Grant Schoenebeck

 

I am an associate professor in the School of Information at the University of Michigan School.

Research Interests:

My current research combines machine learning tools and economic approaches (e.g game theory, mechanism design, and information design) to develop and analyze systems for eliciting and aggregating information from of diverse group of agents with varying information, interests, and abilities.

This work applies to scenarios where a collective decision-making process is required, such as peer grading, peer review, crowd-sourcing, content moderation, misinformation detection, surveys, and employment hiring/evaluation.

More broadly, I am interested in multi-agent systems, a subfield of AI; data economics; and algorithmic game theory.

News:

I am always looking for excellent PhD students. Please apply to UMSI and include my name in your application. (I will not see your application if you do not apply to the School of Information.)
I am interested in students looking to attack problems using both theoretical (formal mathematical) and other methods.

CV:

Curriculum Vitae

Funding:

NSF CAREER Award Recipient
Google Faculty Award Recipient
Facebook Faculty Award Recipient
NSF Algorithms in the Field Grant Recipient
NSF CCF Small Recipient (3x)

Students and Postdocs:

Bo Li - former Post-Doc, now TT at UIUC

Yuqing Kong - former PhD student, now TT at Peking University

Fang-Yi Yu - former PhD Student/Post-Doc, now TT at George Mason University

Biaoshuai Tao - former PhD Student, now TT at SJTU

Noah Burrell - former PhD Student, now at Epistemix

Yichi Zhang - current PhD Student

Md Sanzeed Anwar - current PhD Student

Shengwei Xu - current PhD Student

Christian David Gamba Contrera - current PhD Student

Selected Recent Works:

Benchmarking LLMs' Judgments with No Gold Standard
S. Xu, Y. Lu, Y. Zhang, G. Schoenebeck, Y. Kong
Arxiv

Eliciting Informative Text Evaluations with Large Language Models
Y. Lu, S. Xu, Y. Zhang, Y. Kong, G. Schoenebeck
EC '24, PDF, Arxiv

Measurement Integrity in Peer Prediction: A Peer Assessment Case Study
N. Burrell, G. Schoenebeck
EC '23, Arxiv

Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences
G. Schoenebeck, b. Tao
NeurIPs, 2021, Arxiv

Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
G. Schoenebeck, F. Yu.
ITCS 2021, Arxiv.

Papers:

Benchmarking LLMs' Judgments with No Gold Standard
S. Xu, Y. Lu, Y. Zhang, G. Schoenebeck, Y. Kong
Arxiv

Eliciting Informative Text Evaluations with Large Language Models
Y. Lu, S. Xu, Y. Zhang, Y. Kong, G. Schoenebeck
EC '24, PDF, Arxiv

Spot Check Equivalence: an Interpretable Metric for Information Elicitation Mechanisms
S. Xu, Y. Zhang, P. Resnick, G. Schoenebeck
WWW '24, arXiv

Filter Bubble or Homogenization? {D}isentangling the Long-Term Effects of Recommendations on User Consumption Patterns
S. Anwar and G. Schoenebeck, P. Dhillon
WWW '24, arXiv

Exit Ripple Effects: Understanding the Disruption of Socialization Networks Following Employee Departures
D. Gamba, Y. Yu, Y. Yuan, G. Schoenebeck, D. Romero
WWW '24, arXiv

Eliciting Honest Information From Authors Using Sequential Review
Y. Zhang, G. Schoenebeck, W. Su
AAAI '24, arXiv

Testing Conventional Wisdom (of the Crowd)
N. Burrell, G. Schoenebeck
UAI'23

Measurement Integrity in Peer Prediction: A Peer Assessment Case Study
N. Burrell, G. Schoenebeck
EC '23, Arxiv

The Wisdom of Strategic Voting
Q. Han, G. Schoenebeck, B. Tao, L. Xia
EC '23, Arxiv

High-Effort Crowds: Limited Liability via Tournaments
Y. Zhang, G. Schoenebeck
WWW'23

Multitask Peer Prediction With Task-dependent Strategies
Y. Zhang, G. Schoenebeck
WWW'23

Two Strongly Truthful Mechanisms for Three Heterogeneous Agents Answering One Question
G. Schoenebeck, F. Yu
TEAC, 2023, Wine 2020, pdf

False Consensus, Information Theory, and Prediction Markets.
Y. Kong, G. Schoenebeck
ITCS '23 , arXiv

A System-Level Analysis of Conference Peer Review.
Y. Zhang, F. Yu, G. Schoenebeck, and D. Kempe
EC '22.

Optimal Local Bayesian Differential Privacy Over Markov Chains.
D. Chakrabarti, J. Gao, A. Saraf, G. Schoenebeck, and F. Yu
AAMAS '22 (extended abstract), arXiv.

Bayesian Persuasion in Sequential Trials.
S. Su, V. Subramanian, and G. Schoenebeck
WINE '21, arXiv.

Adaptive Greedy Versus Non-adaptive Greedy for Influence Maximization
W. Chen, B. Peng, G. Schoenebeck, B. Tao
JAIR '22, AAAI '20, arXiv

BONUS! Maximizing Surprise Labels
Z. Huang, Y. Kong, T. X. Liu, G. Schoenebeck, S. Xu
WWW '22, Arxiv

Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences
G. Schoenebeck, b. Tao
NeurIPs, 2021, Arxiv, 2021

SURPRISE! and When to Schedule It
Z. Huang, S. Xu, Y. Shan, Y. Lu, Y. Kong, T. X. Liu, G. Schoenebeck
IJCAI '21, Arxiv

Survey Equivalence: A Procedure for Measuring Classifier Accuracy Against Human Labels
P. Resnick, Y. Kong, G. Schoenebeck, T. Weninger
Arxiv, 2021

Information Elicitation from Rowdy Crowds
G. Schoenebeck, F. Yu, Y. Zhang
WWW '21

Timely Information from Prediction Markets
G. Schoenebeck, C. Yu, F. Yu
AAMAS '21, Arxiv

Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
G. Schoenebeck, F. Yu.
ITCS 2021, Arxiv

Relaxing Common Belief for Social Networks
N. Burrell, G. Schoenebeck
ITCS 2021, Arxiv

Escaping Saddle Points in Constant Dimensional Spaces: An Agent-based Modeling Perspective
G. Schoenebeck, F. Yu
EC' 2020, pdf

Limitations of greed: Influence maximization in undirected networks re-visited
G. Schoenebeck, B. Tao, F. Yu
AAMAS '20, arXiv

Information Elicitation Mechanisms for Statistical Estimation
Y. Kong, G. Schoenebeck, B. Tao, F. Yu
AAAI '20, pdf

Influence Maximization on Undirected Graphs: Towards Closing the (1-1/e) Gap
G. Schoenebeck, B. Tao
EC '19, Video Presentation, TEAC '20

Outsourcing computation: the minimal refereed mechanism.
Y. Kong, C. Peikert, G. Schoenebeck, B. Tao
Wine '19, arXiv

Think globally, act locally: On the optimal seeding for nonsubmodular influence maximization.
G. Schoenebeck, B. Tao, F. Yu
Approx/Random '19, arXiv

An Information Theoretic Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Y. Kong, G. Schoenebeck.
TEAC '19, Arxiv

Complex Contagions in Charitable Donations
J. Gao, G. Ghsemisefeh and J. Jones, G. Schoenebeck.
SocArXiv '19.

Beyond Worst-Case (In)approximability of Nonsubmodular Influence Maximization
G. Schoenebeck, B. Tao
ToCT '19, Wine '17, arXiv '17

Think Globally, Act Locally: On the Optimal Seeding for Nonsubmodular Influence Maximization
G. Schoenebeck, B. Tao, F. Yu
Approx/Random '19

The Volatility of Weak Ties: Co-evolution of Selection and Influence in Social Networks
J. Gao, G. Schoenebeck, F. Yu
AAMAS '19, pdf

Outsourcing Computation: the Minimal Refereed Mechanism
Y. Kong, C. Peikert, G. Schoenebeck, B. Tao,
Wine'19, arXiv

Social learning with questions
S. Su, V. G. Subramanian, G. Schoenebeck
NetEcon '19, arXiv

Water from Two Rocks: Maximizing the Mutual Information
Y. Kong, G. Schoenebeck
EC '18 , arXiv '18

Eliciting Expertise without Verification
Y. Kong, G. Schoenebeck
EC '18, arXiv '18

Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
X. Ma, B. Li, Y. Wang, S. M. Erfani, S. Wijewickrema, M. E. Houle, G. Schoenebeck, D. Song, J. Bailey
ICLR '18, arXiv '18

Consensus of Interacting Particle Systems on Erdos-Renyi Graphs
G. Schoenebeck, F. Yu
SODA '18, pdf

Optimizing Bayesian Information Revelation Strategy in Prediction Markets: the Alice Bob Alice Case
Y. Kong, G. Schoenebeck.
ITCS '18

Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity
Y. Kong, G. Schoenebeck..
ITCS' 18, Arxiv '16

Contention-Aware Lock Scheduling for Transactional Databases
B. Tian, J. Huang, B. Mozafari, G. Schoenebeck
VLDB'18

Don't Be Greedy: Leveraging Community Structure to Find High Quality Seed Sets for Influence Maximization
R. Angell, G. Schoenebeck
WINE'17, arXiv '16

Cascades and Myopic Routing in Nonhomogeneous Kleinbergs Small World Model
J. Gao, G. Schoenebeck, F. Yu
WINE '17

A Top-Down Approach to Achieving Performance Predictability in Database Systems
J. Huang, B. Mozafari, G. Schoenebeck, T. Wenisch
SIGMOD '17

Engineering Agreement:The Naming Game with Asymmetric and Heterogeneous Agents
J. Gao, B. Li, G. Schoenebeck, F. Yu
AAAI '17

How Complex Contagions Spread Quickly in Preferential Attachment Models and Other Time-Evolving Networks
R.Ebrahimi, J. Gao, G. Ghasemiesfeh, G. Schoenebeck
IEEE Transactions on Network Science and Engineering '17, arXiv '14

Sybil Detection Using Latent Network Structure
A. Snook, G. Schoenebeck, F. Yu.
EC '16

General Threshold Model for Social Cascades: Analysis and Simulations
J. Gao, G. Ghasemiesfeh, G. Schoenebeck, F. Yu
EC '16

Complex Contagions on Configuration Model Graphs with a Power-Law Degree Distribution
G. Schoenebeck, F. Yu
WINE '16

Putting Peer Prediction Under the Micro(economic)scope and Making Truth-telling Focal
Y. Kong, K. Ligett, G. Schoenebeck.
WINE '16, Arxiv '15

Identifying the Major Sources of Variance in Transaction Latencies: Towards More Predictable Databases
J. Huang, B. Mozafari, G. Schoenebeck, T. Wenisch
arXiv'16

A Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
Y. Kong, G. Schoenebeck..
Arxiv '15

Complex Contagions in Kleinberg's Small World Model
R. Ebrahimi, J. Gao, G. Ghasemiesfeh, G. Schoenebeck
ITCS '15

Buying Private Data without Verification
A. Ghosh, K. Ligett, A. Roth, G. Schoenebeck.
EC '14

Characterizing Strategic Cascades on Networks
T. Martin, G. Schoenebeck, M. Wellman
EC '14

Graph Isomorphism and the Lasserre Hierarchy
P. Codenotti, G. Schoenebeck, A. Snook
arXiv '14

Better Approximation Algorithms for the Graph Diameter.
S. Chechik, D. H. Larkin, L. Roditty, G. Schoenebeck, R. E. Tarjan, V. V. Williams
SODA '14

Potential Networks, Contagious Communities, and Social Network Structure.
G. Schoenebeck
WWW '13

Conducting Truthful Surveys, Cheaply
A. Roth, G. Schoenebeck.
EC '12

Finding Overlapping Communities in Social Networks: Toward a Rigorous Approach
S. Arora, R. Ge, S. Sachdeva, G. Schoenebeck
EC '12

Social Learning in a Changing World
R. Frongillo, G. Schoenebeck, O. Tamuz
Wine '11

General Hardness Amplification of Predicates and Puzzles
T. Hollenstein, G. Schoenebeck
TCC '11

Constrained Non-monotone Submodular Maximization: Offline and Secretary Algoritms.
A. Gupta, A. Roth. G. Schoenebeck, K. Talwar.
WINE '10

The Limitations of Linear and Semidefinite Programs
G. Schoenebeck
PhD Thesis, 2010

Optimal Testing of Reed-Muller Codes
A. Bhattacharyya, S. Kopparty, G. Schoenebeck, M. Sudan, D. Zuckerman
FOCS '10.

Detecting Spam in a Twitter Network.
S. Yardi, D. Romero, G. Schoenebeck. d. boyd.
First Monday '10

Reaching Consensus on Social Networks
E. Mossel, G. Schoenebeck
ICS '10.

On the Complexity of Nash Equilibria of Action-Graph Games
C. Daskalakis, G. Schoenebeck, G. Valiant, P. Valiant
Soda '09.

Linear Level Lasserre Lower Bounds for Certain k-CSPs
G. Schoenebeck.
FOCS '08

Tight Integrality Gaps for Lovasz-Schrijver LP Relaxations of Vertex Cover and Max Cut
G. Schoenebeck, L. Trevisan, M. Tulsiani.
STOC '07

A Linear Round Lower Bound for Lovasz-Schrijver SDP Relaxations of Vertex Cover
G. Schoenebeck, L. Trevisan, M. Tulsiani.
CCC '07

Chora: Expert-based Peer-to-peer web search
H. Gylfason, O. Khan, G. Schoenebeck
AP2PC workshop at AAMAS '06.

The computational Complexity of Concisely Represented Games
G. Schoenebeck, S. Vadhan.
EC '06. ACM Transactions on Computation Theory 2012.

GrowRange: Anytime VCG-Based Mechanisms
D. Parkes, G. Schoenebeck.
AAAI '04.

Teaching:

Fall 2023

SI: 670: Applied Machine Learning

Winter 2023

EECS 547 / SI: 652: Incentives and Strategic Behavior in Computational Systems

SI 602: Mathematical Foundations for Data Science

Fall 2022

SI: 670: Applied Machine Learning

Winter 2021

SIADS 502: Math Methods for Data Science

SIADS 521: Visual Exploration of Data

Fall 2020

EECS 547 / SI: 652: Incentives and Strategic Behavior in Computational Systems

SI: 670: Applied Machine Learning

SIADS 502: Math Methods for Data Science

Winter 2020

SIADS 502: Math Methods for Data Science

SIADS 521: Visual Exploration of Data

Fall 2019

EECS 547 / SI: 652: Electronic Commerce (about algorithmic game theory)

SI: 670: Applied Machine Learning

Fall 2017

EECS 547 / SI: 652: Electronic Commerce (about algorithmic game theory) .

Winter 2017

EECS 376 Foundations of Computing

Fall 2015

EECS 598-06 Randomness and Computation

Winter 2015

EECS 376 Foundations of Computing

Fall 2014

EECS 574 Computational Complexity Theory

Fall 2013

EECS 574 Computational Complexity Theory

Winter 2013

EECS 203 Discrete Math

Fall 2012

EECS 598-06 Social Networks: Reasoning about Structure and Processes This class looked at social networks research and how a theoretical computer science prospective both brings new questions and gains additional insights into this growing body of research. Schedule and readings on the website.

New Jersey Governor's School: The Math Behind the Maching, Summer 2012

New Jersey Governor's School: The Math Behind the Maching, Summer 2011

Contact Information:

3341 North Quad
501 State St.
University of Michigan
Ann Arbor, MI 48109-2121
Phone: (734)647-4712
Email:

Personal:

I was born in Green Bay, WI and moved to Wichita, KS when I was nine. I attended Harvard University, graduating with highest honors in mathematics. Afterwards, I attended Oxford University as the von Clemm fellow and studied theology. I received my PhD from UC Berkeley in computer science where I was advised by Luca Trevisan. Subsequently I was the Simons Foundation Postdoctoral Research Fellow in Theoretical Computer Science at Princeton University.