1.
Schröder, K., Kastius, A., Schlosser, R.: Welcome to the Jungle: A Conceptual Comparison of Reinforcement Learning Algorithms. ICORES 2023, to appear (2023).
2.
Schlosser, R., Weisgut, M., Huebscher, L., Nordemann, O.: Robust Index Selection for Stochastic Dynamic Workloads. Springer Nature Computer Science. 4 (1), 1–14 (2023).
3.
Perscheid, M., Plattner, H., Ritter, D., Schlosser, R., Teusner, R.: Enterprise Platform and Integration Concepts Research at HPI. ACM SIGMOD Record. 51 (4), 68–73 (2023).
4.
Hagedorn, C., Huegle, J., Schlosser, R.: Understanding Unforeseen Production Downtimes in Manufacturing Processes using Log Data-driven Causal Reasoning. Journal of Intelligent Manufacturing. 33, 2027–2043 (2022).
5.
Kastius, A., Schlosser, R.: Dynamic Pricing under Competition using Reinforcement Learning. Journal of Revenue and Pricing Management. 21, 50–63 (2022).
6.
Chenavaz, R., Klibi, W., Schlosser, R.: Dynamic Pricing with Reference Price Effects in Integrated Online and Offline Retailing. International Journal of Production Research. 60, 5854–5875 (2022).
7.
Richly, K., Schlosser, R., Brauer, J.: Enabling Risk-averse Dispatch Processes for Transportation Network Companies by Probabilistic Location Prediction. Communications in Computer and Information Science, Springer. 1623, 21–42 (2022).
8.
Weisgut, M., Hübscher, L., Nordemann, O., Schlosser, R.: Solver-Based Approaches for Robust Multi-Index Selection Problems with Reconfiguration Costs under Stochastic Dynamic Workloads. 11th International Conference on Operations Research and Enterprise Systems (ICORES 2022). pp. 28–39 (2022).
9.
Kastius, A., Schlosser, R.: Towards Transfer Learning for Revenue and Pricing Management. Operations Research Proceedings, OR2021. pp. 361–366 (2022).
10.
Schlosser, R., Kastius, A.: Stochastic Dynamic Pricing under Duopoly Competition with Mutual Strategy Adjustments. Operations Research Proceedings (OR 2021). pp. 367–372 (2022).
11.
Kossmann, J., Kastius, A., Schlosser, R.: SWIRL: Selection of Workload-aware Indexes using Reinforcement Learning. 25th International Conference on Extending Database Technology (EDBT 2022). pp. 155–168 (2022).
12.
Schlosser, R., Westphal, J., Pörschke, M., Maltenberger, T., Kaminsky, Y.: Self-Adaptive Agents in a Dynamic Pricing Duopoly: Competition, Collusion, and Risk Considerations. Springer Nature Computer Science. 3 (3), 1–17 (2022).
13.
Perscheid, M., Plattner, H., Ritter, D., Schlosser, R., Teusner, R.: Das Fachgebiet “Enterprise Platform and Integration Concepts” am Hasso-Plattner-Institut. Datenbank-Spektrum. 22, 175–180 (2022).
14.
Figge, F., Dimitrov, S., Schlosser, R., Chenavaz, R.: Does the circular economy fuel the throwaway society? The role of opportunity costs for products that lose value over time. Journal of Cleaner Production. 368 (133207), (2022).
15.
Hagedorn, C., Lange, C., Huegle, J., Schlosser, R.: GPU Acceleration for Information-theoretic Constraint-based Causal Discovery. In: Le, T.D., Liu, L., Kıcıman, E., Triantafyllou, S., and Liu, H. (eds.) Proceedings of The KDD’22 Workshop on Causal Discovery, Proceedings of Machine Learning Research (PMLR) 185. pp. 30–60 (2022).
16.
Richly, K., Schlosser, R., Boissier, M.: Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications. Proceedings of the VLDB Endowment. pp. 4079–4092 (2022).
17.
Schlosser, R., Kastius, A.: A Conceptual Framework for Studying Self-Learning Agents in Recommerce Markets. Operations Research Proceedings (OR 2022), to appear (2022).
18.
Kastius, A., Schlosser, R.: Multi-Agent Dynamic Pricing Using Reinforcement Learning and Asymmetric Information. Operations Research Proceedings (OR2022), to appear (2022).
19.
Braun, T., Hurdelhey, B., Meier, D., Tsayun, P., Hagedorn, C., Huegle, J., Schlosser, R.: GPUCSL: GPU-Based Library for Causal Structure Learning. ICDM Open Project Forum. pp. 1236–1239 (2022).
20.
Schlosser, R.: Heuristic Mean Variance Optimization in Markov Decision Processes using State-Dependent Risk Aversion. IMA Journal of Management Mathematics. 33 (2), 181–199 (2022).
21.
Schlosser, R., Halfpap, S.: Robust and Memory-Efficient Database Fragment Allocation for Large and Uncertain Database Workloads. 24th International Conference on Extending Database Technology (EDBT 2021). pp. 367–372 (2021).
22.
Schlosser, R.: Scalable Relaxation Techniques to Solve Stochastic Dynamic Multi-Product Pricing Problems with Substitution Effects. Journal of Revenue and Pricing Management. 20 (1), 54–65 (2021).
23.
Richly, K., Schlosser, R., Brauer, J., Plattner, H.: A Probabilistic Location Prediction Approach to Optimize Dispatch Processes in the Ride-Hailing Industry. HICSS 2021. pp. 1830–1840 (2021).
24.
Kaminsky, Y., Maltenberger, T., Pörschke, M., Westphal, J., Schlosser, R.: Pricing Competition in a Duopoly with Self-Adapting Strategies. 10th International Conference on Operations Research and Enterprise Systems (ICORES 2021). pp. 60–71 (2021).
25.
Hagedorn, C., Huegle, J.: Constraint-Based Causal Structure Learning in Multi-GPU Environments. In: Seidl, T., Fromm, M., and Obermeier, S. (eds.) Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021. pp. 106–118. CEUR-WS.org (2021).
26.
Richly, K., Schlosser, R., Boissier, M.: Joint Index, Sorting, and Compression Optimization for Memory-Efficient Spatio-Temporal Data Management. 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021. pp. 1901–1906 (2021).
27.
Halfpap, S., Schlosser, R.: Memory-Efficient Database Fragment Allocation for Robust Load Balancing when Nodes Fail. 37th IEEE International Conference on Data Engineering, ICDE 2021, Chania, Greece, April 19-22, 2021. pp. 1811–1816 (2021).
28.
Schlosser, R., Chenavaz, R., Dimitrov, S.: Circular Economy: Joint Dynamic Pricing and Recycling Investments. International Journal of Production Economics. 108117, 1–13 (2021).
29.
Hagedorn, C., Huegle, J.: GPU-Accelerated Constraint-Based Causal Structure Learning for Discrete Data. Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). pp. 37–45 (2021).
30.
Huegle, J., Hagedorn, C., Perscheid, M., Plattner, H.: MPCSL - A Modular Pipeline for Causal Structure Learning. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. pp. 3068–3076. Association for Computing Machinery, New York, NY, USA (2021).
31.
Huegle, J.: An Information-Theoretic Approach on Causal Structure Learning for Heterogeneous Data Characteristics of Real-World Scenarios. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. pp. 4891–4892. International Joint Conferences on Artificial Intelligence Organization (2021).
32.
Huegle, J., Hagedorn, C., Boehme, L., Poerschke, M., Umland, J., Schlosser, R.: MANM-CS: Data Generation for Benchmarking Causal Structure Learning from Mixed Discrete-Continuous and Nonlinear Data. WHY-21 @ NeurIPS 2021 (2021).
33.
Schlosser, R.: Stochastic Dynamic Pricing with Waiting and Forward-Looking Consumers. Communications in Computer and Information Science (CCIS), Vol. 1162. pp. 47–69. Springer (2020).
34.
Richly, K., Brauer, J., Schlosser, R.: Predicting Location Probabilities of Drivers to Improve Dispatch Decisions of Transportation Network Companies Based on Trajectory Data. 9th International Conference on Operations Research and Enterprise Systems, ICORES 2020. pp. 47–58 (2020).
35.
Schmidt, C., Huegle, J., Horschig, S., Uflacker, M.: Out-of-Core GPU-Accelerated Causal Structure Learning. Algorithms and Architectures for Parallel Processing. ICA3PP 2019. pp. 89–104. Springer International Publishing (2020).
36.
Schlosser, R., Halfpap, S.: A Decomposition Approach for Risk-Averse Index Selection. 32nd International Conference on Scientific and Statistical Database Management (SSDBM 2020). pp. 16:1–16:4 (2020).
37.
Kossmann, J., Schlosser, R.: Self-driving database systems: a conceptual approach. Distributed and Parallel Databases. 38 (4), 795–817 (2020).
38.
Schlosser, R.: Risk-Sensitive Control of Markov Decision Processes: A Moment-Based Approach with Target Distributions. Computers and Operations Research. 123 (104997), 1–15 (2020).
39.
Kossmann, J., Halfpap, S., Jankrift, M., Schlosser, R.: Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms. Proceedings of the VLDB Endowment. pp. 2382–2395 (2020).
40.
Huegle, J., Hagedorn, C., Uflacker, M.: How Causal Structural Knowledge Adds Decision-Support in Monitoring of Automotive Body Shop Assembly Lines. In: Bessiere, C. (ed.) Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20. pp. 5246–5248. International Joint Conferences on Artificial Intelligence Organization (2020).
41.
Kossmann, J., Schlosser, R.: A Framework for Self-Managing Database Systems. 2019 IEEE 35th International Conference on Data Engineering Workshops (ICDEW). pp. 100–106 (2019).
42.
Schmidt, C., Huegle, J., Bode, P., Uflacker, M.: Load-Balanced Parallel Constraint-Based Causal Structure Learning on Multi-Core Systems for High-Dimensional Data. SIGKDD Workshop on Causal Discovery. pp. 59–77 (2019).
43.
Schlosser, R., Richly, K.: Dynamic Pricing under Competition with Data-Driven Price Anticipations and Endogenous Reference Price Effects. Journal of Revenue & Pricing Management. 18, 451–464 (2019).
44.
Schlosser, R., Kossmann, J., Boissier, M.: Efficient Scalable Multi-Attribute Index Selection Using Recursive Strategies. IEEE 35th International Conference on Data Engineering (ICDE 2019). pp. 1238–1249. IEEE (2019).
45.
Halfpap, S., Schlosser, R.: Workload-Driven Fragment Allocation for Partially Replicated Databases Using Linear Programming. IEEE 35th International Conference on Data Engineering (ICDE 2019). pp. 1746–1749 (2019).
46.
Schlosser, R.: Stochastic Dynamic Pricing with Strategic Customers and Reference Price Effects. 8th International Conference on Operations Research and Enterprise Systems, ICORES 2019. pp. 179–188 (2019).
47.
Schlosser, R., Walther, C., Boissier, M., Uflacker, M.: Automated Repricing and Ordering Strategies in Competitive Markets. AI Communications. 32, 15–29 (2019).
48.
Schlosser, R.: Data-Driven Stochastic Dynamic Pricing and Ordering. Operations Research Proceedings 2018. pp. 397–403 (2019).
49.
Schlosser, R., Richly, K.: Dynamic Pricing Competition with Unobservable Inventory Levels: A Hidden Markov Model Approach. Communications in Computer and Information Science. pp. 15–36. Springer (2019).
50.
Schlosser, R., Boissier, M.: Optimal Repricing Strategies in a Stochastic Infinite Horizon Duopoly. Communications in Computer and Information Science (CCIS). pp. 129–150. Springer (2018).
51.
Schlosser, R.: Stochastic Dynamic Multi-Product Pricing under Competition. Operations Research Proceedings 2017. pp. 527–533 (2018).
52.
Schlosser, R., Richly, K.: Dynamic Pricing Strategies in a Finite Horizon Duopoly with Partial Information. 7th International Conference on Operations Research and Enterprise Systems, ICORES 2018. pp. 21–30 (2018).
53.
Boissier, M., Schlosser, R., Uflacker, M.: Hybrid Data Layouts for Tiered HTAP Databases with Pareto-Optimal Data Placements. IEEE 34th International Conference on Data Engineering (ICDE 2018). pp. 209–220 (2018).
54.
Schlosser, R., Walther, C., Boissier, M., Uflacker, M.: Data-Driven Inventory Management and Dynamic Pricing Competition on Online Marketplaces. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI 2018). pp. 5856–5858 (2018).
55.
Schlosser, R., Boissier, M.: Dynamic Pricing under Competition on Online Marketplaces: A Data-Driven Approach. KDD ’18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 705–714 (2018).
56.
Schlosser, R., Boissier, M.: Dealing with the Dimensionality Curse in Dynamic Pricing Competition: Using Frequent Repricing to Compensate Imperfect Market Anticipations. Computers and Operations Research. 100, 26–42 (2018).
57.
Schmidt, C., Huegle, J., Uflacker, M.: Order-independent constraint-based causal structure learning for gaussian distribution models using GPUs. SSDBM ’18 Proceedings of the 30th International Conference on Scientific and Statistical Database Management. pp. 19:1–19:10. ACM, New York, NY, USA (2018).
58.
Schlosser, R.: Stochastic Dynamic Pricing and Advertising in Isoelastic Oligopoly Models. European Journal of Operational Research. 259, 1144–1155 (2017).
59.
Schlosser, R., Boissier, M.: Optimal Price Reaction Strategies in the Presence of Active and Passive Competitors. Proceedings of the 6th International Conference on Operations Research and Enterprise Systems (ICORES), Porto, Portugal. pp. 47–56 (2017).
60.
Uflacker, M., Schlosser, R., Meinel, C.: Ertragsmanagement im Wandel - Potentiale der In-Memory Technologie. In: Gläß, R. and Leukert, B. (eds.) Handel 4.0: Die Digitalisierung des Handels. Strategien, Technologien, Transformation. pp. 177–190. Springer Gabler (2017).
61.
Boissier, M., Schlosser, R., Podlesny, N., Serth, S., Bornstein, M., Latt, J., Lindemann, J., Selke, J., Uflacker, M.: Data-Driven Repricing Strategies in Competitive Markets: An Interactive Simulation Platform. Proceedings of the Eleventh ACM Conference on Recommender Systems (RecSys ’17). pp. 355–357. ACM, New York, NY, USA (2017).
62.
Serth, S., Podlesny, N., Bornstein, M., Lindemann, J., Latt, J., Selke, J., Schlosser, R., Boissier, M., Uflacker, M.: An Interactive Platform to Simulate Dynamic Pricing Competition on Online Marketplaces. 21st IEEE International Enterprise Distributed Object Computing Conference, EDOC 2017, Quebec City, QC, Canada, October 10-13, 2017. pp. 61–66. IEEE (2017).
63.
Seiffert, M., Holstein, F., Schlosser, R., Schiller, J.: Next Generation Cooperate Wearables: Generalized Activity Assessment Computed Fully Distributed Within a Wireless Body Area Network. IEEE Access Journal. 5, 16793–16807 (2017).
64.
Schlosser, R.: Stochastic Dynamic Multi-Product Pricing with Dynamic Advertising and Adoption Effects. Journal of Revenue and Pricing Management. 15, 153–169 (2016).
65.
Schlosser, R., Boissier, M., Schober, A., Uflacker, M.: How To Survive Dynamic Pricing Competition in E-commerce. Proceedings of the Poster Track of the 10th ACM Conference on Recommender Systems (RecSys 2016), Boston, USA, September 17, 2016 (2016).
66.
Schlosser, R.: Joint Stochastic Dynamic Pricing and Advertising with Time-Dependent Demand. Journal of Economic Dynamics and Control. 73, 439–452 (2016).
67.
Schlosser, R.: Dynamic Pricing with Time-Dependent Elasticities. Journal of Revenue and Pricing Management. 14, 365–383 (2015).
68.
Schlosser, R.: A Stochastic Dynamic Pricing and Advertising Model under Risk Aversion. Journal of Revenue and Pricing Management. 14, 451–468 (2015).
69.
Schlosser, R.: Dynamic Pricing and Advertising Models with Inventory Holding Costs. Journal of Economic Dynamics and Control. 57, 163–181 (2015).
70.
Helmes, K., Schlosser, R.: Dynamic Advertising and Pricing with Constant Demand Elasticities. Journal of Economic Dynamics and Control. 37, 2814–2832 (2013).