VS
Portrait of Vasilis Syrgkanis
Huang Engineering Center
Office 252
475 Via Ortega
Stanford, CA 94305
vsyrgk [at] stanford.edu
Curriculum vitae Stanford Causal AI Lab

Stanford University · School of Engineering

Vasilis Syrgkanis

Assistant Professor of Management Science and Engineering

By courtesy: Computer Science and Electrical Engineering
James and Anna Marie Spilker Faculty Fellow

Affiliations ICME · Operations Research · Statistical ML · CS Theory · Stanford AI Lab · HAI

I study how to make reliable decisions from data. My research brings together causal inference, statistics, machine learning and AI, econometrics, and the design of algorithms and markets. I lead the Stanford Causal AI Lab and serve as an Associate Director of the Stanford Causal Science Center.

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Guidance for interested students

Office hours, advising, and courses

If you are a Stanford undergraduate or master's student, feel free to come by my office hours. If you are a Stanford Ph.D. student and interested in working with me, please reach out. If you want to pursue Ph.D. studies at Stanford, please apply to the relevant Ph.D. programs (primarily MS&E and ICME, and potentially also CS or EE) and list me as a faculty member of interest (I will unfortunately not be able to respond to individual emails regarding Ph.D. applications).

If you are interested in learning more about causal machine learning and artificial intelligence, I offer courses on Applied Causal Inference Powered by ML and AI and Foundations of Causal Machine Learning. For topics at the intersection of machine learning, artificial intelligence, and game theory, see Game Theory, Data Science and AI.

Academic background

Research and education

Prior to joining Stanford in September of 2022, I was a Principal Researcher at Microsoft Research, New England, where I co-led the project on Automated Learning and Intelligence for Causation and Economics (ALICE) and was a member of the EconCS and StatsML groups.

I received my Ph.D. in Computer Science from Cornell University, advised by Eva Tardos, and then spent two years as a postdoctoral researcher at Microsoft Research, New York, in the Algorithmic Economics and Machine Learning groups. I received my diploma in EECS from the National Technical University of Athens, Greece.

At Microsoft Research, I had the opportunity to work with an amazing set of summer interns, including Nika Haghtalab, Gautam Kamath, Jieming Mao, Jonas Mueller, Yichen Wang, Steven Wu, Juba Ziani, Dylan Foster, Khashayar Khosravi, Mert Demirer, Nishanth Dikkala, Manolis Zampetakis, Michael Celentano, Gali Noti, Rahul Singh, Dhruv Rohatgi, Anish Agarwal, Matthew O'Keefe, Andrew Bennett, and Korinna Frangias.

For seminar organizers and the press

Bio & photo

Use this short biography and portrait for seminar announcements, event programs, and other academic materials.

Vasilis Syrgkanis is an Assistant Professor of Management Science and Engineering and (by courtesy) of Computer Science and Electrical Engineering at Stanford University. His research interests lie in the areas of machine learning and artificial intelligence, causal inference, econometrics, online and reinforcement learning, game theory, mechanism design, and algorithm design. Until August 2022, he was a Principal Researcher at Microsoft Research, New England, where he was a member of the EconCS and StatsML groups and co-led the project on Automated Learning and Intelligence for Causation and Economics. He received his Ph.D. in Computer Science from Cornell University. His research has received best paper awards at several top-tier machine learning and artificial intelligence conferences (ACM EC, NeurIPS, and COLT). He is the recipient of a 2022 Amazon Research Award, a 2023 Google Research Scholar Award, the 2023 Bodossaki Distinguished Young Scientist Award, a 2024 NSF CAREER Award, a 2025 Balakrishnan Early Career Award, and a 2026 Intuit Faculty Research Award.

Portrait of Vasilis Syrgkanis for seminar announcements
Portrait for event and seminar materials Download JPEG (1500 × 1500)

Selected course and lecture resources

Teaching materials

Course sites and teaching resources from Stanford, MIT, and Cornell.

Course websites

Tutorials and surveys

Books

Complete research record

Publications

A complete, searchable list of peer-reviewed publications, working papers, surveys, theses, and other research.

Showing all 128 publications and archival works. Search titles, authors, or venues.

2026

  1. Corrupt Plans, Clean Traces: Evading Chain-of-Thought Monitoring with Plan Injection

    Keertana Chidambaram, Andrew Ilyas, Vasilis Syrgkanis, NeurIPS26

  2. CausalSmith: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference

    Jiyuan Tan, Vasilis Syrgkanis, Arxiv26

  3. The Partial Testimony of Logs: Evaluation of Language Model Generation under Confounded Model Choice

    Jikai Jin, Vasilis Syrgkanis, Arxiv26

  4. Partial Identification of Policy-Relevant Treatment Effects with Instrumental Variables via Optimal Transport

    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, Arxiv26

  5. Adaptive Estimation and Inference in Conditional Moment Models via the Discrepancy Principle

    Jiyuan Tan, Vasilis Syrgkanis, Arxiv26

  6. The Double Diagonal Estimator: Reducing Bias in Two-Sided Marketplace Experiments

    Saanvi Chawla, Vasilis Syrgkanis, EC26

  7. CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation

    Ayush Sawarni, Jiyuan Tan, Vasilis Syrgkanis, Arxiv26

  8. Statistical Inference and Learning for Shapley Additive Explanations (SHAP)

    Justin Whitehouse, Ayush Sawarni, Vasilis Syrgkanis, Arxiv26

  9. Sharp Structure-Agnostic Lower Bounds for General Linear Functional Estimation

    Jikai Jin, Vasilis Syrgkanis, Arxiv26

  10. Prescriptive Scaling Reveals the Evolution of Language Model Capabilities

    Hanlin Zhang, Jikai Jin, Vasilis Syrgkanis, Sham Kakade, ICML26 (spotlight)

  11. Learning Treatment Representations for Downstream Instrumental Variable Regression

    Shiangyi Lin, Hui Lan, Vasilis Syrgkanis, ICML26

  12. Policy Learning with Abstention

    Ayush Sawarni, Jikai Jin, Justin Whitehouse, Vasilis Syrgkanis, AISTATS26

  13. Direct Preference Optimization with Unobserved Preference Heterogeneity: The Necessity of Ternary Preferences

    Keertana Chidambaram, Karthik Vinary Seetharaman, Vasilis Syrgkanis, AISTATS26

2025

  1. Inference on Optimal Policy Values and Other Irregular Functionals via Softmax Smoothing

    Justin Whitehouse, Qizhao Chen, Morgane Austern, Vasilis Syrgkanis, Arxiv25

  2. Adversarial Estimation of Riesz Representers

    Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis, JASA 2025

  3. Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learning

    Jikai Jin, Vasilis Syrgkanis, Sham Kakade, Hanlin Zhang, Arxiv25 + COLM2026

  4. It's Hard to Be Normal: The Impact of Noise on Structure-agnostic Estimation

    Jikai Jin, Lester Mackey, Vasilis Syrgkanis, NeurIPS 2025

  5. Estimation of Treatment Effects in Extreme and Unobserved Data

    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, NeurIPS 2025

  6. Preference Learning with Response Time

    Ayush Sawarni, Sahasrajit Sarmasarkar, Vasilis Syrgkanis, NeurIPS 2025

  7. Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation

    Jikai Jin, Vasilis Syrgkanis, COLT 2025

  8. Orthogonal Causal Calibration

    Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu, COLT 2025

  9. A Meta-learner for Heterogeneous Effects in Difference-in-Differences

    Hui Lan, Haoge Chang, Eleanor Dillon, Vasilis Syrgkanis, ICML 2025

  10. Detecting clinician implicit biases in diagnoses using proximal causal inference

    Kara Liu, Russ Altman, Vasilis Syrgkanis, Pacific Symposium on Biocomputing 2025

2024

  1. Predicting Long Term Sequential Policy Value Using Softer Surrogates

    Hyunji Nam, Allen Nie, Ge Gao, Vasilis Syrgkanis, Emma Brunskill, Arxiv24

  2. Conditional Influence Functions

    Victor Chernozhukov, Whitney K. Newey, Vasilis Syrgkanis, Arxiv24, Quantitative Economics 2026

  3. Automatic Doubly Robust Forests

    Zhaomeng Chen, Junting Duan, Victor Chernozhukov, Vasilis Syrgkanis, Arxiv24

  4. Switchback Price Experiments with Forward-Looking Demand

    Yifan Wu, Ramesh Johari, Vasilis Syrgkanis, Gabriel Y. Weintraub, Arxiv24, EC26

  5. Personalized Adaptation via In-Context Preference Learning

    Allison Lau, Younwoo Choi, Vahid Balazadeh, Keertana Chidambaram, Vasilis Syrgkanis, Rahul G. Krishnan, Arxiv24

  6. Dynamic Local Average Treatment Effects

    Ravi Sojitra, Vasilis Syrgkanis, Arxiv24

  7. Simultaneous Inference for Local Structural Parameters with Random Forests

    David Ritzwoller, Vasilis Syrgkanis, Arxiv24

  8. Regularized DeepIV with Model Selection

    Zihao Li, Hui Lan, Vasilis Syrgkanis, Mengdi Wang, Masatoshi Uehara, Arxiv24

  9. Taking a Moment for Distributional Robustness

    Jabari Hastings, Christopher Jung, Charlotte Peale, Vasilis Syrgkanis, Arxiv24

  10. Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity

    Jikai Jin, Vasilis Syrgkanis, NeurIPS24 (Spotlight)

  11. Consistency of Neural Causal Partial Identification

    Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis, NeurIPS24

  12. Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity

    Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis, NeurIPS24

  13. Causal Q-Aggregation for CATE Model Selection

    Hui Lan, Vasilis Syrgkanis, AISTATS24

  14. Adaptive Instrument Design for Indirect Experiments

    Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill, ICLR24

  15. Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation

    Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis, ICLR24

2023

  1. Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration

    Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu, Arxiv23, TMLR 2026

  2. Automatic Debiased Machine Learning for Covariate Shifts

    Victor Chernozhukov, Michael Newey, Whitney K Newey, Rahul Singh, Vasilis Syrgkanis, Arxiv23, Biometrika 2026

  3. Post Reinforcement Learning Inference

    Ruohan Zhan, Vasilis Syrgkanis, Arxiv23, Operations Research 2025

  4. Source Condition Double Robust Inference on Functionals of Inverse Problems

    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, Arxiv23

  5. Inference on Strongly Identified Functionals of Weakly Identified Functions

    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, COLT23, JRSS-B 2025

  6. Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness

    Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara, COLT23

2022

  1. Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects

    Anish Agarwal, Sukjin Han, Dwaipayan Saha, Vasilis Syrgkanis, Haeyeon Yoon, Arxiv22

  2. Finding Subgroups with Significant Treatment Effects

    Jann Spiess, Vasilis Syrgkanis, Victor Yaneng Wang, CLear22

  3. Non-Parametric Inference Adaptive to Intrinsic Dimension

    Khashayar Khosravi, Gregory Lewis, Vasilis Syrgkanis, CLear22

  4. Towards efficient representation identification in supervised learning

    Kartik Ahuja, Divyat Mahajan, Ioannis Mitliagkas, Vasilis Syrgkanis, CLear22

  5. Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models

    Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis, The Econometrics Journal 2022

  6. Automatic Debiased Machine Learning for Dynamic Treatment Effects

    Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis, Arxiv22

  7. Robust Generalized Method of Moments: A Finite Sample Viewpoint

    Dhruv Rohatgi, Vasilis Syrgkanis, NeurIPS22

  8. Debiased Machine Learning without Sample-Splitting for Stable Estimators

    Qizhao Chen, Vasilis Syrgkanis, Morgane Austern, NeurIPS22

  9. Partial Identification of Treatment Effects with Implicit Generative Models

    Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G Krishnan, NeurIPS22 (Spotlight)

  10. RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

    Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis, ICML22 (Long Oral)

  11. Evidence-based Policy Learning

    Jann Spiess, Vasilis Syrgkanis, CLear2022

2021

  1. Long Story Short: Omitted Variable Bias in Causal Machine Learning

    Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis, Arxiv21, ReSTAT 2026

  2. Automatic Debiased Machine Learning via Riesz Regression

    Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis, Arxiv21

  3. Estimating the Long-Term Effects of Novel Treatments

    Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis, NeurIPS21

  4. Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation

    Greg Lewis, Vasilis Syrgkanis, NeurIPS21

  5. Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection

    Morgane Austern, Vasilis Syrgkanis, NeurIPS2021

  6. DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

    Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman, ICML21 Workshop on the Neglected Assumptions in Causal Inference

  7. Dynamically Aggregating Diverse Information

    Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis, EC2021 and Econometrica 2021

  8. Incentivizing Compliance with Algorithmic Instruments

    Daniel Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu, ICML21

  9. Knowledge Distillation as Semi-Parametric Inference

    Tri Dao, Govinda Kamath, Vasilis Syrgkanis, Lester Mackey, ICLR21

  10. Bid Prediction in Repeated Auctions with Learning

    Gali Noti, Vasilis Syrgkanis, WWW2021

  11. Genome-scale screens identify factors regulating tumor cell responses to natural killer cells

    Sheffer et al., Nature Genetics 2021

2020

  1. Estimation and Inference with Trees and Forests in High Dimensions

    Vasilis Syrgkanis, Manolis Zampetakis, COLT20

  2. Minimax Estimation of Conditional Moment Models

    Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis, NeurIPS20

  3. Simple, Credible, and Approximately-Optimal Auctions

    Constantinos Daskalakis, Maxwell Fishelson, Brendan Lucier, Vasilis Syrgkanis, Santhoshini Velusamy, EC2020

2019

  1. Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments

    Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis, NeurIPS19 (spotlight)

  2. Semi-Parametric Efficient Policy Learning with Continuous Actions

    Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov, NeurIPS19

  3. Low-rank Bandit Methods for High-dimensional Dynamic Pricing

    Jonas Mueller, Vasilis Syrgkanis, Matt Taddy, NeurIPS19

  4. Orthogonal Statistical Learning

    Dylan Foster, Vasilis Syrgkanis, COLT19 (Best Paper Award) and Annals of Statistics 2022

  5. Orthogonal Random Forest for Causal Inference

    Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu, ICML19

2018

  1. Semiparametric Contextual Bandits

    Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis, ICML18

  2. Accurate Inference for Adaptive Linear Models

    Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy, ICML18

  3. Optimal Data Acquisition for Statistical Estimation

    Yiling Chen, Nicole Immorlica, Brendan Lucier, Vasilis Syrgkanis, Juba Ziani, EC18

  4. Orthogonal Machine Learning: Power and Limitations

    Lester Mackey, Vasilis Syrgkanis, Ilias Zadik, ICML18

  5. A Multifactorial Model of T Cell Expansion and Durable Clinical Benefit in Response to a PD-L1 Inhibitor

    Mark DM Leiserson, Vasilis Syrgkanis, Amy Gilson, Miroslav Dudik, Samuel Funt, Alexandra Snyder, Lester Mackey, PLOS ONE 2018

  6. Combinatorial Assortment Optimization

    Nicole Immorlica, Brendan Lucier, Jieming Mao, Vasilis Syrgkanis, Christos Tzamos, WINE 2018

  7. Learning to Bid Without Knowing your Value

    Zhe Feng, Chara Podimata, Vasilis Syrgkanis, EC 2018

  8. Optimal and Myopic Information Acquisition

    Annie Liang, Xiaoseng Mu, Vasilis Syrgkanis, EC 2018

  9. Training GANs with Optimism

    Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng, ICLR 2018

  10. Simple vs Optimal Contests with Convex Costs

    Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis, WWW 2018

  11. Truthful Multi-parameter Auctions with Online Supply: an Impossible Combination

    Nikhil Devanur, Balasubramanian Sivan, Vasilis Syrgkanis, SODA 2018

2017

  1. Robust Optimization for Non-Convex Objectives

    Robert Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis, NeurIPS 2017 Oral Presentation (top 1%)

  2. A Sample Complexity Measure with Applications to Learning Optimal Auctions

    Vasilis Syrgkanis, NeurIPS 2017

  3. Efficiency Guarantees from Data

    Darrell Hoy, Denis Nekipelov, Vasilis Syrgkanis, NeurIPS 2017

  4. Oracle Efficient Learning and Auction Design

    Miroslav Dudik, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan, FOCS 2017

  5. Inference on Auctions with Weak Assumptions on Information

    Vasilis Syrgkanis, Elie Tamer, Juba Ziani

  6. Price of Anarchy in Auctions

    Tim Roughgarden, Vasilis Syrgkanis, Eva Tardos, Journal of Artificial Intelligence Research 2017

  7. A Proof of Orthogonal Double Machine Learning with Z-Estimators

    Vasilis Syrgkanis

2016

  1. Improved Regret Bounds for Adversarial Contextual Bandits

    Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire, NeurIPS 2016

  2. Learning in Auctions: Regret is Hard, Envy is Easy

    Constantinos Daskalakis, Vasilis Syrgkanis, FOCS 2016

  3. Bayesian Exploration: Incentivizing Exploration in Bayesian Games

    Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu, EC 2016 Full version: Operations Research 2021

  4. Bounded Rationality in Wagering Mechanisms

    David M. Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan, UAI 2016

  5. Efficient Algorithms for Adversarial Contextual Learning

    Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire, ICML 2016

  6. The Price of Anarchy in Large Games

    Michal Feldman, Nicole Immorlica, Brendan Lucier, Tim Roughgarden, Vasilis Syrgkanis, STOC 2016

  7. Learning and Efficiency in Games with Dynamic Population

    Thodoris Lykouris, Vasilis Syrgkanis, Eva Tardos, SODA 2016

2015

  1. Fast Convergence of Regularized Learning in Games

    Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire, NeurIPS 2015 Best Paper Award

  2. No-Regret Learning in Repeated Bayesian Games

    Jason Hartline, Vasilis Syrgkanis, Eva Tardos, NeurIPS 2015

  3. Econometrics for Learning Agents

    Denis Nekipelov, Vasilis Syrgkanis, Eva Tardos, EC 2015 Best paper award

  4. Bayesian Incentive-Compatible Bandit Exploration

    Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, EC 2015 Full version: Operations Research 2020

  5. Greedy Algorithms make Efficient Mechanisms

    Brendan Lucier, Vasilis Syrgkanis, EC 2015

  6. Simple Auctions with Simple Strategies

    Nikhil Devanur, Jamie Morgenstern, Vasilis Syrgkanis, S. Matthew Weinberg, EC 2015

  7. Information Asymmetries in Common-Value Auctions with Discrete Signals

    Vasilis Syrgkanis, David Kempe, Eva Tardos, EC 2015 Full version: Mathematics of Operations Research 2019

  8. Social Status and Badge Design

    Nicole Immorlica, Greg Stoddard, Vasilis Syrgkanis, WWW 2015

  9. A Unifying Hierarchy of Valuations with Complements and Substitutes

    Uriel Feige, Michal Feldman, Nicole Immorlica, Rani Izsak, Brendan Lucier, Vasilis Syrgkanis, AAAI 2015

  10. Algorithmic Game Theory and Econometrics

    Vasilis Syrgkanis, SIGecom Exchanges, June 2015

  11. Pricing Queries Approximately Optimally

    Vasilis Syrgkanis, Johannes Gehrke

  12. Price of Stability in Games of Incomplete Information

    Vasilis Syrgkanis

2014

  1. Strong Price of Anarchy, Utility Games and Coalitional Dynamics

    Yoram Bachrach, Vasilis Syrgkanis, Eva Tardos, Milan Vojnovic, SAGT 2014

  2. Efficiency of Mechanisms in Complex Markets

    PhD Thesis, Cornell University, Computer Science Department, August 2014

2013

  1. Composable and Efficient Mechanisms

    Vasilis Syrgkanis, Eva Tardos, STOC 2013

  2. Cost-Recovering Bayesian Algorithmic Mechanism Design

    Hu Fu, Brendan Lucier, Balasubramanian Sivan, Vasilis Syrgkanis, EC 2013

  3. Vickrey Auctions for Irregular Distributions

    Balasubramanian Sivan, Vasilis Syrgkanis, WINE 2013

  4. Incentives and Efficiency in Uncertain Collaborative Environments

    Yoram Bachrach, Vasilis Syrgkanis, Milan Vojnovic, WINE 2013

  5. Limits of Efficiency in Sequential Auctions

    Michal Feldman, Brendan Lucier, Vasilis Syrgkanis, WINE 2013

  6. Equilibrium in Combinatorial Public Projects

    Brendan Lucier, Yaron Singer, Vasilis Syrgkanis, Eva Tardos, WINE 2013

2012

  1. Bayesian Games and the Smoothness Framework

    Vasilis Syrgkanis, March 2012

  2. Bayesian Sequential Auctions

    Vasilis Syrgkanis, Eva Tardos, EC 2012

  3. Sequential Auctions and Externalities

    Renato Paes Leme, Vasilis Syrgkanis, Eva Tardos, SODA 2012

  4. The Curse of Simultaneity

    Renato Paes Leme, Vasilis Syrgkanis, Eva Tardos, ITCS 2012

  5. Lower Bounds on Revenue of Approximately Optimal Auctions

    Balasubramanian Sivan, Vasilis Syrgkanis, Omer Tamuz, WINE 2012

  6. The Dining Bidder Problem: a la russe et a la francaise

    Renato Paes Leme, Vasilis Syrgkanis, Eva Tardos, SIGecom Exchanges, December 2012 A review of recent results in simultaneous and sequential item auctions

2010

  1. The Complexity of Equilibria in Cost Sharing Games

    Vasilis Syrgkanis, WINE 2010 [Slides]

2009

  1. Colored Resource Allocation Games

    E. Bampas, A. Pagourtzis, G. Pierrakos, V. Syrgkanis, CTW 2009

  2. Equilibria in Congestion Game Models: Existence, Complexity and Efficiency

    Vasilis Syrgkanis Undergraduate Diploma Thesis, National Technical University of Athens, July 2009 (title is in Greek but main content, p. 6 and on, is in English)