Vasilis Syrgkanis

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 126

Top Venues

CoRR

NeurIPS

ICML

Publications

Jabari Hastings, Christopher Jung, Charlotte Peale, Vasilis Syrgkanis
Taking a Moment for Distributional Robustness. CoRR (2024)
Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu
Orthogonal Causal Calibration. CoRR (2024)
Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill
Adaptive Instrument Design for Indirect Experiments. ICLR (2024)
Vahid Balazadeh Meresht, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity. CoRR (2024)
Victor Chernozhukov, Christian Hansen, Nathan Kallus, Martin Spindler, Vasilis Syrgkanis
Applied Causal Inference Powered by ML and AI. CoRR (2024)
Keertana Chidambaram, Karthik Vinay Seetharaman, Vasilis Syrgkanis
Direct Preference Optimization With Unobserved Preference Heterogeneity. CoRR (2024)
Jikai Jin, Vasilis Syrgkanis
Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation. CoRR (2024)
Jiyuan Tan, Jose H. Blanchet, Vasilis Syrgkanis
Consistency of Neural Causal Partial Identification. CoRR (2024)
Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation. ICLR (2024)
Hui Lan, Vasilis Syrgkanis
Causal Q-Aggregation for CATE Model Selection. AISTATS (2024)
Zihao Li, Hui Lan, Vasilis Syrgkanis, Mengdi Wang, Masatoshi Uehara
Regularized DeepIV with Model Selection. CoRR (2024)
Ravi B. Sojitra, Vasilis Syrgkanis
Dynamic Local Average Treatment Effects. CoRR (2024)
Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill
Adaptive Instrument Design for Indirect Experiments. CoRR (2023)
Jikai Jin, Vasilis Syrgkanis
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity. CoRR (2023)
Vasilis Syrgkanis, Ruohan Zhan
Post-Episodic Reinforcement Learning Inference. CoRR (2023)
Dung Daniel T. Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Zhiwei Steven Wu
Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration. CoRR (2023)
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
Convergence Guarantees without Identification or Closedness. COLT (2023)
Qizhao Chen, Morgane Austern, Vasilis Syrgkanis
Inference on Optimal Dynamic Policies via Softmax Approximation. CoRR (2023)
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
Inference on Strongly Identified Functionals of Weakly Identified Functions. COLT (2023)
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
Source Condition Double Robust Inference on Functionals of Inverse Problems. CoRR (2023)
Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara
Minimax Instrumental Variable Regression and $L_2$ Convergence Guarantees without Identification or Closedness. CoRR (2023)
Hui Lan, Vasilis Syrgkanis
Causal Q-Aggregation for CATE Model Selection. CoRR (2023)
Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis
Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation. CoRR (2022)
Anish Agarwal, Vasilis Syrgkanis
Synthetic Blip Effects: Generalizing Synthetic Controls for the Dynamic Treatment Regime. CoRR (2022)
Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G. Krishnan
Partial Identification of Treatment Effects with Implicit Generative Models. NeurIPS (2022)
Victor Chernozhukov, Whitney Newey, Victor Quintas-Martinez, Vasilis Syrgkanis
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests. ICML (2022)
Constantinos Daskalakis, Maxwell Fishelson, Brendan Lucier, Vasilis Syrgkanis, Santhoshini Velusamy
Multi-Item Nontruthful Auctions Achieve Good Revenue. SIAM J. Comput. 51 (6) (2022)
Qizhao Chen, Vasilis Syrgkanis, Morgane Austern
Debiased Machine Learning without Sample-Splitting for Stable Estimators. CoRR (2022)
Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas
Towards efficient representation identification in supervised learning. CLeaR (2022)
Qizhao Chen, Vasilis Syrgkanis, Morgane Austern
Debiased Machine Learning without Sample-Splitting for Stable Estimators. NeurIPS (2022)
Khashayar Khosravi, Greg Lewis, Vasilis Syrgkanis
Non-parametric Inference Adaptive to Intrinsic Dimension. CLeaR (2022)
Vahid Balazadeh, Vasilis Syrgkanis, Rahul G. Krishnan
Partial Identification of Treatment Effects with Implicit Generative Models. CoRR (2022)
Rahul Singh, Vasilis Syrgkanis
Automatic Debiased Machine Learning for Dynamic Treatment Effects. CoRR (2022)
Constantinos Daskalakis, Vasilis Syrgkanis
Learning in auctions: Regret is hard, envy is easy. Games Econ. Behav. 134 (2022)
Dhruv Rohatgi, Vasilis Syrgkanis
Robust Generalized Method of Moments: A Finite Sample Viewpoint. NeurIPS (2022)
Yishay Mansour, Alex Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. Oper. Res. 70 (2) (2022)
Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas
Towards efficient representation identification in supervised learning. CoRR (2022)
Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis
Estimating the Long-Term Effects of Novel Treatments. NeurIPS (2021)
Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis
Omitted Variable Bias in Machine Learned Causal Models. CoRR (2021)
Morgane Austern, Vasilis Syrgkanis
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection. NeurIPS (2021)
Gali Noti, Vasilis Syrgkanis
Bid Prediction in Repeated Auctions with Learning. WWW (2021)
Vasilis Syrgkanis, Greg Lewis, Miruna Oprescu, Maggie Hei, Keith Battocchi, Eleanor Dillon, Jing Pan, Yifeng Wu, Paul Lo, Huigang Chen, Totte Harinen, Jeong-Yoon Lee
Causal Inference and Machine Learning in Practice with EconML and CausalML: Industrial Use Cases at Microsoft, TripAdvisor, Uber. KDD (2021)
Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, Lester Mackey
Knowledge Distillation as Semiparametric Inference. ICLR (2021)
Greg Lewis, Vasilis Syrgkanis
Double/Debiased Machine Learning for Dynamic Treatment Effects. NeurIPS (2021)
Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu
Incentivizing Compliance with Algorithmic Instruments. CoRR (2021)
Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
Dynamically Aggregating Diverse Information. EC (2021)
Nicole Immorlica, Brendan Lucier, Jieming Mao, Vasilis Syrgkanis, Christos Tzamos
Combinatorial Assortment Optimization. ACM Trans. Economics and Comput. 9 (1) (2021)
Jann Spiess, Vasilis Syrgkanis
Evidence-Based Policy Learning. CoRR (2021)
Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests. CoRR (2021)
Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis
Adversarial Estimation of Riesz Representers. CoRR (2021)
Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu
Incentivizing Compliance with Algorithmic Instruments. ICML (2021)
Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions. CoRR (2021)
Dhruv Rohatgi, Vasilis Syrgkanis
Robust Generalized Method of Moments: A Finite Sample Viewpoint. CoRR (2021)
Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, Lester Mackey
Knowledge Distillation as Semiparametric Inference. CoRR (2021)
Miroslav Dudíte, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Oracle-efficient Online Learning and Auction Design. J. ACM 67 (5) (2020)
Morgane Austern, Vasilis Syrgkanis
Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality. CoRR (2020)
Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis
Minimax Estimation of Conditional Moment Models. CoRR (2020)
Constantinos Daskalakis, Maxwell Fishelson, Brendan Lucier, Vasilis Syrgkanis, Santhoshini Velusamy
Simple, Credible, and Approximately-Optimal Auctions. EC (2020)
Constantinos Daskalakis, Maxwell Fishelson, Brendan Lucier, Vasilis Syrgkanis, Santhoshini Velusamy
Simple, Credible, and Approximately-Optimal Auctions. CoRR (2020)
Vasilis Syrgkanis, Manolis Zampetakis
Estimation and Inference with Trees and Forests in High Dimensions. CoRR (2020)
Dylan J. Foster, Vasilis Syrgkanis
Statistical Learning with a Nuisance Component (Extended Abstract). IJCAI (2020)
Gali Noti, Vasilis Syrgkanis
Bid Prediction in Repeated Auctions with Learning. CoRR (2020)
Vasilis Syrgkanis, Manolis Zampetakis
Estimation and Inference with Trees and Forests in High Dimensions. COLT (2020)
Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis
Minimax Estimation of Conditional Moment Models. NeurIPS (2020)
Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis
Bayesian Incentive-Compatible Bandit Exploration. Oper. Res. 68 (4) (2020)
Greg Lewis, Vasilis Syrgkanis
Double/Debiased Machine Learning for Dynamic Treatment Effects. CoRR (2020)
Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. CoRR (2019)
Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
Dynamically Aggregating Diverse Information. CoRR (2019)
Vasilis Syrgkanis, David Kempe, Éva Tardos
Information Asymmetries in Common-Value Auctions with Discrete Signals. Math. Oper. Res. 44 (4) (2019)
Jonas Mueller, Vasilis Syrgkanis, Matt Taddy
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing. NeurIPS (2019)
Khashayar Khosravi, Greg Lewis, Vasilis Syrgkanis
Non-Parametric Inference Adaptive to Intrinsic Dimension. CoRR (2019)
Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. NeurIPS (2019)
Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis
Semi-Parametric Efficient Policy Learning with Continuous Actions. NeurIPS (2019)
Dylan J. Foster, Vasilis Syrgkanis
Statistical Learning with a Nuisance Component. COLT (2019)
Dylan J. Foster, Vasilis Syrgkanis
Orthogonal Statistical Learning. CoRR (2019)
Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov
Semi-Parametric Efficient Policy Learning with Continuous Actions. CoRR (2019)
Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu
Orthogonal Random Forest for Causal Inference. ICML (2019)
Nikhil R. Devanur, Balasubramanian Sivan, Vasilis Syrgkanis
Truthful Multi-Parameter Auctions with Online Supply: an Impossible Combination. SODA (2018)
Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu
Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation. CoRR (2018)
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng
Training GANs with Optimism. ICLR (Poster) (2018)
Zhe Feng, Chara Podimata, Vasilis Syrgkanis
Learning to Bid Without Knowing your Value. EC (2018)
Greg Lewis, Vasilis Syrgkanis
Adversarial Generalized Method of Moments. CoRR (2018)
Yash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy
Accurate Inference for Adaptive Linear Models. ICML (2018)
Jimmy Wu, Diondra Peck, Scott Hsieh, Vandana Dialani, Constance D. Lehman, Bolei Zhou, Vasilis Syrgkanis, Lester W. Mackey, Genevieve Patterson
Expert identification of visual primitives used by CNNs during mammogram classification. Medical Imaging: Computer-Aided Diagnosis (2018)
Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis
On Revenue-Maximizing Mechanisms Assuming Convex Costs. SAGT (2018)
Nicole Immorlica, Brendan Lucier, Jieming Mao, Vasilis Syrgkanis, Christos Tzamos
Combinatorial Assortment Optimization. WINE (2018)
Victor Chernozhukov, Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis
Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models. CoRR (2018)
Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis
Simple vs Optimal Contests with Convex Costs. WWW (2018)
Ilias Zadik, Lester W. Mackey, Vasilis Syrgkanis
Orthogonal Machine Learning: Power and Limitations. ICML (2018)
Yiling Chen, Nicole Immorlica, Brendan Lucier, Vasilis Syrgkanis, Juba Ziani
Optimal Data Acquisition for Statistical Estimation. EC (2018)
Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis
Optimal and Myopic Information Acquisition. EC (2018)
Jimmy Wu, Diondra Peck, Scott Hsieh, Vandana Dialani, Constance D. Lehman, Bolei Zhou, Vasilis Syrgkanis, Lester W. Mackey, Genevieve Patterson
Expert identification of visual primitives used by CNNs during mammogram classification. CoRR (2018)
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
Semiparametric Contextual Bandits. ICML (2018)
Jonas Mueller, Vasilis Syrgkanis, Matt Taddy
Low-rank Bandit Methods for High-dimensional Dynamic Pricing. CoRR (2018)
Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis
Semiparametric Contextual Bandits. CoRR (2018)
Tim Roughgarden, Vasilis Syrgkanis, Éva Tardos
The Price of Anarchy in Auctions. J. Artif. Intell. Res. 59 (2017)
Vasilis Syrgkanis
A Sample Complexity Measure with Applications to Learning Optimal Auctions. CoRR (2017)
Yash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy
Accurate Inference for Adaptive Linear Models. CoRR (2017)
Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis
Simple vs Optimal Mechanisms in Auctions with Convex Payments. CoRR (2017)
Nicole Immorlica, Brendan Lucier, Jieming Mao, Vasilis Syrgkanis, Christos Tzamos
Combinatorial Assortment Optimization. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Vasilis Syrgkanis