5th Google Market Algorithms Workshop

Feb 22, 2019

Google, Mountain View, CA

Market Algorithms is an important area at Google -- both in Research and in Engineering -- bringing research from Algorithmic Game Theory, Online Algorithms, Microeconomics, Statistics, Business and Operations Research, to study, understand, and improve the marketplaces at Google, in particular, Online Advertising. This workshop -- following similar workshops in the past -- brings together a distinguished set of participants, our academic faculty guests and Google's Engineers and Research Scientists working in this area, for a day of exchange of ideas and with an aim towards building further collaborations.

External Participants

Mohammad Akbarpour, Stanford

Santiago Balseiro, Columbia

Guillaume Basse, Berkeley

Dirk Bergemann, Yale

Shuchi Chawla, University of Wisconsin - Madison

Yang Cai, Yale

John Dickerson, University of Maryland

Paul Duetting, London School of Economics

Vineet Goyal, Columbia

MohammadTaghi Hajiaghayi, University of Maryland

Jason Hartline, Northwestern

Ramesh Johari, Stanford

Anthony Kim, Columbia University

Ilan Lobel, NYU

Thodoris Lykouris, Cornell

Edoardo M Airoldi, Harvard

Suraj Malladi, Stanford

Jieming Mao, UPenn

Rahul Mazumder, MIT

Paul Milgrom, Stanford

Ellen Muir, Stanford

Hamid Nazerzadeh, USC

Denis Nekipelov, University of Virginia

Rad Niazadeh, Stanford

Michael Ostrovsky, Stanford

Alvin Roth, Stanford

Tim Roughgarden, Columbia

Aviad Rubenstein, Stanford

Dan Russo, Columbia

Amin Saberi, Stanford

Aravind Srinivisan, University of Maryland

Panos Toulis, Chicago Booth

Johan Ugander, Stanford

Vijay Vazirani, University of California, Irvine

Steven Wu, University of Minnesota


9:00-9:30 Registration

Session 1: Learning and Auctions 9:30-10:45

Chair: Sunita Verma

    1. Tim Roughgarden: Learning Near-Optimal Auctions
    2. Mohammad Mahdian: Static Incentive-aware Learning: Large Markets and Beyond Differential Privacy
    3. Steven Wu: Incentivizing Exploration with Unbiased Histories
    4. Negin Golrezaei: Dynamic Incentive-aware Learning: Robust Pricing in Contextual Auctions
    5. Paul Duetting: Deep Learning for Auctions
    6. Sebastien Lahaie: Testing Truthfulness in Auctions

10:45-11:15 Break

Session 2: Auction Design 11:15-12:30

Chair: Vahab Mirrokni

    1. Mohammad Akbarpour: Credible Mechanisms
    2. Balu Sivan: Dynamic Auctions Robust to Buyer Heterogeneity
    3. Florin Constantin: Session Auction for App Ads
    4. Hamid Nazerzadeh: Revenue Optimization for Shallow Auctions
    5. Song Zuo/Renato Paes Leme: Simple dynamic mechanisms without predicting future
    6. Yang Cai: Simple Mechanisms for Two-sided Markets

12:30-2PM Lunch

Session 3: Online Optimization, Budgets, and Bidding. 2PM-3:15PM

Chair: Aranyak Mehta

    1. Jason Hartline: Dashboard Mechanisms
    2. Santiago Balseiro/Anthony Kim: Budget planning strategies: Convergence and equilibrium analysis
    3. D. Sivakumar: Can RL learn classic allocation algorithms?
    4. Ashwinkumar Varadaraja: Auto-bidding Optimization
    5. MohammadTaghi Hajiaghayi: Online Decision-making: Prophets & Secretaries
    6. Aravind Srinivasan/John Dickerson: Balancing Relevance & Diversity in Online Matching

3:15-3:45 Break

Session 4: Experimental design and simulations for Auctions 3:45-5PM Break

Chair: Gagan Aggarwal

    1. Jean Pouget-Abadie: Experimental design under interference
    2. Guillaume Basse: Minimax designs for assessing long-term causal effects
    3. Johan Ugandar: Evaluating stochastic seeding strategies in networks
    4. Saeed Alaei: Ad Auction Simulations: Challenges & Insights
    5. Shuchi Chawla: A/B Testing for Auctions

5PM-6PM Final Remarks and Poster Discussions

Address and Arrival Instructions:

Google - Bldg CL2

1200 Crittenden Lane

Mountain View, CA 94043

Attendees please park anywhere in the lot and check in with Bldg CL2 Reception between 8:30am-9:30am Friday February 22