Aleksei Shpilman

Publications³³

• Chapter Vladimir Egorov, Alexei Shpilman. Scalable Multi-Agent Model-Based Reinforcement Learning, in: AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems, 2022. P. 381-390.
• Article Lukashina N., Kartysheva E., Spjuth O., Elizaveta Virko, Shpilman A. SimVec: predicting polypharmacy side effects for new drugs // Journal of Cheminformatics. 2022. Vol. 14. Article 49. doi
• Chapter Eichenberger C., Neun M., Martin H., Herruzo P., Spanring M., Lu Y., Choi S., Konyakhin V., Lukashina N., Shpilman A., Wiedemann N., Raubal M., Wang B., Vu H. L., Mohajerpoor R., Cai C., Kim I., Hermes L., Melnik A., Veliogl R., Vieth M., Schilling M., Bojesomo A., Marzouqi H. A., Liatsis P., Santokhi J., Hillier D., Yang Y., Sarwar J., Jordan A., Hewage E., Jonietz D., Tang F., Gruca A., Kopp M., Kreil D., Hochreiter S. Traffic4cast at NeurIPS 2021 - Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes, in: Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track. PMLR, 2022. P. 97-112.
• Chapter Ivanov D., Egorov V., Shpilman A. Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive Environments, in: AAMAS'2021: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems. IFAAMAS, 2021. P. 1536-1538.
• Chapter Laurent F., Schneider M., Scheller C., Watson J., Li J., Chen Z., Zheng Y., Chan S., Махнев К. И., Svidchenko O., Егоров В. С., Ivanov D., Shpilman A., Spirovska E., Tanevski O., Nikov A., Grunder R., Galevski D., Mitrovski J., Sartoretti G., Luo Z., Damani M., Bhattacharya N., Agarwal S., Egli A., Nygren E., Mohanty S. Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World, in: Proceedings of Machine Learning Research Vol. 133: Proceedings of the NeurIPS 2020: Competition and Demonstration Track. PMLR, 2021. P. 275-301.
• Chapter Svidchenko O., Shpilman A. Maximum Entropy Model-based Reinforcement Learning, in: NeurIPS'2021 Deep Reinforcement Learning Workshop. , 2021.
• Chapter Ivanov D., Пшихачев Г. А., Егоров В. С., Shpilman A. Self-Imitation Learning from Demonstrations, in: NeurIPS'2021 Deep Reinforcement Learning Workshop. , 2021.
• Chapter Зенкова Н. В., Sedykh E., Shugaeva T., V S., Ermak T., Shpilman A. Simple End-to-end Deep Learning Model for CDR-H3 Loop Structure Prediction, in: NeurIPS'2021 Machine Learning for Structural Biology Workshop. , 2021.
• Chapter V K., Lukashina N., Shpilman A. Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation, in: NeurIPS 2021 Traffic4cast Competition. , 2021.
• Preprint Vsevolod K., Lukashina N., Shpilman A. Solving Traffic4Cast Competition with U-Net and Temporal Domain Adaptation / Cornell University. Series Computer Science "arxiv.org". 2021.
• Chapter Sazanovich M., Nikolskaya A., Belousov Y., Shpilman A. Solving black-box optimization challenge via learning search space partition for local bayesian optimization, in: Proceedings of Machine Learning Research Vol. 133: Proceedings of the NeurIPS 2020: Competition and Demonstration Track. PMLR, 2021. P. 77-85.
• Chapter Shpilman A., Kidzinski L., Ong C., Mohanty S. P., Hicks J., Carroll S., Zhou B., Zeng H., Wang F., Lian R., Tian H., Jaskowski W., Garrett A., Lykkebo O. R., Toklu N. E., Shyam P., Srivastava R. K., Kolesnikov S., Hrinchuk O., Pechenko A., Mattias L., Wang Z., Hu X., Hu Z., Qiu M., Huang J., Sosin I., Svidchenko O., Malysheva A., Kudenko D., Rane L., Bhatt A., Wang Z., Qi P., Yu Z., Peng P., Yuan Q., Li W., Tian Y., Yang R., Ma P., Khadka S., Majumdar S., Dwiel Z., Liu Y., Tumer E., Watson J., Salathe M., Levine S., Delp S. Artificial Intelligence for Prosthetics: Challenge Solutions, in: The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations. Springer, 2020. doi P. 69-128. doi
• Chapter Kidziński Ł., Ong C., Mohanty S. P., Hicks J., Carroll S., Zhou B., Zeng H., Wang F., Lian R., Tian H., Jaśkowski W., Andersen G., Lykkebø O. R., Toklu N. E., Shyam P., Srivastava R. K., Kolesnikov S., Hrinchuk O., Pechenko A., Ljungström M., Wang Z., Hu X., Hu Z., Qiu M., Huang J., Shpilman A., Sosin I., Svidchenko O., Малышева А. И., Kudenko D., Rane L., Bhatt A., Wang Z., Qi P., Yu Z., Peng P., Yuan Q., Li W., Tian Y., Yang R., Pingchuan M., Khadka S., Majumdar S., Dwiel Z., Liu Y., Tumer E., Watson J., Salathé M., Levine S., Delp S. Artificial Intelligence for Prosthetics: Challenge Solutions, in: The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations. Springer, 2020. doi P. 69-128.
• Chapter Nikiforovskaya A., Kapralov N., Vlasova A., Shpynov O., Shpilman A. Automatic generation of reviews of scientific papers, in: 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA 2020). Miami : IEEE, 2020. P. 314-319. doi
• Chapter Mikita Sazanovich, Chaika K., Krinkin K., Shpilman A. Imitation Learning Approach for AI Driving Olympics Trained on Real-world and Simulation Data Simultaneously, in: Workshop on AI for Autonomous Driving (AIAD). , 2020.
• Chapter Lukashina N., Аленичева А. Р., Vlasova E., Kondiukov A., Khakimova A., Magerramov E., Churikov N., Shpilman A. Lipophilicity Prediction with Multitask Learning and Molecular Substructures Representation, in: Machine Learning for Molecules at NeurIPS'2020. , 2020.
• Chapter Bryksin T., Petukhov V., Alexin I., Prikhodko S., Shpilman A., Kovalenko V., Povarov N. Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler, in: MSR '20: Proceedings of the 17th International Conference on Mining Software Repositories. ACM Press, 2020. P. 455-465. doi
• Chapter Лобанов А. Н., Bryksin T., Shpilman Alexey. Automatic classification of error types in solutions to programming assignments at online learning platform, in: Artificial Intelligence in Education Vol. 11626. Springer, 2019. doi P. 174-178. doi
• Chapter Shpilman A., Malysheva A., Belyaev V. End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box, in: Proceedings of 2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY). IEEE, 2019. P. 165-170. doi
• Chapter Shpilman A., Malysheva A., Kudenko D. MAGNet: Multi-Agent Graph Network for Deep Multi-Agent Reinforcement Learning, in: Proceedings of 2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY). IEEE, 2019. P. 171-176. doi
• Chapter Shpilman A., Malysheva A., Kudenko D. MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning, in: Adaptive and Learning Agents Workshop at International Joint Conference on Autonomous Agents and Multiagent Systems. , 2019. P. 1-8.
• Chapter Омельченко А. В., Шпильман А. А., Москвин Д. Н., Храбров А. И. Особенности построения образовательных программ в области анализа данных в финансах // В кн.: Сборник научных трудов Санкт-Петербургской конференции исследователей в сфере экономики, бизнеса и общества: итоги 2019 года / Под общ. ред.: Е. М. Рогова, Ю. А. Тарасова, Е. А. Шакина. СПб. : Национальный исследовательский университет "Высшая школа экономики", 2019. С. 14-14.
• Chapter Shpilman A., Kudenko D., Gaydashenko A. A Comparative Evaluation of Machine Learning Methods for Robot Navigation Through Human Crowds, in: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. P. 553-557. doi
• Chapter Bryksin T., Shpilman A., Kudenko D. Automated Refactoring of Object-Oriented Code Using Clustering Ensembles, in: Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence. , 2018. P. 754-757.
• Chapter Bryksin T., Novozhilov E., Shpilman A. Automatic recommendation of move method refactorings using clustering ensembles, in: Proceedings of the 2nd International Workshop on Refactoring. ACM, 2018. doi P. 42-45. doi
• Chapter Shpilman A., Sosin I., Kudenko D. Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation, in: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2018. P. 1436-1441. doi
• Chapter Shpilman A., Malysheva A., Sung T. T., Sohn C., Kudenko D. Deep Multi-Agent Reinforcement Learning with Relevance Graphs, in: Deep RL Workshop NeurIPS 2018. , 2018. P. 1-10.
• Chapter Shpilman A., Malysheva A., Kudenko D. Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data, in: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2018. P. 286-291. doi
• Chapter Malysheva A., Shpilman A., Kudenko D. Learning to Run with Reward Shaping from Video Data, in: ALA 2018 - Workshop at the Federated AI Meeting 2018. ALA, 2018. P. 1-7.
• Chapter Shpilman A., Boikiy D., Polyakova M., Kudenko D., Burakov A., Nadezhdina E. Deep Learning of Cell Classification Using Microscope Images of Intracellular Microtubule Networks, in: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2017. doi P. 1-6. doi
• Article Usova E., Burakov A., Shpilman A.A. Disturbance of the radial system of interphase microtubules in the presence of excess serum in cell culture medium // Biofizika. 2010. Vol. 53. No. 6. P. 523-526. doi
• Article Alexey A. Shpilman, Nadezhdina E. S., Lomakin A. J., Chudinova E. M., Ivanov P. A. Microtubules govern stress granule mobility and dynamics // Biochimica et Biophysica Acta - Molecular Cell Research. 2010. Vol. 1803. No. 3. P. 361-371. doi
• Article Shpil'man A.A., Nadezhdina E. Stochastic computer model of the cell microtubule dynamics // Biofizika. 2006. Vol. 51. No. 5. P. 880-884. doi