Syrgkanis, Vasilis

50 publications

TMLR 2026 Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration Dung Daniel Ngo, Keegan Harris, Anish Agarwal, Vasilis Syrgkanis, Steven Wu

ICML 2025 A Meta-Learner for Heterogeneous Effects in Difference-in-Differences Hui Lan, Haoge Chang, Eleanor Wiske Dillon, Vasilis Syrgkanis

NeurIPS 2025 Estimation of Treatment Effects in Extreme and Unobserved Data Jiyuan Tan, Vasilis Syrgkanis, Jose Blanchet

NeurIPS 2025 It’s Hard to Be Normal: The Impact of Noise on Structure-Agnostic Estimation Jikai Jin, Lester Mackey, Vasilis Syrgkanis

COLT 2025 Orthogonal Causal Calibration (Extended Abstract) Justin Whitehouse, Christopher Jung, Vasilis Syrgkanis, Bryan Wilder, Zhiwei Steven Wu

NeurIPS 2025 Preference Learning with Response Time: Robust Losses and Guarantees Ayush Sawarni, Sahasrajit Sarmasarkar, Vasilis Syrgkanis

COLT 2025 Structure-Agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation (Extended Abstract) Jikai Jin, Vasilis Syrgkanis

ICLR 2024 Adaptive Instrument Design for Indirect Experiments Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill

AISTATS 2024 Causal Q-Aggregation for CATE Model Selection Hui Lan, Vasilis Syrgkanis

NeurIPS 2024 Consistency of Neural Causal Partial Identification Jiyuan Tan, Jose Blanchet, Vasilis Syrgkanis

ICLR 2024 Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis

NeurIPS 2024 Learning Linear Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity Jikai Jin, Vasilis Syrgkanis

NeurIPSW 2024 Personalized Adaptation via In-Context Preference Learning Allison Lau, Younwoo Choi, Vahid Balazadeh, Keertana Chidambaram, Vasilis Syrgkanis, Rahul Krishnan

NeurIPS 2024 Sequential Decision Making with Expert Demonstrations Under Unobserved Heterogeneity Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G. Krishnan, Vasilis Syrgkanis

ICMLW 2024 Sequential Decision Making with Expert Demonstrations Under Unobserved Heterogeneity Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul Krishnan, Vasilis Syrgkanis

COLT 2023 Inference on Strongly Identified Functionals of Weakly Identified Functions Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

COLT 2023 Minimax Instrumental Variable Regression and $l_2$ Convergence Guarantees Without Identification or Closedness Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara

NeurIPS 2022 Debiased Machine Learning Without Sample-Splitting for Stable Estimators Qizhao Chen, Vasilis Syrgkanis, Morgane Austern

CLeaR 2022 Non-Parametric Inference Adaptive to Intrinsic Dimension Khashayar Khosravi, Greg Lewis, Vasilis Syrgkanis

NeurIPS 2022 Partial Identification of Treatment Effects with Implicit Generative Models Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G Krishnan

ICML 2022 RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests Victor Chernozhukov, Whitney Newey, Vı́ctor M Quintas-Martı́nez, Vasilis Syrgkanis

NeurIPS 2022 Robust Generalized Method of Moments: A Finite Sample Viewpoint Dhruv Rohatgi, Vasilis Syrgkanis

CLeaR 2022 Towards Efficient Representation Identification in Supervised Learning Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas

NeurIPS 2021 Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection Morgane Austern, Vasilis Syrgkanis

NeurIPS 2021 Double/Debiased Machine Learning for Dynamic Treatment Effects Greg Lewis, Vasilis Syrgkanis

NeurIPS 2021 Estimating the Long-Term Effects of Novel Treatments Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis

ICML 2021 Incentivizing Compliance with Algorithmic Instruments Dung Daniel T Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu

ICLR 2021 Knowledge Distillation as Semiparametric Inference Tri Dao, Govinda M Kamath, Vasilis Syrgkanis, Lester Mackey

COLT 2020 Estimation and Inference with Trees and Forests in High Dimensions Vasilis Syrgkanis, Manolis Zampetakis

NeurIPS 2020 Minimax Estimation of Conditional Moment Models Nishanth Dikkala, Greg Lewis, Lester W. Mackey, Vasilis Syrgkanis

IJCAI 2020 Statistical Learning with a Nuisance Component (Extended Abstract) Dylan J. Foster, Vasilis Syrgkanis

NeurIPS 2019 Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing Jonas W Mueller, Vasilis Syrgkanis, Matt Taddy

NeurIPS 2019 Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis

ICML 2019 Orthogonal Random Forest for Causal Inference Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu

NeurIPS 2019 Semi-Parametric Efficient Policy Learning with Continuous Actions Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis

COLT 2019 Statistical Learning with a Nuisance Component Dylan J. Foster, Vasilis Syrgkanis

ICML 2018 Accurate Inference for Adaptive Linear Models Yash Deshpande, Lester Mackey, Vasilis Syrgkanis, Matt Taddy

ICML 2018 Orthogonal Machine Learning: Power and Limitations Lester Mackey, Vasilis Syrgkanis, Ilias Zadik

ICML 2018 Semiparametric Contextual Bandits Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis

ICLR 2018 Training GANs with Optimism Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng

NeurIPS 2017 A Sample Complexity Measure with Applications to Learning Optimal Auctions Vasilis Syrgkanis

NeurIPS 2017 Robust Optimization for Non-Convex Objectives Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis

JAIR 2017 The Price of Anarchy in Auctions Tim Roughgarden, Vasilis Syrgkanis, Éva Tardos

NeurIPS 2017 Welfare Guarantees from Data Darrell Hoy, Denis Nekipelov, Vasilis Syrgkanis

UAI 2016 Bounded Rationality in Wagering Mechanisms David M. Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan

ICML 2016 Efficient Algorithms for Adversarial Contextual Learning Vasilis Syrgkanis, Akshay Krishnamurthy, Robert Schapire

NeurIPS 2016 Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire

AAAI 2015 A Unifying Hierarchy of Valuations with Complements and Substitutes Uriel Feige, Michal Feldman, Nicole Immorlica, Rani Izsak, Brendan Lucier, Vasilis Syrgkanis

NeurIPS 2015 Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire

NeurIPS 2015 No-Regret Learning in Bayesian Games Jason Hartline, Vasilis Syrgkanis, Eva Tardos