)
  • Minutes spend on Facebook per day, two years ago (June 2010)

4. Roughly, from 1$ to 10000$, how much value would you assign to your Facebook account if someone asks you to sell your account? (enter 0 if you were not using Facebook at time of question)

  • Value of your Facebook account, currently (June 2012)
  • Value of your Facebook account one, year ago (June 2011)
  • Value of your Facebook account two, years ago (June 2010)

5. (Optional) Please provide any comments you may have below, we appreciate your input

Experimental HIT results

  • It is important to run your HIT for a small bacth so as to make sure that all the fields are correct. It is also important to analyze the workers response to ensure that they are not getting confused and frustated by your HIT.
  • In total, I had batch of 100 tasks in the HIT. Here are some of the interesting responses.
    • Feedback 1
      • Years Usage = 3
      • # Friends in 2012, 2011, 2010 = 368, 250, 150
      • Time spent in 2012, 2011, 2010 (in minutes) = 30, 120, 30
      • Value of Facebook account to sell in 2012, 2011, 2010 (in dollars) = 5000, 2500, 1250
      • Optional Comments = I’m trying to quit
    • Feedback 2
      • Years Usage = 3
      • # Friends in 2012, 2011, 2010 = 380, 300, 170
      • Time spent in 2012, 2011, 2010 (in minutes) = 60, 180, 120
      • Value of Facebook account to sell in 2012, 2011, 2010 (in dollars) = 8000, 10000, 8000
      • Optional Comments = All of my friends including me, do not like timeline on facebook. It should be made optional
    • Feedback 3
      • Years Usage = 4
      • # Friends in 2012, 2011, 2010 = 780, 500, 300
      • Time spent in 2012, 2011, 2010 (in minutes) = 40, 30, 20
      • Value of Facebook account to sell in 2012, 2011, 2010 (in dollars) = 10000, 8000, 2000
      • Optional Comments = I enjoy facebook!
  • Overall, it seemed that workers liked the HIT. Out of 100 workers, about 20 gave a positive feedback in “Optional Comments” section. This is useful to know since I can now deploy this kind of HIT on bigger scale to get more data.
    • This was a variety HIT, I like it..
    • It was interesting thinking back to how my facebook/my interaction with facebook has changed throughout the years.
    • Very interesting and valuable study.
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Welcome to CrowdML Blog

The purpose of this blog is to share ideas, discuss interesting papers and keep track of latest research in Crowdsourcing and Machine Learning.

To begin with, I will consolidate my blog posts from http://adishsingla.com/ into CrowdML blog.

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