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ORCIDVasilis Syrgkanis
Vasileios Syrgkanis
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- affiliation: Cornell University, Ithaca, USA
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2020 – today
- 2023
[c72]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Inference on Strongly Identified Functionals of Weakly Identified Functions. COLT 2023: 2265- [c71]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Minimax Instrumental Variable Regression and L2 Convergence Guarantees without Identification or Closedness. COLT 2023: 2291-2318 - [i79]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 abs/2302.05404 (2023) - [i78]Vasilis Syrgkanis, Ruohan Zhan:
Post-Episodic Reinforcement Learning Inference. CoRR abs/2302.08854 (2023) - [i77]Qizhao Chen, Morgane Austern, Vasilis Syrgkanis:
Inference on Optimal Dynamic Policies via Softmax Approximation. CoRR abs/2303.04416 (2023) - [i76]Andrew Bennett, Nathan Kallus, Xiaojie Mao, Whitney Newey, Vasilis Syrgkanis, Masatoshi Uehara:
Source Condition Double Robust Inference on Functionals of Inverse Problems. CoRR abs/2307.13793 (2023) - 2022
- [j10]Constantinos Daskalakis, Vasilis Syrgkanis:
Learning in auctions: Regret is hard, envy is easy. Games Econ. Behav. 134: 308-343 (2022) - [j9]Yishay Mansour, Alex Slivkins
, Vasilis Syrgkanis, Zhiwei Steven Wu:
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. Oper. Res. 70(2): 1105-1127 (2022) - [j8]Constantinos Daskalakis, Maxwell Fishelson, Brendan Lucier, Vasilis Syrgkanis, Santhoshini Velusamy:
Multi-Item Nontruthful Auctions Achieve Good Revenue. SIAM J. Comput. 51(6): 1796-1838 (2022) - [c70]Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas:
Towards efficient representation identification in supervised learning. CLeaR 2022: 19-43 - [c69]Khashayar Khosravi, Greg Lewis, Vasilis Syrgkanis:
Non-parametric Inference Adaptive to Intrinsic Dimension. CLeaR 2022: 373-389 - [c68]Victor Chernozhukov, Whitney Newey, Victor Quintas-Martinez, Vasilis Syrgkanis:
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests. ICML 2022: 3901-3914 - [c67]Qizhao Chen, Vasilis Syrgkanis, Morgane Austern:
Debiased Machine Learning without Sample-Splitting for Stable Estimators. NeurIPS 2022 - [c66]Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G. Krishnan:
Partial Identification of Treatment Effects with Implicit Generative Models. NeurIPS 2022 - [c65]Dhruv Rohatgi, Vasilis Syrgkanis:
Robust Generalized Method of Moments: A Finite Sample Viewpoint. NeurIPS 2022 - [i75]Rahul Singh, Vasilis Syrgkanis:
Automatic Debiased Machine Learning for Dynamic Treatment Effects. CoRR abs/2203.13887 (2022) - [i74]Kartik Ahuja, Divyat Mahajan, Vasilis Syrgkanis, Ioannis Mitliagkas:
Towards efficient representation identification in supervised learning. CoRR abs/2204.04606 (2022) - [i73]Qizhao Chen, Vasilis Syrgkanis, Morgane Austern:
Debiased Machine Learning without Sample-Splitting for Stable Estimators. CoRR abs/2206.01825 (2022) - [i72]Vahid Balazadeh Meresht, Vasilis Syrgkanis, Rahul G. Krishnan:
Partial Identification of Treatment Effects with Implicit Generative Models. CoRR abs/2210.08139 (2022) - [i71]Anish Agarwal, Vasilis Syrgkanis:
Synthetic Blip Effects: Generalizing Synthetic Controls for the Dynamic Treatment Regime. CoRR abs/2210.11003 (2022) - [i70]Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, Vasilis Syrgkanis:
Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation. CoRR abs/2211.01939 (2022) - 2021
- [j7]Nicole Immorlica, Brendan Lucier, Jieming Mao, Vasilis Syrgkanis, Christos Tzamos:
Combinatorial Assortment Optimization. ACM Trans. Economics and Comput. 9(1): 5:1-5:34 (2021) - [c64]Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, Lester Mackey:
Knowledge Distillation as Semiparametric Inference. ICLR 2021 - [c63]Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Steven Wu:
Incentivizing Compliance with Algorithmic Instruments. ICML 2021: 8045-8055 - [c62]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: 4072-4073 - [c61]Keith Battocchi, Eleanor Dillon, Maggie Hei, Greg Lewis, Miruna Oprescu, Vasilis Syrgkanis:
Estimating the Long-Term Effects of Novel Treatments. NeurIPS 2021: 2925-2935 - [c60]Morgane Austern, Vasilis Syrgkanis:
Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection. NeurIPS 2021: 10705-10717 - [c59]Greg Lewis, Vasilis Syrgkanis:
Double/Debiased Machine Learning for Dynamic Treatment Effects. NeurIPS 2021: 22695-22707 - [c58]Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis:
Dynamically Aggregating Diverse Information. EC 2021: 687-688 - [c57]Gali Noti, Vasilis Syrgkanis:
Bid Prediction in Repeated Auctions with Learning. WWW 2021: 3953-3964 - [i69]Victor Chernozhukov, Whitney Newey, Rahul Singh, Vasilis Syrgkanis:
Adversarial Estimation of Riesz Representers. CoRR abs/2101.00009 (2021) - [i68]Jann Spiess, Vasilis Syrgkanis:
Evidence-Based Policy Learning. CoRR abs/2103.07066 (2021) - [i67]Tri Dao, Govinda M. Kamath, Vasilis Syrgkanis, Lester Mackey:
Knowledge Distillation as Semiparametric Inference. CoRR abs/2104.09732 (2021) - [i66]Dung Daniel T. Ngo, Logan Stapleton, Vasilis Syrgkanis, Zhiwei Steven Wu:
Incentivizing Compliance with Algorithmic Instruments. CoRR abs/2107.10093 (2021) - [i65]Amit Sharma, Vasilis Syrgkanis, Cheng Zhang, Emre Kiciman:
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions. CoRR abs/2108.13518 (2021) - [i64]Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis:
RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests. CoRR abs/2110.03031 (2021) - [i63]Dhruv Rohatgi, Vasilis Syrgkanis:
Robust Generalized Method of Moments: A Finite Sample Viewpoint. CoRR abs/2110.03070 (2021) - [i62]Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, Vasilis Syrgkanis:
Omitted Variable Bias in Machine Learned Causal Models. CoRR abs/2112.13398 (2021) - 2020
- [j6]Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis:
Bayesian Incentive-Compatible Bandit Exploration. Oper. Res. 68(4): 1132-1161 (2020) - [j5]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-efficient Online Learning and Auction Design. J. ACM 67(5): 26:1-26:57 (2020) - [c56]Vasilis Syrgkanis, Manolis Zampetakis:
Estimation and Inference with Trees and Forests in High Dimensions. COLT 2020: 3453-3454 - [c55]Dylan J. Foster, Vasilis Syrgkanis:
Statistical Learning with a Nuisance Component (Extended Abstract). IJCAI 2020: 4726-4729 - [c54]Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis:
Minimax Estimation of Conditional Moment Models. NeurIPS 2020 - [c53]Constantinos Daskalakis, Maxwell Fishelson, Brendan Lucier, Vasilis Syrgkanis, Santhoshini Velusamy:
Simple, Credible, and Approximately-Optimal Auctions. EC 2020: 713 - [i61]Constantinos Daskalakis, Maxwell Fishelson, Brendan Lucier, Vasilis Syrgkanis, Santhoshini Velusamy:
Simple, Credible, and Approximately-Optimal Auctions. CoRR abs/2002.06702 (2020) - [i60]Greg Lewis, Vasilis Syrgkanis:
Double/Debiased Machine Learning for Dynamic Treatment Effects. CoRR abs/2002.07285 (2020) - [i59]Nishanth Dikkala, Greg Lewis, Lester Mackey, Vasilis Syrgkanis:
Minimax Estimation of Conditional Moment Models. CoRR abs/2006.07201 (2020) - [i58]Vasilis Syrgkanis, Manolis Zampetakis:
Estimation and Inference with Trees and Forests in High Dimensions. CoRR abs/2007.03210 (2020) - [i57]Gali Noti, Vasilis Syrgkanis:
Bid Prediction in Repeated Auctions with Learning. CoRR abs/2007.13193 (2020) - [i56]Morgane Austern, Vasilis Syrgkanis:
Asymptotics of the Empirical Bootstrap Method Beyond Asymptotic Normality. CoRR abs/2011.11248 (2020)
2010 – 2019
- 2019
- [j4]Vasilis Syrgkanis, David Kempe, Éva Tardos:
Information Asymmetries in Common-Value Auctions with Discrete Signals. Math. Oper. Res. 44(4): 1450-1476 (2019) - [c52]Dylan J. Foster, Vasilis Syrgkanis:
Statistical Learning with a Nuisance Component. COLT 2019: 1346-1348 - [c51]Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu:
Orthogonal Random Forest for Causal Inference. ICML 2019: 4932-4941 - [c50]Victor Chernozhukov, Mert Demirer, Greg Lewis, Vasilis Syrgkanis:
Semi-Parametric Efficient Policy Learning with Continuous Actions. NeurIPS 2019: 15039-15049 - [c49]Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis:
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. NeurIPS 2019: 15167-15176 - [c48]Jonas Mueller, Vasilis Syrgkanis, Matt Taddy:
Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing. NeurIPS 2019: 15442-15452 - [i55]Khashayar Khosravi, Greg Lewis, Vasilis Syrgkanis:
Non-Parametric Inference Adaptive to Intrinsic Dimension. CoRR abs/1901.03719 (2019) - [i54]Dylan J. Foster, Vasilis Syrgkanis:
Orthogonal Statistical Learning. CoRR abs/1901.09036 (2019) - [i53]Mert Demirer, Vasilis Syrgkanis, Greg Lewis, Victor Chernozhukov:
Semi-Parametric Efficient Policy Learning with Continuous Actions. CoRR abs/1905.10116 (2019) - [i52]Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis:
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. CoRR abs/1905.10176 (2019) - [i51]Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis:
Dynamically Aggregating Diverse Information. CoRR abs/1910.07015 (2019) - 2018
- [c47]Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng:
Training GANs with Optimism. ICLR (Poster) 2018 - [c46]Yash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy:
Accurate Inference for Adaptive Linear Models. ICML 2018: 1202-1211 - [c45]Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis:
Semiparametric Contextual Bandits. ICML 2018: 2781-2790 - [c44]Ilias Zadik, Lester W. Mackey, Vasilis Syrgkanis:
Orthogonal Machine Learning: Power and Limitations. ICML 2018: 5723-5731 - [c43]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: 105752T - [c42]Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis:
On Revenue-Maximizing Mechanisms Assuming Convex Costs. SAGT 2018: 113-124 - [c41]Yiling Chen, Nicole Immorlica, Brendan Lucier, Vasilis Syrgkanis, Juba Ziani:
Optimal Data Acquisition for Statistical Estimation. EC 2018: 27-44 - [c40]Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis:
Optimal and Myopic Information Acquisition. EC 2018: 45-46 - [c39]Zhe Feng, Chara Podimata, Vasilis Syrgkanis:
Learning to Bid Without Knowing your Value. EC 2018: 505-522 - [c38]Nikhil R. Devanur, Balasubramanian Sivan, Vasilis Syrgkanis:
Truthful Multi-Parameter Auctions with Online Supply: an Impossible Combination. SODA 2018: 753-769 - [c37]Nicole Immorlica, Brendan Lucier, Jieming Mao, Vasilis Syrgkanis, Christos Tzamos:
Combinatorial Assortment Optimization. WINE 2018: 218-231 - [c36]Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis:
Simple vs Optimal Contests with Convex Costs. WWW 2018: 1429-1438 - [i50]Jonas Mueller, Vasilis Syrgkanis, Matt Taddy:
Low-rank Bandit Methods for High-dimensional Dynamic Pricing. CoRR abs/1801.10242 (2018) - [i49]Akshay Krishnamurthy, Zhiwei Steven Wu, Vasilis Syrgkanis:
Semiparametric Contextual Bandits. CoRR abs/1803.04204 (2018) - [i48]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 abs/1803.04858 (2018) - [i47]Greg Lewis, Vasilis Syrgkanis:
Adversarial Generalized Method of Moments. CoRR abs/1803.07164 (2018) - [i46]Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu:
Orthogonal Random Forest for Heterogeneous Treatment Effect Estimation. CoRR abs/1806.03467 (2018) - [i45]Victor Chernozhukov, Denis Nekipelov, Vira Semenova, Vasilis Syrgkanis:
Plug-in Regularized Estimation of High-Dimensional Parameters in Nonlinear Semiparametric Models. CoRR abs/1806.04823 (2018) - 2017
- [j3]Tim Roughgarden, Vasilis Syrgkanis, Éva Tardos:
The Price of Anarchy in Auctions. J. Artif. Intell. Res. 59: 59-101 (2017) - [c35]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-Efficient Online Learning and Auction Design. FOCS 2017: 528-539 - [c34]Darrell Hoy, Denis Nekipelov, Vasilis Syrgkanis:
Welfare Guarantees from Data. NIPS 2017: 3768-3777 - [c33]Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis:
Robust Optimization for Non-Convex Objectives. NIPS 2017: 4705-4714 - [c32]Vasilis Syrgkanis:
A Sample Complexity Measure with Applications to Learning Optimal Auctions. NIPS 2017: 5352-5359 - [c31]Vasilis Syrgkanis:
Fast convergence of learning in games (invited talk). STOC 2017: 5 - [i44]Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis:
Simple vs Optimal Mechanisms in Auctions with Convex Payments. CoRR abs/1702.06062 (2017) - [i43]Annie Liang, Xiaosheng Mu, Vasilis Syrgkanis:
Optimal Learning from Multiple Information Sources. CoRR abs/1703.06367 (2017) - [i42]Vasilis Syrgkanis:
A Sample Complexity Measure with Applications to Learning Optimal Auctions. CoRR abs/1704.02598 (2017) - [i41]Vasilis Syrgkanis:
A Proof of Orthogonal Double Machine Learning with Z-Estimators. CoRR abs/1704.03754 (2017) - [i40]Robert S. Chen, Brendan Lucier, Yaron Singer, Vasilis Syrgkanis:
Robust Optimization for Non-Convex Objectives. CoRR abs/1707.01047 (2017) - [i39]Vasilis Syrgkanis, Elie Tamer, Juba Ziani:
Inference on Auctions with Weak Assumptions on Information. CoRR abs/1710.03830 (2017) - [i38]Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis, Haoyang Zeng:
Training GANs with Optimism. CoRR abs/1711.00141 (2017) - [i37]Lester W. Mackey, Vasilis Syrgkanis, Ilias Zadik:
Orthogonal Machine Learning: Power and Limitations. CoRR abs/1711.00342 (2017) - [i36]Yiling Chen, Nicole Immorlica, Brendan Lucier, Vasilis Syrgkanis, Juba Ziani:
Optimal Data Acquisition for Statistical Estimation. CoRR abs/1711.01295 (2017) - [i35]Zhe Feng, Chara Podimata, Vasilis Syrgkanis:
Learning to Bid Without Knowing your Value. CoRR abs/1711.01333 (2017) - [i34]Nicole Immorlica, Brendan Lucier, Jieming Mao, Vasilis Syrgkanis, Christos Tzamos:
Combinatorial Assortment Optimization. CoRR abs/1711.02601 (2017) - [i33]Yash Deshpande, Lester W. Mackey, Vasilis Syrgkanis, Matt Taddy:
Accurate Inference for Adaptive Linear Models. CoRR abs/1712.06695 (2017) - 2016
- [c30]Constantinos Daskalakis, Vasilis Syrgkanis:
Learning in Auctions: Regret is Hard, Envy is Easy. FOCS 2016: 219-228 - [c29]Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire:
Efficient Algorithms for Adversarial Contextual Learning. ICML 2016: 2159-2168 - [c28]Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire:
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits. NIPS 2016: 3135-3143 - [c27]Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu:
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. EC 2016: 661 - [c26]Thodoris Lykouris, Vasilis Syrgkanis, Éva Tardos:
Learning and Efficiency in Games with Dynamic Population. SODA 2016: 120-129 - [c25]Michal Feldman, Nicole Immorlica, Brendan Lucier, Tim Roughgarden, Vasilis Syrgkanis:
The price of anarchy in large games. STOC 2016: 963-976 - [c24]David M. Pennock, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Bounded Rationality in Wagering Mechanisms. UAI 2016 - [i32]Amy Greenwald, Takehiro Oyakawa, Vasilis Syrgkanis:
Optimal Auctions with Convex Perceived Payments. CoRR abs/1601.07163 (2016) - [i31]Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire:
Efficient Algorithms for Adversarial Contextual Learning. CoRR abs/1602.02454 (2016) - [i30]Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis, Zhiwei Steven Wu:
Bayesian Exploration: Incentivizing Exploration in Bayesian Games. CoRR abs/1602.07570 (2016) - [i29]Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire:
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits. CoRR abs/1606.00313 (2016) - [i28]Tim Roughgarden, Vasilis Syrgkanis, Éva Tardos:
The Price of Anarchy in Auctions. CoRR abs/1607.07684 (2016) - [i27]Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan:
Oracle-Efficient Learning and Auction Design. CoRR abs/1611.01688 (2016) - 2015
- [j2]Vasilis Syrgkanis:
Algorithmic game theory and econometrics. SIGecom Exch. 14(1): 105-108 (2015) - [c23]Uriel Feige, Michal Feldman, Nicole Immorlica, Rani Izsak, Brendan Lucier, Vasilis Syrgkanis:
A Unifying Hierarchy of Valuations with Complements and Substitutes. AAAI 2015: 872-878 - [c22]Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire:
Fast Convergence of Regularized Learning in Games. NIPS 2015: 2989-2997 - [c21]Jason D. Hartline, Vasilis Syrgkanis, Éva Tardos:
No-Regret Learning in Bayesian Games. NIPS 2015: 3061-3069 - [c20]Denis Nekipelov, Vasilis Syrgkanis, Éva Tardos:
Econometrics for Learning Agents. EC 2015: 1-18 - [c19]Brendan Lucier, Vasilis Syrgkanis:
Greedy Algorithms Make Efficient Mechanisms. EC 2015: 221-238 - [c18]Vasilis Syrgkanis, David Kempe, Éva Tardos:
Information Asymmetries in Common-Value Auctions with Discrete Signals. EC 2015: 303 - [c17]Nikhil R. Devanur, Jamie Morgenstern, Vasilis Syrgkanis, S. Matthew Weinberg:
Simple Auctions with Simple Strategies. EC 2015: 305-322 - [c16]Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis:
Bayesian Incentive-Compatible Bandit Exploration. EC 2015: 565-582 - [c15]Nicole Immorlica, Gregory Stoddard, Vasilis Syrgkanis:
Social Status and Badge Design. WWW 2015: 473-483 - [i26]Yishay Mansour, Aleksandrs Slivkins, Vasilis Syrgkanis:
Bayesian Incentive-Compatible Bandit Exploration. CoRR abs/1502.04147 (2015) - [i25]Vasilis Syrgkanis:
Price of Stability in Games of Incomplete Information. CoRR abs/1503.03739 (2015) - [i24]Michal Feldman, Nicole Immorlica, Brendan Lucier, Tim Roughgarden, Vasilis Syrgkanis:
The Price of Anarchy in Large Games. CoRR abs/1503.04755 (2015) - [i23]Brendan Lucier, Vasilis Syrgkanis:
Greedy Algorithms make Efficient Mechanisms. CoRR abs/1503.05608 (2015) - [i22]Thodoris Lykouris, Vasilis Syrgkanis, Éva Tardos:
Learning and Efficiency in Games with Dynamic Population. CoRR abs/1505.00391 (2015) - [i21]