Research outputs
Selected research outputs
- The Complexity of the Simplex Method (Conference Paper - 2015)
- Learning equilibria of games via payoff queries (Journal article - 2015)
- The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions (Journal article - 2013)
- Enumeration of Nash equilibria for two-player games (Journal article - 2009)
- Unique end of potential line (Journal article - 2020)
2026
Empirical Mixnet Design
Wang, Y., Elahi, T., Mavroudis, V., Plumb, E., Savani, R., & Turocy, T. (2026). Empirical Mixnet Design. In Lecture Notes in Computer Science (pp. 209-227). Springer Nature Switzerland. doi:10.1007/978-3-032-08067-7_11
2025
The Complexity of Computing KKT Solutions of Quadratic Programs
Fearnley, J., Goldberg, P., Hollender, A., & Savani, R. (2025). The Complexity of Computing KKT Solutions of Quadratic Programs. In JOURNAL OF THE ACM Vol. 72. doi:10.1145/3745017
The Complexity of Computing KKT Solutions of Quadratic Programs.
Fearnley, J., Goldberg, P. W., Hollender, A., & Savani, R. (2025). The Complexity of Computing KKT Solutions of Quadratic Programs.. J. ACM, 72, 31:1.
Difference rewards policy gradients
Castellini, J., Devlin, S., Oliehoek, F. A., & Savani, R. (2022). Difference rewards policy gradients. In NEURAL COMPUTING & APPLICATIONS. doi:10.1007/s00521-022-07960-5
Monotone Contractions
Batziou, E., Fearnley, J., Gordon, S., Mehta, R., & Savani, R. (2025). Monotone Contractions. In PROCEEDINGS OF THE 57TH ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING, STOC 2025 (pp. 507-517). doi:10.1145/3717823.3718175
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
Ottomano, F., Goulermas, J. Y., Gusev, V., Savani, R., Gaultois, M. W., Manning, T. D., . . . Rosseinsky, M. J. (2025). Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials. DIGITAL DISCOVERY, 4(7), 1794-1811. doi:10.1039/d5dd00010f
From Natural Language to Extensive-Form Game Representations
Deng, S., Wang, Y., & Savani, R. (2025). From Natural Language to Extensive-Form Game Representations. In PROCEEDINGS OF THE 24TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, AAMAS 2025 (pp. 593-601). Retrieved from https://www.webofscience.com/
Monotone Contractions.
Batziou, E., Fearnley, J., Gordon, S., Mehta, R., & Savani, R. (2025). Monotone Contractions.. In M. Koucký, & N. Bansal (Eds.), STOC (pp. 507-517). ACM. Retrieved from https://doi.org/10.1145/3717823
2024
Selfishly Prepaying in Financial Credit Networks
Zhou, H., Wang, Y., Varsos, K., Bishop, N., Savani, R., Calinescu, A., & Wooldridge, M. (2024). Selfishly Prepaying in Financial Credit Networks. The journal of artificial intelligence research, 81.
Market Making with Learned Beta Policies
Wang, Y., Savani, R., Gu, A., Mascioli, C., Turocy, T., & Wellman, M. (2024). Market Making with Learned Beta Policies. In 5TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2024 (pp. 643-651). doi:10.1145/3677052.3698623
The Effect of Liquidity on the Spoofability of Financial Markets
Gu, A., Wang, Y., Mascioli, C., Chakraborty, M., Savani, R., Turocy, T. L., & Wellman, M. P. (2024). The Effect of Liquidity on the Spoofability of Financial Markets. In 5TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2024 (pp. 239-247). doi:10.1145/3677052.3698634
First Order Methods for Geometric Optimization of Crystals: Experimental Analysis
Tsili, A., Dyer, M. S., Gusev, V. V., Krysta, P., & Savani, R. (2024). First Order Methods for Geometric Optimization of Crystals: Experimental Analysis. Advanced Theory and Simulations. doi:10.1002/adts.202400124
First Order Methods for Geometric Optimization of Crystals: Theoretical Derivations
Tsili, A., Dyer, M. S., Gusev, V. V., Krysta, P., & Savani, R. (2024). First Order Methods for Geometric Optimization of Crystals: Theoretical Derivations. Advanced Theory and Simulations. doi:10.1002/adts.202400125
Two Choices Are Enough for P-LCPs, USOs, and Colorful Tangents
Borzechowski, M., Fearnley, J., Gordon, S., Savani, R., Schnider, P., & Weber, S. (2024). Two Choices Are Enough for P-LCPs, USOs, and Colorful Tangents. In Leibniz International Proceedings in Informatics Lipics Vol. 297. doi:10.4230/LIPIcs.ICALP.2024.32
The Complexity of Computing KKT Solutions of Quadratic Programs
Fearnley, J., Goldberg, P. W., Hollender, A., & Savani, R. (2024). The Complexity of Computing KKT Solutions of Quadratic Programs. PROCEEDINGS OF THE 56TH ANNUAL ACM SYMPOSIUM ON THEORY OF COMPUTING, STOC 2024, 892-903. doi:10.1145/3618260.3649647
A Strategic Analysis of Prepayments in Financial Credit Networks
Li, H., Qi, L., Liu, W., Xu, X., Dou, W., Cao, Y., . . . Zhou, X. (2024). Balancing User-Item Structure and Interaction with Large Language Models and Optimal Transport for Multimedia Recommendation. In Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence (pp. 3027-3035). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2024/337
Ordinal Potential-based Player Rating
Vadori, N., & Savani, R. (2024). Ordinal Potential-based Player Rating. In INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 238 Vol. 238. Retrieved from https://www.webofscience.com/
Policy Space Response Oracles: A Survey
Ye, J., Zhang, W., Li, Z., Li, J., Zhao, M., & Tsung, F. (2024). MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning. In Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence (pp. 7913-7921). International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2024/880
Strategic Analysis of Prepayments in Financial Credit Networks
Zhou, H., Wang, Y., Varsos, K., Bishop, N., Savani, R., Calinescu, A., & Wooldridge, M. (2024). Strategic Analysis of Prepayments in Financial Credit Networks. In PROCEEDINGS OF THE THIRTY-THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2024 (pp. 3040-3048). Retrieved from https://www.webofscience.com/
Two Choices Are Enough for P-LCPs, USOs, and Colorful Tangents.
Borzechowski, M., Fearnley, J., Gordon, S., Savani, R., Schnider, P., & Weber, S. (2024). Two Choices Are Enough for P-LCPs, USOs, and Colorful Tangents.. In K. Bringmann, M. Grohe, G. Puppis, & O. Svensson (Eds.), ICALP Vol. 297 (pp. 32:1). Schloss Dagstuhl - Leibniz-Zentrum für Informatik. Retrieved from https://www.dagstuhl.de/dagpub/978-3-95977-322-5
2023
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
Coletta, A., Jerome, J., Savani, R., & Vyetrenko, S. (2023). Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness. In PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023 (pp. 27-35). doi:10.1145/3604237.3626854
Mbt-gym: Reinforcement learning for model-based limit order book trading
Jerome, J., Sanchez-Betancourt, L., Savani, R., & Herdegen, M. (2023). Mbt-gym: Reinforcement learning for model-based limit order book trading. In PROCEEDINGS OF THE 4TH ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2023 (pp. 619-627). doi:10.1145/3604237.3626873
Analysing Factorizations of Action-Value Networks for Cooperative Multi-Agent Reinforcement Learning
Difference Rewards Policy Gradients
Reinforcement Learning in Crystal Structure Prediction
Zamaraeva, E., Collins, C. M., Antypov, D., Gusev, V. V., Savani, R., Dyer, M. S., . . . Spirakis, P. G. (2023). Reinforcement Learning in Crystal Structure Prediction. Digital Discovery. doi:10.1039/d3dd00063j
Recommender Systems and Supplier Competition on Platforms
Fletcher, A., Ormosi, P. L., & Savani, R. (2023). Recommender Systems and Supplier Competition on Platforms. Journal of Competition Law and Economics. doi:10.1093/joclec/nhad009
Reinforcement Learning in Crystal Structure Prediction
The Complexity of Gradient Descent: CLS = PPAD $\cap$ PLS
Biased Recommender Systems And Supplier Competition
Recommender Systems and Competition on Subscription-Based Platforms
2022
Market Making with Scaled Beta Policies
Jerome, J., Palmer, G., & Savani, R. (2022). Market Making with Scaled Beta Policies. In 3RD ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE, ICAIF 2022 (pp. 214-222). doi:10.1145/3533271.3561745
A Faster Algorithm for Finding Tarski Fixed Points
Fearnley, J., Palvolgyi, D., & Savani, R. (2022). A Faster Algorithm for Finding Tarski Fixed Points. ACM TRANSACTIONS ON ALGORITHMS, 18(3). doi:10.1145/3524044
A Faster Algorithm for Finding Tarski Fixed Points
Fearnley, J., Pálvölgyi, D., & Savani, R. (2022). A Faster Algorithm for Finding Tarski Fixed Points. ACM Transactions on Algorithms, 18(3), 1-23. doi:10.1145/3524044
Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures
Vadori, N., Savani, R., Spooner, T., & Ganesh, S. (2022). Consensus Multiplicative Weights Update: Learning to Learn using Projector-based Game Signatures. In INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162. Retrieved from https://www.webofscience.com/
Generative Models over Neural Controllers for Transfer Learning
Butterworth, J., Savani, R., & Tuyls, K. (2022). Generative Models over Neural Controllers for Transfer Learning. In Unknown Book (Vol. 13398, pp. 400-413). doi:10.1007/978-3-031-14714-2_28
Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent
Gemp, I., Savani, R., Lanctot, M., Bachrach, Y., Anthony, T., Everett, R., . . . Kramár, J. (2022). Sample-based Approximation of Nash in Large Many-Player Games via Gradient Descent. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 1 (pp. 507-515).
2021
A deep learning approach to identify unhealthy advertisements in street view images
Palmer, G., Green, M., Boyland, E., Vasconcelos, Y. S. R., Savani, R., & Singleton, A. (2021). A deep learning approach to identify unhealthy advertisements in street view images. SCIENTIFIC REPORTS, 11(1). doi:10.1038/s41598-021-84572-4
The Complexity of Gradient Descent
Savani, R. (2021). The Complexity of Gradient Descent. In 41ST IARCS ANNUAL CONFERENCE ON FOUNDATIONS OF SOFTWARE TECHNOLOGY AND THEORETICAL COMPUTER SCIENCE, FSTTCS 2021 Vol. 213. doi:10.4230/LIPIcs.FSTTCS.2021.5
Trading via Selective Classification
Chalkidis, N., & Savani, R. (2021). Trading via Selective Classification. In ICAIF 2021: THE SECOND ACM INTERNATIONAL CONFERENCE ON AI IN FINANCE. doi:10.1145/3490354.3494379
Trading via Selective Classification
Trading via Selective Classification
Chalkidis, N., & Savani, R. (2021). Trading via Selective Classification. Retrieved from http://dx.doi.org/10.1145/3490354.3494379
Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning
Castellini, J., Oliehoek, F. A., Savani, R., & Whiteson, S. (2021). Analysing factorizations of action-value networks for cooperative multi-agent reinforcement learning. AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 35(2). doi:10.1007/s10458-021-09506-w
The complexity of gradient descent: CLS = PPAD ∩ PLS
Fearnley, J., Goldberg, P. W., Hollender, A., & Savani, R. (2021). The Complexity of Gradient Descent: CLS = PPAD ∧ PLS. STOC '21: PROCEEDINGS OF THE 53RD ANNUAL ACM SIGACT SYMPOSIUM ON THEORY OF COMPUTING, 46-59. doi:10.1145/3406325.3451052
Reachability Switching Games
A faster algorithm for finding Tarski fixed points
A Faster Algorithm for Finding Tarski Fixed Points
Fearnley, J., & Savani, R. (2021). A Faster Algorithm for Finding Tarski Fixed Points. 38TH INTERNATIONAL SYMPOSIUM ON THEORETICAL ASPECTS OF COMPUTER SCIENCE (STACS 2021), 187. doi:10.4230/LIPIcs.STACS.2021.29
A deep learning approach to identify unhealthy advertisements in street view images
Difference Rewards Policy Gradients
Castellini, J., Devlin, S., Oliehoek, F. A., & Savani, R. (2021). Difference Rewards Policy Gradients. In International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 1475-1477). IEEE Computer Society. doi:10.65109/rxqz8456
Difference Rewards Policy Gradients
Castellini, J., Devlin, S., Oliehoek, F. A., & Savani, R. (2020). Difference Rewards Policy Gradients. Retrieved from http://dx.doi.org/10.1007/s00521-022-07960-5
Reachability Switching Games.
Fearnley, J., Gairing, M., Mnich, M., & Savani, R. (2021). REACHABILITY SWITCHING GAMES. In LOGICAL METHODS IN COMPUTER SCIENCE Vol. 17. doi:10.23638/LMCS-17(2:10)2021
2020
Unique end of potential line
Fearnley, J. S., Gordon, S., Mehta, R., & Savani, R. (2020). Unique End of Potential Line. Journal of Computer and System Sciences, 114, 1-35. doi:10.1016/j.jcss.2020.05.007
Bayesian optimisation of restriction zones for bluetongue control.
Spooner, T., Jones, A. E., Fearnley, J., Savani, R., Turner, J., & Baylis, M. (2020). Bayesian optimisation of restriction zones for bluetongue control.. Scientific Reports, 10(1), 15139. doi:10.1038/s41598-020-71856-4
The Automated Inspection of Opaque Liquid Vaccines
Palmer, G., Schnieders, B., Savani, R., Tuyls, K., Fossel, J., & Flore, H. (2020). The Automated Inspection of Opaque Liquid Vaccines. In ECAI 2020: 24TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE Vol. 325 (pp. 1898-1905). doi:10.3233/FAIA200307
One-Clock Priced Timed Games are PSPACE-hard
Fearnley, J., Ibsen-Jensen, R., & Savani, R. (2020). One-Clock Priced Timed Games are PSPACE-hard. LICS '20: Proceedings of the 35th Annual ACM/IEEE Symposium on Logic in Computer Science. Retrieved from http://arxiv.org/abs/2001.04458v2
Mapping the geodemographics of digital inequality in Great Britain: An integration of machine learning into small area estimation
Singleton, A., Alexiou, A., & Savani, R. (2020). Mapping the geodemographics of digital inequality in Great Britain: An integration of machine learning into small area estimation. Computers, Environment and Urban Systems, 82, 101486. doi:10.1016/j.compenvurbsys.2020.101486
Tree Polymatrix Games are PPAD-hard
Deligkas, A., Fearnley, J., & Savani, R. (2020). Tree Polymatrix Games are PPAD-hard. Retrieved from http://arxiv.org/abs/2002.12119v1
Robust Market Making via Adversarial Reinforcement Learning
Spooner, T., & Savani, R. (2020). Robust Market Making via Adversarial Reinforcement Learning. In International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 2014-2016). IEEE Computer Society. doi:10.65109/stvw4095
One-Clock Priced Timed Games are PSPACE-hard
Tree Polymatrix Games are PPAD-hard
The Automated Inspection of Opaque Liquid Vaccines
Robust market making via adversarial reinforcement learning
Spooner, T., & Savani, R. (2020). Robust Market Making via Adversarial Reinforcement Learning. In PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 4590-4596). Retrieved from https://www.webofscience.com/
The Automated Inspection of Opaque Liquid Vaccines.
Palmer, G., Schnieders, B., Savani, R., Tuyls, K., Fossel, J. -D., & Flore, H. (2020). The Automated Inspection of Opaque Liquid Vaccines.. In G. D. Giacomo, A. Catalá, B. Dilkina, M. Milano, S. Barro, A. Bugarín, & J. Lang (Eds.), ECAI Vol. 325 (pp. 1898-1905). IOS Press. Retrieved from https://doi.org/10.3233/FAIA325
Tree Polymatrix Games Are PPAD-Hard.
Deligkas, A., Fearnley, J., & Savani, R. (2020). Tree Polymatrix Games Are PPAD-Hard.. In A. Czumaj, A. Dawar, & E. Merelli (Eds.), ICALP Vol. 168 (pp. 38:1). Schloss Dagstuhl - Leibniz-Zentrum für Informatik. Retrieved from https://www.dagstuhl.de/dagpub/978-3-95977-138-2
2019
Evolving indoor navigational strategies using gated recurrent units in NEAT
Butterworth, J., Savani, R., & Tuyls, K. (2019). Evolving indoor navigational strategies using gated recurrent units in NEAT. In GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 111-112). doi:10.1145/3319619.3321995
Unique End of Potential Line
Fearnley, J., Gordon, S., Mehta, R., & Savani, R. (2018). Unique End of Potential Line. Retrieved from http://arxiv.org/abs/1811.03841v1
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Palmer, G., Savani, R., & Tuyls, K. (2019). Negative Update Intervals in Deep Multi-Agent Reinforcement Learning. In International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 43-51). IEEE Computer Society. doi:10.65109/afte1429
The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning
Castellini, J., Oliehoek, F. A., Savani, R., & Whiteson, S. (2019). The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning. IEEE Computer Society. doi:10.65109/smgt4941
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Evolving Indoor Navigational Strategies Using Gated Recurrent Units In NEAT
Distributed Methods for Computing Approximate Equilibria
Czumaj, A., Deligkas, A., Fasoulakis, M., Fearnley, J. S., Jurdzinski, M., & Savani, R. (2019). Distributed Methods for Computing Approximate Equilibria. Algorithmica: an international journal in computer science, 81, 1205-1231. doi:10.1007/s00453-018-0465-y
Beyond Local Nash Equilibria for Adversarial Networks
Oliehoek, F. A., Savani, R., Gallego, J., van der Pol, E., & Gross, R. (2019). Beyond Local Nash Equilibria for Adversarial Networks. In ARTIFICIAL INTELLIGENCE, BNAIC 2018 (Vol. 1021, pp. 73-89). doi:10.1007/978-3-030-31978-6_7
Computing Stable Outcomes in Symmetric Additively Separable Hedonic Games
Gairing, M., & Savani, R. (2019). Computing Stable Outcomes in Symmetric Additively Separable Hedonic Games. In MATHEMATICS OF OPERATIONS RESEARCH (Vol. 44, Iss. 3, pp. 1101-1121). doi:10.1287/moor.2018.0960
Computing Stable Outcomes in Symmetric Additively Separable Hedonic Games.
Gairing, M., & Savani, R. (2019). Computing Stable Outcomes in Symmetric Additively Separable Hedonic Games.. Math. Oper. Res., 44, 1101-1121. doi:10.1287/moor.2018.0960
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Palmer, G., Savani, R., & Tuyls, K. (2019). Negative Update Intervals in Deep Multi-Agent Reinforcement Learning. In AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (pp. 43-51). Retrieved from https://www.webofscience.com/
The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning
Castellini, J., Oliehoek, F. A., Savani, R., & Whiteson, S. (2019). The Representational Capacity of Action-Value Networks for Multi-Agent Reinforcement Learning. In AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (pp. 1862-1864). Retrieved from https://www.webofscience.com/
2018
Unique End of Potential Line
The Complexity of All-Switches Strategy Improvement
Fearnley, J. S., & Savani, R. S. J. (2018). The Complexity of All-Switches Strategy Improvement. Logical Methods in Computer Science, 14(4), 1-57. doi:10.23638/LMCS-14(4:9)2018
The Complexity of All-switches Strategy Improvement
Inapproximability results for constrained approximate Nash equilibria
Deligkas, A., Fearnley, J. S., & Savani, R. (2018). Inapproximability results for constrained approximate Nash equilibria. Information and Computation, 262(1), 40-56. doi:10.1016/j.ic.2018.06.001
Beyond Local Nash Equilibria for Adversarial Networks
Lenient Multi-Agent Deep Reinforcement Learning
Palmer, G., Tuyls, K., Bloembergen, D., & Savani, R. (2018). Lenient Multi-Agent Deep Reinforcement Learning. In International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 443-451). IEEE Computer Society. doi:10.65109/qdcv6054
Market Making via Reinforcement Learning
Spooner, T., Fearnley, J., Savani, R., & Koukorinis, A. (2018). Market Making via Reinforcement Learning. In International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 434-442). IEEE Computer Society. doi:10.65109/yyqw8667
Reachability switching games
Fearnley, J., Gairing, M., Mnich, M., & Savani, R. (2017). Reachability Switching Games. In April (Vol. 22, pp. 2021). Retrieved from http://dx.doi.org/10.23638/LMCS-17(2:10)2021
Space Debris Removal: Learning to Cooperate and the Price of Anarchy
Klima, R., Bloembergen, D., Savani, R., Tuyls, K., Wittig, A., Sapera, A., & Izzo, D. (2018). Space Debris Removal: Learning to Cooperate and the Price of Anarchy. Frontiers in Robotics and AI, 5. doi:10.3389/frobt.2018.00054
End of Potential Line
Market Making via Reinforcement Learning
End of Potential Line
Fearnley, J., Gordon, S., Mehta, R., & Savani, R. (2018). End of Potential Line. Retrieved from http://arxiv.org/abs/1804.03450v2
Lenient Multi-Agent Deep Reinforcement Learning
Symmetric Decomposition of Asymmetric Games
Tuyls, K., Perolat, J., Lanctot, M., Ostrovski, G., Savani, R., Leibo, J., . . . Legg, S. (2018). Symmetric Decomposition of Asymmetric Games. Scientific Reports, 8. doi:10.1038/s41598-018-19194-4
Symmetric Decomposition of Asymmetric Games
Beyond Local Nash Equilibria for Adversarial Networks.
Oliehoek, F. A., Savani, R., Gallego-Posada, J., Pol, E. V. D., & Groß, R. (2018). Beyond Local Nash Equilibria for Adversarial Networks.. In M. Atzmueller, & W. Duivesteijn (Eds.), BNCAI Vol. 1021 (pp. 73-89). Springer. Retrieved from https://doi.org/10.1007/978-3-030-31978-6
Lenient Multi-Agent Deep Reinforcement Learning
Palmer, G., Tuyls, K., Bloembergen, D., & Savani, R. (2018). Lenient Multi-Agent Deep Reinforcement Learning. In PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) (pp. 443-451). Retrieved from https://www.webofscience.com/
Market Making via Reinforcement Learning
Spooner, T., Fearnley, J., Savani, R., & Koukorinis, A. (2018). Market Making via Reinforcement Learning. In PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) (pp. 434-442). Retrieved from https://www.webofscience.com/
2017
GANGs: Generative Adversarial Network Games
GANGs: Generative Adversarial Network Games
Oliehoek, F. A., Savani, R., Gallego-Posada, J., Pol, E. V. D., Jong, E. D. D., & Gross, R. (2017). GANGs: Generative Adversarial Network Games. Retrieved from http://arxiv.org/abs/1712.00679v2
Computing Constrained Approximate Equilibria in Polymatrix Games
Deligkas., Fearnley., & Savani, R. S. J. (2017). Computing Constrained Approximate Equilibria in Polymatrix Games. In Symposium on Algorithmic Game Theory (SAGT) (pp. 93-105). doi:10.1007/978-3-319-66700-3_8
LiftUpp: Support to Develop Learner Performance
Oliehoek, F. A., Savani, R., Adderton, E. A., Cui, X., Jackson, D., Jimmieson, P., . . . Dawson, L. (2017). LiftUpp: Support to Develop Learner Performance. In International Journal of Artificial Intelligence in Education. Wuhan, China: International Artificial Intelligence in Education Society. doi:10.1007/978-3-319-61425-0_62
Computing Constrained Approximate Equilibria in Polymatrix Games
Inapproximability Results for Approximate Nash Equilibria
LiftUpp: Support to develop learner performance
CLS: New Problems and Completeness
CLS: New Problems and Completeness
Fearnley, J., Gordon, S., Mehta, R., & Savani, R. (2017). CLS: New Problems and Completeness. Retrieved from http://arxiv.org/abs/1702.06017v2
Computing Approximate Nash Equilibria in Polymatrix Games
Deligkas, A., Fearnley, J., Savani, R., & Spirakis, P. (2017). Computing Approximate Nash Equilibria in Polymatrix Games. ALGORITHMICA, 77(2), 487-514. doi:10.1007/s00453-015-0078-7
Computing Constrained Approximate Equilibria in Polymatrix Games.
Deligkas, A., Fearnley, J., & Savani, R. (2017). Computing Constrained Approximate Equilibria in Polymatrix Games.. CoRR, abs/1705.02266.
LiftUpp: Support to Develop Learner Performance
Oliehoek, F. A., Savani, R., Adderton, E., Cui, X., Jackson, D., Jimmieson, P., . . . Dawson, L. (2017). LiftUpp: Support to Develop Learner Performance. In Artificial Intelligence in Education (Vol. 10331, pp. 553-556). Springer Nature. doi:10.1007/978-3-319-61425-0_62
2016
Distributed Methods for Computing Approximate Equilibria
Czumaj, A., Deligkas, A., Fasoulakis, M., Fearnley, J. S., Jurdzinski, M., & Savani, R. (2016). Distributed Methods for Computing Approximate Equilibria. In Lecture Notes in Computer Science Vol. 10123. Berlin: Springer Nature. doi:10.1007/978-3-662-54110-4_2
Inapproximability Results for Approximate Nash Equilibria.
Deligkas, A., Fearnley, J., & Savani, R. (2016). Inapproximability Results for Approximate Nash Equilibria.. In Y. Cai, & A. Vetta (Eds.), WINE Vol. 10123 (pp. 29-43). Springer. Retrieved from https://doi.org/10.1007/978-3-662-54110-4
Approximate Well-supported Nash Equilibria Below Two-thirds
Fearnley, J., Goldberg, P. W., Savani, R., & Sørensen, T. B. (2016). Approximate Well-supported Nash Equilibria Below Two-thirds. Algorithmica, 76, 297-319. doi:10.1007/s00453-015-0029-3
Space Debris Removal: A Game Theoretic Analysis
Klima, R., Bloembergen, D., Savani, R., Tuyls, K., Hennes, D., & Izzo, D. (2016). Space Debris Removal: A Game Theoretic Analysis. Games, 7(3). doi:10.3390/g7030020
Finding Approximate Nash Equilibria of Bimatrix Games via Payoff Queries
Fearnley, J., & Savani, R. (2016). Finding Approximate Nash Equilibria of Bimatrix Games via Payoff Queries. ACM Transactions on Economics and Computation (TEAC), 4(4). doi:10.1145/2956579
Finding approximate Nash equilibria of bimatrix games via payoff queries
Fearnley, J., & Savani, R. (2013). Finding Approximate Nash Equilibria of Bimatrix Games via Payoff Queries. Retrieved from http://arxiv.org/abs/1310.7419v2
An Empirical Study on Computing Equilibria in Polymatrix Games
Deligkas, A., Fearnley, J., Igwe, T. P., & Savani, R. (2016). An Empirical Study on Computing Equilibria in Polymatrix Games. IEEE Computer Society. doi:10.65109/wfsv6966
An Empirical Study on Computing Equilibria in Polymatrix Games
Unit vector games
Savani, R., & von Stengel, B. (2016). Unit vector games. INTERNATIONAL JOURNAL OF ECONOMIC THEORY, 12(1), 7-27. doi:10.1111/ijet.12077
Unit Vector Games
An empirical study on computing equilibria in polymatrix games
Deligkas, A., Fearnley, J., Igwe, T. P., & Savani, R. (2016). An Empirical Study on Computing Equilibria in Polymatrix Games. In AAMAS'16: PROCEEDINGS OF THE 2016 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (pp. 186-195). Retrieved from https://www.webofscience.com/
Hedonic Games
Aziz, H., & Savani, R. (2016). Hedonic Games. In Handbook of Computational Social Choice (pp. 356-376). Cambridge University Press. doi:10.1017/cbo9781107446984.016
Inapproximability results for approximate Nash equilibria
Deligkas, A., Fearnley, J., & Savani, R. (2016). Inapproximability Results for Approximate Nash Equilibria. Retrieved from http://arxiv.org/abs/1608.03574v3
Preface
Gairing, M., & Savani, R. (2016). Preface (Vol. 9928 LNCS).
Space Debris Removal: A Game Theoretic Analysis
Klima, R., Bloembergen, D., Savani, R., Tuyls, K., Hennes, D., & Izzo, D. (2016). Space Debris Removal: A Game Theoretic Analysis. In ECAI 2016: 22ND EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE Vol. 285 (pp. 1658-1659). doi:10.3233/978-1-61499-672-9-1658
Space Debris Removal: A Game Theoretic Analysis
Klima Richard., Bloembergen Daan., Savani Rahul., Tuyls Karl., Hennes Daniel., & Izzo Dario. (2016). Space Debris Removal: A Game Theoretic Analysis. In Frontiers in Artificial Intelligence and Applications. IOS Press. doi:10.3233/978-1-61499-672-9-1658
The Complexity of All-Switches Strategy Improvement
Fearnley, J., & Savani, R. (2016). The Complexity of All-Switches Strategy Improvement. In R. Krauthgamer (Ed.), Proceedings of the 2016 Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 130-139). Society for Industrial and Applied Mathematics. doi:10.1137/1.9781611974331.ch10
2015
Distributed Methods for Computing Approximate Equilibria
Computing stable outcomes in symmetric additively-separable hedonic games
Learning equilibria of games via payoff queries
Fearnley, J., Gairing, M., Goldberg, P. W., & Savani, R. (2015). Learning Equilibria of Games via Payoff Queries. JOURNAL OF MACHINE LEARNING RESEARCH, 16, 1305-1344. Retrieved from https://www.webofscience.com/
The Complexity of the Simplex Method
Fearnley, J., & Savani, R. (2015). The Complexity of the Simplex Method. In STOC '15: Proceedings of the forty-seventh annual ACM symposium on Theory of Computing (pp. 201-208). doi:10.1145/2746539.2746558
An Empirical Study of Finding Approximate Equilibria in Bimatrix Games
An Empirical Study of Finding Approximate Equilibria in Bimatrix Games
Fearnley, J., Igwe, T. P., & Savani, R. (2015). An Empirical Study of Finding Approximate Equilibria in Bimatrix Games. In EXPERIMENTAL ALGORITHMS, SEA 2015 Vol. 9125 (pp. 339-351). doi:10.1007/978-3-319-20086-6_26
An Empirical Study of Finding Approximate Equilibria in Bimatrix Games.
Fearnley, J., Igwe, T. P., & Savani, R. (2015). An Empirical Study of Finding Approximate Equilibria in Bimatrix Games.. In E. Bampis (Ed.), SEA Vol. 9125 (pp. 339-351). Springer. Retrieved from https://doi.org/10.1007/978-3-319-20086-6
Game Theory Explorer: software for the applied game theorist
Savani, R., & von Stengel, B. (2015). Game Theory Explorer: software for the applied game theorist. COMPUTATIONAL MANAGEMENT SCIENCE, 12(1), 5-33. doi:10.1007/s10287-014-0206-x
2014
Approximate Well-supported Nash Equilibria below Two-thirds
Computing Approximate Nash Equilibria in Polymatrix Games
The Complexity of the Simplex Method
Polylogarithmic Supports are required for Approximate Well-Supported Nash Equilibria below 2/3
Game Theory Explorer - Software for the Applied Game Theorist
Finding Approximate Nash Equilibria of Bimatrix Games via Payoff Queries
Learning Equilibria of Games via Payoff Queries
A Data Rich Money Market Model - Agent-based Modelling for Financial Stability
Devine, P., & Savani, R. (2014). A Data Rich Money Market Model - Agent-based Modelling for Financial Stability. In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (pp. 231-236). SCITEPRESS - Science and Technology Publications. doi:10.5220/0005096602310236
Computing Approximate Nash Equilibria in Polymatrix Games
Deligkas, A., Fearnley, J., Savani, R., & Spirakis, P. (2014). Computing Approximate Nash Equilibria in Polymatrix Games. In Web and Internet Economics (Vol. 8877, pp. 58-71). Springer Nature. doi:10.1007/978-3-319-13129-0_5
Computing Approximate Nash Equilibria in Polymatrix Games.
Deligkas, A., Fearnley, J., Savani, R., & Spirakis, P. (2014). Computing Approximate Nash Equilibria in Polymatrix Games. In WEB AND INTERNET ECONOMICS Vol. 8877 (pp. 58-71). Retrieved from https://www.webofscience.com/
Cooperative Max Games and Agent Failures
Bachrach, Y., Savani, R., & Shah, N. (2014). Cooperative Max Games and Agent Failures. In AAMAS'14: PROCEEDINGS OF THE 2014 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (pp. 29-36). Retrieved from https://www.webofscience.com/
Equilibrium Computation (Dagstuhl Seminar 14342)
Megiddo, N., Mehlhorn, K., Savani, R., & Vazirani, V. (2014). Equilibrium Computation (Dagstuhl Seminar 14342). Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. doi:10.4230/DagRep.4.8.73
Finding approximate nash equilibria of bimatrix games via payoff queries
Fearnley, J., & Savani, R. (2014). Finding approximate nash equilibria of bimatrix games via payoff queries. In Proceedings of the fifteenth ACM conference on Economics and computation (pp. 657-674). ACM. doi:10.1145/2600057.2602847
Increasing VCG Revenue by Decreasing the Quality of Items
Guo, M., Deligkas, A., & Savani, R. (2014). Increasing VCG Revenue by Decreasing the Quality of Items. In PROCEEDINGS OF THE TWENTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (pp. 705-711). Retrieved from https://www.webofscience.com/
2013
Polylogarithmic Supports are required for Approximate Well-Supported Nash Equilibria below 2/3
Anbalagan, Y., Norin, S., Savani, R., & Vetta, A. (2013). Polylogarithmic Supports are required for Approximate Well-Supported Nash Equilibria below 2/3. Retrieved from http://arxiv.org/abs/1309.7258v2
On the approximation performance of fictitious play in finite games
Goldberg, P. W., Savani, R., Sorensen, T. B., & Ventre, C. (2013). On the approximation performance of fictitious play in finite games. INTERNATIONAL JOURNAL OF GAME THEORY, 42(4), 1059-1083. doi:10.1007/s00182-012-0362-6
Learning equilibria of games via payoff queries
Fearnley, J., Gairing, M., Goldberg, P., & Savani, R. (2013). Learning equilibria of games via payoff queries. In Proceedings of the fourteenth ACM conference on Electronic commerce (pp. 397-414). ACM. doi:10.1145/2482540.2482558
The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions
Goldberg, P. W., Papadimitriou, C. H., & Savani, R. (2013). The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions. ACM Transactions on Economics and Computation, 1(2), 1-25. doi:10.1145/2465769.2465774
Game Theory Explorer
Egesdal, M., Gomez-Jordana, A., Pelissier, C., Savani, R. S. J., von Stengel, B., & Prause, M. (2013). Game Theory Explorer [Computer Software].
Game Theory Explorer
Savani, R. S. J. (2013). Game Theory Explorer [Computer Software]. Retrieved from http://gametheoryexplorer.org/
Learning Equilibria of Games via Payoff Queries
Fearnley, J., Gairing, M., Goldberg, P., & Savani, R. (2013). Learning Equilibria of Games via Payoff Queries. Retrieved from http://arxiv.org/abs/1302.3116v4
Polylogarithmic Supports Are Required for Approximate Well-Supported Nash Equilibria below 2/3
Anbalagan, Y., Norin, S., Savani, R., & Vetta, A. (2013). Polylogarithmic Supports Are Required for Approximate Well-Supported Nash Equilibria below 2/3. In Web and Internet Economics (Vol. 8289, pp. 15-23). Springer Nature. doi:10.1007/978-3-642-45046-4_2
2012
Approximate Well-Supported Nash Equilibria Below Two-Thirds
Fearnley, J., Goldberg, P. W., Savani, R., & Sørensen, T. B. (2012). Approximate Well-Supported Nash Equilibria Below Two-Thirds. In Unknown Conference (pp. 108-119). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33996-7_10
High-Frequency Trading: The Faster, the Better?
Savani, R. (2012). High-Frequency Trading: The Faster, the Better?. IEEE INTELLIGENT SYSTEMS, 27(4), 70-73. doi:10.1109/MIS.2012.75
2011
The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions.
Goldberg, P. W., Papadimitriou, C. H., & Savani, R. (2011). The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions. In 2011 IEEE 52ND ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE (FOCS 2011) (pp. 67-76). doi:10.1109/FOCS.2011.26
On the Approximation Performance of Fictitious Play in Finite Games
Goldberg, P. W., Savani, R., Sorensen, T. B., & Ventre, C. (2011). On the Approximation Performance of Fictitious Play in Finite Games. In ALGORITHMS - ESA 2011 Vol. 6942 (pp. 93-105). Retrieved from https://www.webofscience.com/
The Complexity of the Homotopy Method, Equilibrium Selection, and Lemke-Howson Solutions
On the Approximation Performance of Fictitious Play in Finite Games
Computing Stable Outcomes in Hedonic Games with Voting-Based Deviations
Gairing, M., & Savani, R. (2011). Computing Stable Outcomes in Hedonic Games with Voting-Based Deviations. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011) (pp. 559-566). Taipei: -. Retrieved from http://portal.acm.org/
Computing stable outcomes in hedonic games with voting-based deviations
Gairing, M., & Savani, R. (2011). Computing stable outcomes in hedonic games with voting-based deviations. In 10th International Conference on Autonomous Agents and Multiagent Systems 2011 Aamas 2011 Vol. 1 (pp. 521-528).
2010
Computing Stable Outcomes in Hedonic Games
Gairing, M., & Savani, R. (2010). Computing Stable Outcomes in Hedonic Games. In ALGORITHMIC GAME THEORY Vol. 6386 (pp. 174-185). Retrieved from https://www.webofscience.com/
Linear complementarity algorithms for infinite games
Fearnley, J., Jurdzinski, M., & Savani, R. (2010). Linear Complementarity Algorithms for Infinite Games. In SOFSEM 2010: THEORY AND PRACTICE OF COMPUTER SCIENCE, PROCEEDINGS Vol. 5901 (pp. 382-+). Retrieved from https://www.webofscience.com/
Linear complementarity algorithms for infinite games
Fearnley, J., Jurdziński, M., & Savani, R. (2010). Linear complementarity algorithms for infinite games. In 36th Conference on Current Trends in Theory and Practice of Computer Science (pp. 382-393). Czech Republic: Springer-Verlag.
2009
Wiretapping a hidden network
Aziz, H., Lachish, O., Paterson, M., & Savani, R. (2009). Wiretapping a Hidden Network. In INTERNET AND NETWORK ECONOMICS, PROCEEDINGS Vol. 5929 (pp. 438-+). Retrieved from https://www.webofscience.com/
Power Indices in Spanning Connectivity Games
Aziz, H., Lachish, O., Paterson, M., & Savani, R. (2009). Power Indices in Spanning Connectivity Games. In ALGORITHMIC ASPECTS IN INFORMATION AND MANAGEMENT, PROCEEDINGS Vol. 5564 (pp. 55-67). Retrieved from https://www.webofscience.com/
Wiretapping a hidden network
Enumeration of Nash equilibria for two-player games
Avis, D., Rosenberg, G. D., Savani, R., & von Stengel, B. (2010). Enumeration of Nash equilibria for two-player games. ECONOMIC THEORY, 42(1), 9-37. doi:10.1007/s00199-009-0449-x
Linear Complementarity Algorithms for Infinite Games
Spanning connectivity games
Spanning connectivity games
Aziz, H., Lachish, O., Paterson, M., & Savani, R. (2009). Spanning connectivity games. Retrieved from http://arxiv.org/abs/0906.3643v1
Multi-strategy trading utilizing market regimes
Mlnarik, H., Ramamoorthy, S., & Savani, R. (2009). Multi-strategy trading utilizing market regimes.
Wiretapping a Hidden Network
Aziz, H., Lachish, O., Paterson, M., & Savani, R. (2009). Wiretapping a Hidden Network. In Internet and Network Economics (Vol. 5929, pp. 438-446). Springer Nature. doi:10.1007/978-3-642-10841-9_40
2008
A simple P-matrix linear complementarity problem for discounted games
Jurdzinski, M., & Savani, R. (2008). A simple P-matrix linear complementarity problem for discounted games. In LOGIC AND THEORY OF ALGORITHMS Vol. 5028 (pp. 283-293). doi:10.1007/978-3-540-69407-6_32
Good neighbors are hard to find: computational complexity of network formation
Baron, R., Durieu, J., Haller, H., Savani, R., & Solal, P. (2008). Good neighbors are hard to find: computational complexity of network formation. REVIEW OF ECONOMIC DESIGN, 12(1), 1-19. doi:10.1007/s10058-008-0043-x
2006
Hard-to-solve bimatrix games
Savani, R., & von Stengel, B. (2006). Hard-to-solve bimatrix games. ECONOMETRICA, 74(2), 397-429. doi:10.1111/j.1468-0262.2006.00667.x
'Finding Nash equilibria of bimatrix games'
Savani, R. (2006). 'Finding Nash equilibria of bimatrix games'. (PhD Thesis, London School of Economics and Political Science).
2005
Mixed-species aggregations in birds:: zenaida doves, <i>Zenaida aurita</i>, respond to the alarm calls of carib grackles, <i>Quiscalus lugubris</i>
Griffin, A. S., Savani, R. S., Hausmanis, K., & Lefebvre, L. (2005). Mixed-species aggregations in birds:: zenaida doves, <i>Zenaida aurita</i>, respond to the alarm calls of carib grackles, <i>Quiscalus lugubris</i>. ANIMAL BEHAVIOUR, 70, 507-515. doi:10.1016/j.anbehav.2004.11.023
A novel strategy for the Penn-Lehman automated trading competition
Veal, B., & Savani, R. (2005). A novel strategy for the Penn-Lehman automated trading competition. Retrieved from http://www.cdam.lse.ac.uk/Reports/Abstracts/cdam-2005-12.html
2004
Exponentially many steps for finding a nash equilibrium in a bimatrix game
Savani, R., & von Stengel, B. (2004). Exponentially many steps for finding a nash equilibrium in a bimatrix game. In 45TH ANNUAL IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, PROCEEDINGS (pp. 258-267). doi:10.1109/FOCS.2004.28
Challenge instances for NASH
Savani, R. (2004). Challenge instances for NASH. Retrieved from http://www.cdam.lse.ac.uk/Reports/Abstracts/cdam-2004-14.html
2002
Solve a bimatrix game
Savani, R. (2002). Solve a bimatrix game [Internet (free access)]. Retrieved from http://banach.lse.ac.uk/form.html