Vasilis Syrgkanis

About

Vasilis Syrgkanis is an Assistant Professor of Management Science and Engineering, with a courtesy appointment in Computer Science at Stanford University. He is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His research focuses on artificial intelligence, management science, and their applications in medicine and imaging. Syrgkanis contributes to advancing AI methodologies and their integration into healthcare, leveraging his expertise to develop innovative solutions in these fields.

Research topics

• Computer Science
• Computer Security
• Machine Learning
• Statistics
• Mathematics
• Artificial Intelligence
• Mathematical optimization
• Immunology
• Operations research
• Applied mathematics

Selected publications

• Orthogonal statistical learning

  The Annals of Statistics · 2023-06-01 · 55 citations

  preprintOpen accessSenior author

  We provide nonasymptotic excess risk guarantees for statistical learning in a setting where the population risk with respect to which we evaluate the target parameter depends on an unknown nuisance parameter that must be estimated from data. We analyze a two-stage sample splitting meta-algorithm that takes as input arbitrary estimation algorithms for the target parameter and nuisance parameter. We show that if the population risk satisfies a condition called Neyman orthogonality, the impact of t…

  PublisherOA PDFDOI

• DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

  arXiv (Cornell University) · 2021-08-27 · 19 citations

  preprintOpen access

  Estimation of causal effects involves crucial assumptions about the data-generating process, such as directionality of effect, presence of instrumental variables or mediators, and whether all relevant confounders are observed. Violation of any of these assumptions leads to significant error in the effect estimate. However, unlike cross-validation for predictive models, there is no global validator method for a causal estimate. As a result, expressing different causal assumptions formally and val…

  PublisherOA PDFDOI

• Multi-Item Nontruthful Auctions Achieve Good Revenue

  SIAM Journal on Computing · 2022-12-15 · 4 citations

  article

  We present a general framework for designing approximately revenue-optimal mechanisms for multi-item additive auctions, which applies to both truthful and nontruthful auctions. Given a (not necessarily truthful) single-item auction format satisfying certain technical conditions, we run simultaneous item auctions augmented with a personalized entry fee for each bidder that must be paid before the auction can be accessed. These entry fees depend only on the prior distribution of bidder types and i…

  PublisherDOI

• Adversarial Estimation of Riesz Representers

  Journal of the American Statistical Association · 2025-12-02 · 3 citations

  preprintOpen accessSenior author

  Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as lea…

  PublisherOA PDFDOI

• Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity

  2024-01-01 · 2 citations

  articleSenior author
  PublisherDOI

Frequent coauthors

• Éva Tardos

  38 shared

• Brendan Lucier

  Microsoft Research (United Kingdom)

  38 shared

• Robert E. Schapire

  28 shared

• Nicole Immorlica

  23 shared

• Jennifer Wortman Vaughan

  17 shared

• Aleksandrs Slivkins

  16 shared

• Denis Nekipelov

  16 shared

• Haipeng Luo

  Zhejiang University

  14 shared

Similar researchers at Stanford University

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• Ronald Davish-183
• Robert Harringtonh-169